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SPECIAL ISSUE

Genome TechnologyGT Celebrates the Rising Young Stars of Science30 promising researchers – recommended by today’s established PIs – are profiled in this exclusive year-end issue. We highlight their work in these categories and more:

SequencingSynthetic biologyRNAiGene expressionComputational biologyStructural variationProteomicsTranslational research

Log on now at www.genome-technology.comExplore all of our exclusive new resources for molecular biologists:

The Daily Scan: Our editors’ daily picks of what’s worth reading on the Web.

The Forum: Where scientists can inquire and comment on research, tools, and other topics.

Podcasts: Hear the full comments – in their own voices – of researchers interviewed in the Genome Technology Magazine.

Polls: Make your opinion known on topics from the sublime to the ridiculous.

Archive: Magazine subscribers can read every word of every article we have ever published.

Recent results from GT PollsWould you speak at a conference that didn’t pay your registration fee?

Nope, I’d boycott on principle. They should at least let you into the conference. 66%

I’d grumble about it, but ultimately accept. It’s worth the extra line on my CV. 16%

Sure, why not? 18%

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NEXT-GEN SEQUENCE ANALYSIS AND AGBIO TECH GUIDES

Tomorrow’s PIs

THIR

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UAL

Genome Technology’s special year-end issue profiles rising young investigators


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DECEMBER 2008/JANUARY 2009 Contents

D E C E M B E R 2 0 0 8 /J A N U A R Y 2 0 0 9 G E N O M E T E C H N O L O G Y 3

“In the future, experimental and computationalbiology are only going to become more intertwined and even inseparable. What I hope to see is a culture that ... [understands] if it isn’t transparent and reproducible, it isn’t science.”

James Taylor, page 29

Tomorrow’s PIsSEQUENCING9 JAY SHENDURE Functional testing with next-gen technology

10 LI DING The genomic basis of cancer

11 ROBERT RIEHN Stretching and sequencing DNA

SYNTHETIC BIOLOGY12 TIM LU Letting the bedside inform the bench

STRUCTURAL BIOLOGY15 DAVID MATHEWS Looking for secondary structure

16 BOJAN ZAGROVIC Finding order in disorder

17 OLIVER RANDO Opening the ‘black box’ of chromatin

RNA INTERFERENCE19 JULIUS BRENNECKE Parsing nature’s elegant solutions

20 KEVIN MORRISAnother mode of gene silencing

STRUCTURAL VARIATION21 GREG COOPER A compelling look at variation

GENE EXPRESSION23 ADRIANA BRISCOE It’s all in the eyes

24 UWE OHLER Imaging gene regulation

25 MARIAN WALHOUT A fresh look at differential expression

COMPUTATIONAL BIOLOGY27 PAUL FLICEK In the informatics trenches

28 CARL KINGSFORD A computer scientist takes on transcription

29 JAMES TAYLOR Researching to help researchers

30 WOLFGANG HUBER Pushing statistics to the limit

PROTEOMICS31 XUDONG YAO From signal transduction to cystic fibrosis

32 GISELLE KNUDSEN From detection to drugs

33 WEI-JUN QIAN Step by step, a better mass spec

POPULATION GENETICS34 SHAMIL SUNYAEV A better approach to interpretation

35 RAUL RABADAN Elucidating evolution, virus by virus

COMPARATIVE GENOMICS37 RACHAEL THOMAS Cancer in dogs, cats, and people

TRANSLATIONALRESEARCH39 CAREY LUMENG Investigating the perils of obesity

40 BRIDGET WAGNER Large-scale screens to study diabetes

41 CHARLES SCHROEDER ‘Cells in, disease out’

42 GAD GETZ The physicist who tackled cancer

REGULATORY ELEMENTS45 LEN PENNACCHIO Finding function in dark matter

46 ZHAOLEI ZHANG Where collaborations are king

47 LAURA ELNITSKIThe functional perspective

Resources5 PRIMER Starting out, already stars

7 DEMOGRAPHICSIn numbers

48 CAREERS Steps to start your career

51 FUNDINGGrant opportunities

53 LOOKING AHEAD Next-gen tomorrow’s PIs

54 CLASS OF 2007A year later

57 RECOMMENDERSThanks to today’s PIs

58 BLUNT END


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FROM THE EDITOR Primer


Starting Out, Already Stars

Back when we came up with the idea for Tomorrow’s PIs — a special issue designed to introduce our readers to a new generation of promising scientists still early in their careers — we really had no clue how the whole thing would pan out. Now, as we’re getting ready to send the

third annual Tomorrow’s PIs edition to the printer, we can’t imagine what we’d do without it. People we’ve profiled in the past have gone on to do great things in science, and no doubt that will continue.

This magazine began in the summer, when we started consulting experts in the systems biology community to get recommenda-tions for scientists who will soon be taking the world by storm. We looked for nominees who are no more than five years or so past their postdoc and then selected a group with a diverse range of scientific interests, backgrounds, and affiliations. After many, many inter-views (and more planning meetings than the Genome Technology staff would care to remember), we emerged with the final 30 people who are profiled in this issue. As always, we had far more recommendations than we could actually print, so we’ll have mini-profiles of many more young investigators throughout the year at www.genome-technology.com.

We’re so grateful to the outstanding cast of scientists who contacted us with nominations — without them, this issue would not be possible.

One thing we noticed this year was a particular challenge in as-signing these up-and-coming scientists to technology categories. The research they’re engaged in is becoming more integrative, bringing together lots of platforms and concepts from different disciplines. You’ll also see that next-gen sequencing is really shaking things up; many of the scientists say that advances in that technology have thoroughly changed the kinds of experiments they’re able to do.

The advertisers in this issue have made it possible for us to give a travel stipend to each of the scientists profiled here, so we also thank them very much for their generosity and support of these early-career researchers.

Meredith W. Salisbury, Editor

What do you think of Genome Technology? Let me know how we’re doing by e-mailing me at [email protected] or by calling me at +1.212.651.5635

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In NumbersOur bunch of young investigators is an accomplished group. Here is a smattering of what they have accomplished and where.

Demographics

26Number of Nature papers

13Number of Science papers

2HHMI pre-doctoral fellows

6 Investigators with NHGRI funding

4MD/PhDs

8Number of PhDs from non-US institutions

10Number who say that next-gen sequencing has changed their work

Stanford UniversityMost common PhD institute

11 yearsLongest time since PhD:Shamil Sunyaev and Marian Walhout, 1997

This year Shortest time since PhD: Tim Lu

BostonMost common current metropolitan area

Massachusetts, Washington, MarylandMost popular locations for recommenders

4Recommenders are current HHMI Investigators: James Collins, Evan Eichler, Stuart Schreiber, Phillip Zamore

1Recommenders who won the Lasker Prize this year: Victor Ambros


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Functional Testing with Next-Gen Technology

Many in the sequenc-ing world may already be famil-iar with Jay Shen-

dure, the young investigator who, along with his colleagues in George Church’s lab, helped to pioneer polony sequencing, a highly paral-lel, low-cost sequencing method. Now, Shendure’s lab continues to advance technical elements related to sequencing — specifically in areas of experimental methods and computational tool development, in-cluding platforms for array-based, programmable DNA synthesis and massively parallelized, short-read DNA sequencing.

Shendure has also been instrumen-tal in developing a method to selec-tively capture all the protein-coding sequences with a microarray as a sample prep approach to next-gen sequencing. His lab continues to work on aqueous-phase and solid-phase protocols to capture genomic subsets.

The Shendure lab is also check-ing out experimental methods that would enable de novo genome sequencing using next-gen technol-ogy. “A growing interest [is] in syn-thetic aspects — more specifically, how you use off-array synthesis of oligos in other ways, such as trying to create long synthetic constructs that can be used for various things,” he says. “The particular thing we’re interested in is developing very high-throughput means of doing func-tional testing of variants.”

While there is currently a lack of quantitative models for defining cis-regulatory elements, Shendure and his team are developing a platform for high-throughput screening and high-resolution functional analysis of

cis-regulatory elements. So far, they have been able to demonstrate this approach on well-characterized bac-teriophage promoters, he says.

It probably goes without saying that Shendure’s time in George Church’s lab left an indelible mark on his ap-proach to science. “I think it’s one of these rare places where there are all kinds of those people around — biologists, physicist, engineers, all in the same working group — and also just the tremendous amount of creative freedom and flexibility very early in your career to do whatever experiment you want to do,” he says. “George never told me I couldn’t do an experiment, no matter how outlandish it was. It was always my choice, and I think having that sort of freedom very early on was a good thing.”

Looking ahead Peering into his crystal ball, Shen-

dure predicts that five to 10 years down the line, he and everyone else involved in life sciences research will be able to sequence virtually any-thing in a cost-effective and feasible way. Shendure says the ability to accurately predict function from a se-quence in a robust and accurate way would help speed things up as well. “This is something that’s extremely difficult, but that certainly would be nice to have,” he says. “It would be great if we’re building things, if we’re synthesizing things, to be able to predict what they’ll do, but also, looking at variation in the human genome, to know what that does as well.”

Publications of note

In the paper “Accurate multiplex polony sequencing of a bacterial ge-nome” published in Science in 2005, Shendure, Church, and their col-leagues outline their use of off-the-shelf instrumentation and reagents to perform polony sequencing. The authors describe resequencing an evolved strain of E. coli at less than one error per million consensus bases using a method to convert an epifluorescence microscope into a nonelectrophoretic automated DNA sequencing platform.

And the Nobel goes to...

Shendure says that if he were to be awarded the Nobel prize, he would like it be for something contributing to world peace, rather than any sort of technical innovation.

— Matthew Dublin

Jay Shendure SEQUENCING

TITLE: Assistant Professor of Genome Sciences, University of WashingtonEDUCATION: PhD, Harvard University, 2005; MD, Harvard Medical School, 2007RECOMMENDED BY:

Alan Guttmacher, Mary-Claire King


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10 W W W. G E N O M E -T E C H N O L O G Y. C O M D E C E M B E R 2 0 0 8 /J A N U A R Y 2 0 0 9

The Genomic Basis of Cancer

Cancer is a disease of genes and of pathways, says Li Ding, a researcher at the genome center at Wash-

ington University in St. Louis. As head of the medical genomics group, Ding works to uncover the genomic changes that lead up to and are as-sociated with human cancer. In par-ticular, she is part of both the Tumor Sequencing Project and the Cancer Genome Atlas efforts. “My research mostly focuses on identifying the genomic alteration associated with human cancer,” Ding says.

The TSP is a multicenter collabora-tion that has a goal of mapping the genomic changes that occur in lung adenocarcinoma, the most common form of lung cancer. The average five-year survival rate for patients with this form of cancer is 15 percent. “The low survival rate is largely due to late-stage detection, so that’s why we are interested in studies of this cancer type because of its high over-all incidence, in the US and world-wide,” Ding says.

The Cancer Genome Atlas proj-ect is studying a variety of can-cer types: glioblastoma multiforme, ovarian cancer, and squamous cell lung cancer. Some of the findings from the glioblastoma arm of the project were published online in the September 4th edition of Nature. In that study, Ding and her colleagues found three genes — NF1, ERBB2, and PIK3R1 — that are significantly mutated in these tumors that had not previously been associated with the disease. “We’re extremely excited about this finding of mutations in

the gene called the PIK3R1 because this is the first time that PIK3R1 is identified as a major cancer gene in GBM,” she says. Furthermore, she adds, the gene mutations they detected were clustered together in a coding domain that is responsible for interacting with the catalytic domain.

Along the way, Ding has been encouraged by her bosses, Rick Wil-son and Elaine Mardis, to follow what interests her. “[Wilson] would always say, ‘Go for it! If you think it’s interesting, you should dig deeper.’ He has always been very supportive,” Ding says. For example, as part of the GBM study, Ding saw an interesting feature of the PIK3R1 mutation; Wil-

son and Mardis suggested looking at more samples even more deeply and going back to older data to figure it out. “They will support you 100 per-cent,” she says.

Looking ahead

Ding says that next-generation sequencing technology will be a boon for determining how cancer arises. These new methods will allow researchers like Ding to screen more genes, more samples, and more types of cancer. “It will allow us [to see] the whole transcriptome, the entire exons of all genes, and the whole genome. It will allow us to look at a large number of tumors for each tumor type,” she says. “Eventually [it] will allow us to look at every type of major cancers found in humans and, hopefully, with next-generation sequencing, we can also look into metastasis.”


Besides Ding’s work with the Cancer Genome Atlas project, she has also published a number of papers dealing with her work on lung adenocarcinomas. In a Cancer Research paper that came out this past July, Ding and her colleagues identified a novel MEK1 mutation in the EGFR signaling pathway of lung adenocarcinomas.

And the Nobel goes to …

Ding’s end goal is to improve patient care. “If we can develop something that can help treatment of cancer, any type of human cancer, I think that would be my dream,” she says.

—Ciara Curtin

Li Ding SEQUENCING

TITLE: Research Instructor, Washington UniversityEDUCATION: PhD, University of Utah, 1998RECOMMENDED BY: Rick Wilson

“This is the first time that PIK3R1 is identified as a major cancer gene in GBM.”


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D E C E M B E R 2 0 0 8 /J A N U A R Y 2 0 0 9 G E N O M E T E C H N O L O G Y 1 1

Stretching and Sequencing DNA

Many approaches to DNA sequencing rely on fluorescent tags to signal the

different bases contained in a given stretch of DNA. Instead of using tags, North Carolina State’s Robert Riehn thinks that the electrical properties of DNA can be harnessed to deter-mine its sequence — and he’s putting his background in nanotechnology to use to find sequence-specific electric signals from DNA. “What I’m doing is very basic: trying to show there is a signal and trying to show that we can make this signal sequence-specific,” Riehn says.

In his microfluidic device, Riehn uses nanofluidic channels with cross-sections smaller than 50 nanometers and a few hundred microns long. Strands of DNA are then stretched out in these channels and placed between two electrodes. “The DNA is directly between two opposing electrodes, and the two opposing electrodes try to attract the DNA,” Riehn says. His team members are currently working on measuring how current flows sideways through DNA. They hope that if they choose the conditions properly, they will be able to detect a sequence-depen-dent signal. “Chemically, the bases have different chemical structure,” Riehn says. “What we hope is that by choosing the conditions of the electrical field correctly, or of the energies of the electrons correctly, that we can gain some insight into

the electrical energy structure of the molecular orbitals inside the mol-ecule. And then, hopefully, if we choose correctly, the different bases have different signatures.”

The challenges, he adds, are get-ting that sequence-dependent signal and knowing how fast the strands of DNA are flowing through the nano-channels. Though all the bases are different, they are still fairly similar, hanging off the same sugar phos-phate backbones; Riehn says that only about a third of the base ac-counts for the difference. Then, you have to know how fast the DNA moves through the channel because if you don’t know that, “you still don’t know what the sequence is,” he says.

Riehn’s interest in this area was piqued by working in Robert Austin’s lab at Princeton University. Riehn says that Austin’s rigorous way of asking questions has shaped how he does his own research. “His ap-proach definitely is not one of incre-mental research. He has more of an approach of trying to do something big — try to do something big and you may fail and you may not fail,” Riehn says. “I like that.”

Looking aheadIn the next five years, Riehn says,

the goal of sequencing a human genome for $1,000 will most likely be met. However, he thinks that sequencing will still be performed in large diagnostic laboratories. Fur-ther down the line, in about 10 years, he thinks that doctors’ offices may have sequencers that could pro-vide low or medium-quality read-outs to be used as a screening tool, say for a microbial or viral infection. “We wouldn’t necessarily be seeing the resequencing everything to true fidelity, but simply a screening for a large number of common questions,” Riehn says.


In a PNAS paper from 2005 called “Restriction mapping in nanofluidic devices,” Riehn and his coauthors restriction-mapped DNA stretched in their nanochannel. Riehn says that restriction mapping is one of the more basic tools in the field, but the effort showed “that we can really do biology on our microfluidic devices, that they are not just applicable to a very narrow range of applications, that we can do real biology on the stretch-out DNA inside the nano-channels.”


“A very large-scale issue would be the eradication of transmittable dis-eases,” Riehn says. “That would be one thing that I would do if I could, really — diagnose every person in-stantly for exactly which pathogen they are carrying.”

— Ciara Curtin

Robert Riehn SEQUENCING

TITLE: Assistant Professor, North Carolina State UniversityEDUCATION: PhD, University of Cambridge, 2003RECOMMENDED BY: Robert Austin, Alan Guttmacher

“What I’m doing is very basic: trying to show there is a signal and trying to show that we can make this signal sequence-specific,” Riehn says of his microfluidic device.


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Letting the Bedside Inform the Bench

T im Lu is in the relatively rare position of having first-hand experience at the bedside and bring-

ing his own research efforts to bear on real-life problems in the clinic. As a researcher also pursuing his MD, Lu uses synthetic biology to combat bacterial infections, with a specific focus on engineered bacteriophages.

The initial inspiration for this came to him while fulfilling his med school duties: examining dialysis patients with indwelling catheters, many of whom suffer from severe bacterial infections caused by biofilms. “There are a lot of biofilms in all sorts of medical devices, such as indwelling catheters. You can get a bacteria that sticks to the catheter surfaces that are really hard to eradicate with any conventional antibiotics,” Lu says. “So you have to rip out the catheter and insert a new one, which causes morbidity in a lot of patients.”

Instead of inventing a nuclear-powered can of Lysol, Lu saw an op-portunity to use his synthetic biology skills to engineer a bacteriophage capable of destroying the biofilm by penetrating the extracellular wall and attacking the bacteria inside. Last year, Lu and Boston University’s Jim Collins demonstrated that this was possible using an engineered E. coli-specific phage expressing dispersin B, an enzyme capable of break-ing down several different types of biofilms. According to their initial experiments using E. coli biofilms on a plastic surface, the engineered bacteriophage was capable of killing 99.997 percent of the biofilm cells.

But the US Food and Drug Administration has not yet approved bacteriophages for use in humans, although the medical community has

been aware of their potential for some time. “The biggest hurdle for us going forward is acceptance of the medical community and the FDA. We’re us-ing engineered bacteriophage, which are virus, even though they only affect bacteria,” he says. “People are becoming increasingly interested in using bacteriophage, so I don’t think this is going to be a problem in the next 10 or 20 years — but there’s obviously a lot of work that needs to be done to prove to [the] medical community that this is something that’s viable.”

In addition to fighting biofilms, engineered bacteriophages can also be used to fight antibiotic-resistant bacteria, another issue with which Lu is familiar from his experience as a physician-in-training. Lu says that by inserting bacteriophages that

knock out networks of bacteria re-sponsible for the resistance mutation in combination with prescribing cur-rently available antibiotics, he and Collins have demonstrated that it is possible to reduce the mutation rate 1,000-fold and kill bacteria 30,000 times more effectively than with just the antibiotic alone.

Looking ahead

The long-term vision for the bac-teriophage that can eliminate anti-biotic-resistant bacteria is to use it first against the most serious types of infections such as MRSA, which can’t easily be eradicated. At pres-ent, Lu and Collins are using mouse trials and hope to eventually work with the Centers for Disease Control and Prevention to start gaining more clinical acceptance. In the future, Lu says, they would like to engineer their bacteriophage to work around a particular resistance to offer a more rational approach to fighting bugs that are constantly evolving.


In a 2007 paper published in PNASentitled “Dispersing biofilms with en-gineered enzymatic bacteriophage,” Lu and Collins showed that engineered biofilm-destroying bacteriophage were several orders of magnitude better than wild-type bacteriophage at eliminating biofilms.


Lu says he would like to receive the Nobel for creating a long-term, sustainable solution to treating anti-biotic-resistant bacteria.

— Matthew Dublin

Tim Lu SYNTHETIC BIOLOGY

TITLE: Research Assistant, MITEDUCATION: PhD, Harvard-MIT Division of Health Sciences and Technology, 2008; currently pursuing MD at Harvard Medical SchoolRECOMMENDED BY: James Collins


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Thanks For Your SupportGenome Technology extends its sincere thanks to the advertisers in this issue, whose sponsorship helped proved a travel stipend honorarium for each of Tomorrow’s PIs


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Looking for Secondary Structure

T he most widely known secondary structure of RNA is the cloverleaf-shaped configuration of

transfer RNA. The three loops of the cloverleaf have specific roles during translation at the ribosome. There are, however, other RNA arrange-ments that occur, and their functions are not always as clear. The Univer-sity of Rochester’s David Mathews is building algorithms to predict sec-ondary structures of RNA and apply-ing that knowledge to finding novel, noncoding RNA in the genome and uncovering what those RNAs do. In addition, he says that knowing more about secondary RNA structure might help researchers design better and more effective siRNAs. “We’re best known for our work in predict-ing secondary structure,” Mathews says of his lab.

Mathews’ group wants to be able to predict, from any given sequence, if there will be an RNA structure and, if so, what base pairs will form. As a bioinformatics and computational bi-ology group, the team has developed an algorithm to detect the RNA struc-ture common to two homologous sequences. “We’d like to be able to, given a set of homologous sequences, be able to determine the secondary structure of those sequences with 100 percent accuracy. That would improve our ability to determine three-dimensional structures and it would improve our ability to find noncoding RNAs,” Mathews says.

Currently, the accuracy with which Mathews and others working in the area of predicting secondary struc-

ture ranges. With a single sequence, he says, the predictions are about 70 percent accurate; with multiple sequences, that figure can reach 90 percent. He says people working on this problem use different approaches, which has resulted in variation in accuracy. “But I’d say none is perfect,” he says.

Studying RNA structure leads to questions plaguing genomics, Mathews says —in particular to the attention paid to noncoding RNAs. “They may, for example, explain why higher organisms are more compli-cated than lower ones even though we don’t have dramatically more

protein-coding genes. We may have many more noncoding RNAs that are functioning at the level of RNA, so there’s a lot of interest to be able to find these genome sequences,” he says. Mathews’ group has also de-veloped an algorithm that searches genomes for noncoding RNAs.

Another area that RNA secondary structure knowledge will help is in designing effective siRNAs, Mathews says. Providing complementary short duplex RNAs may silence a message, but it isn’t always effective. “We’ve looked at that as a problem of equi-librium binding and so approaching it that way we have an algorithm that can design effective siRNAs for a given mRNA target,” he says.

Looking ahead

Mathews says the goals for the next five years are to find all the noncod-ing RNAs and to move toward better prediction of three-dimensional RNA structures. “If we are able to find more in noncoding RNAs, then we have more information to use to de-termine three-dimensional structures — and if we’re better at predicting three-dimensional structures, we’re going to be able to more effectively find noncoding RNAs with better sensitivity and specificity,” he says.


In 2006, Mathews and his team published a paper in BMC Bioinfor-matics. “That’s where we apply our algorithm for finding common sec-ondary structures and use that to find regions in genome alignments that are likely to contain structured RNA,” Mathews says.

— Ciara Curtin

David Mathews STRUCTURAL BIOLOGY

TITLE: Assistant Professor, University of RochesterEDUCATION: PhD, University of Rochester, 2002; MD, University of Rochester, 2003RECOMMENDED BY:

Alan Guttmacher, Doug Turner

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Finding Order in Disorder

As a grad student who helped Stanford’s Vijay Pande develop the Fold-ing@Home project, Bo-

jan Zagrovic knows what the current protein modeling tools can and can-not do. And while a lot of progress has been made in the field of com-putational proteomics, there’s still much to be figured out.

As group leader of the computa-tional biophysics lab at the Mediter-ranean Institute for Life Sciences in Split, Croatia, Zagrovic uses molecu-lar simulations to model the folding and binding dynamics of mainly protein-protein interactions, but also lipids and DNA. “It’s 99 percent theo-retical,” Zagrovic says. After leav-ing Stanford in 2004, Zagrovic has continued to use and develop the Folding@Home distributed comput-ing cluster centered at Pande’s lab in order to look at “how dynamics and structure are connected, and related to function.”

One of his lab’s main areas of in-terest outside protein folding is the unstructured nature of proteins. Natively unfolded proteins, or “in-trinsically unstructured proteins,” he says, are common and present a new frontier for computational sim-ulation. “It turns out that almost 30 percent of eukaryotic proteins actu-ally have significant regions [where] there’s simply no structure. They do not conform to the 3D structure-equals-function paradigm,” he says. “The problem is the standard tech-niques of structural biology are not capable of describing these mol-ecules.”

The limiting factor in his work, of course, is computational power, and in the coming years he hopes to have “better algorithms, faster computers,

[and] more sampling.” As it stands, processors are capable of render-ing only a small fraction of what he needs to see in order to understand many biological processes. “There’s two [or] three orders of magnitude — if not more — difference between what we want [to see] and what we can,” he says.

Looking ahead

Zagrovic will continue studying what he thinks could become a new paradigm for understanding unfold-ed protein structure. In the next five years, he hopes to apply computa-tional modeling to solving problems associated with the “entropic com-ponent to protein activation,” which could have a clinical impact when it comes to finding ideal drug target

binding sites. “If you tap into these allosteric effects, could you actually affect the function of an enzyme by creating something that would bind somewhere else,” he says, “not neces-sarily in the active site, but still have effect in the active site?”


In 2005, Zagrovic published a paper in PNAS that explored the conforma-tion of unfolded polypeptides. In the study, he combined wet lab work and molecular modeling using the Fold-ing@Home cluster and found that the structure of unfolded proteins does reside in the previously de-scribed configuration, a polyproline type II helix, “but that it is much, much more compact than previously thought,” Zagrovic says. “In other words, what we have shown is that locally the chain is PPII, but when it comes to its long-range structure it is still a compact random coil.” It was one of the largest simulations to date — the team employed seven different commonly used models, or force fields, to simulate the polypep-tide configurations.


If Zagrovic were to win the Nobel prize, he would hope to get it “for proving that proteins and biomol-ecules, in general, are more much flexible than we think,” he says. “The way we picture things right now is quite ordered still; our logic is still very much mechanistic. But I have this feeling that things are much more fuzzy, things are much more fluctuant, things are much more crazy than we think.”

— Jeanene Swanson

Bojan Zagrovic STRUCTURAL BIOLOGY

TITLE: Group Leader in ComputationalBiophysics, Mediterranean Institute for Life SciencesEDUCATION: PhD, Stanford University, 2004RECOMMENDED BY: Vijay Pande


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Opening the ‘Black Box’ of Chromatin

After years of studying transcription in other people’s labs, Oliver Rando, now at the Uni-

versity of Massachusetts Medical School, is finally leading a lab of his own focusing on the genome-wide structure and function of a key regu-lator, chromatin.

Rando studies everything from the sequence of repeating histones to nucleosome positioning, covalent modifications, and the histone vari-ants themselves. “We’re interested in how DNA is packaged in the cell — in other words, what chromatin structure looks like,” he says. “We’re interested in that both independently as well as a potential carrier for epigenetic information.” He runs a broad array of tools, spanning home-made and commercial microarrays to deep sequencing, to make sense of this information.

Over the past several years, Rando has focused on mapping chroma-tin structure in yeast, and how that changes as a cell proceeds through its cycle. “All of these basic mechanistic questions about how nucleosomes move and interact with each other after replication have huge conse-quences for how and whether you can inherit chromatin states,” he says. He notes that while a majority of people believe chromatin states to be heritable, he contends that there’s no hard proof yet. “Whether chroma-tin states are heritable is, to my mind, still an open question.”

Since his undergrad days, Rando has always worked in transcription labs, and it was from these early experiences that he noticed how little people knew about what was regulating the process of transcrip-tion. “It became very clear over my

early years of training that chromatin was a fairly mysterious black box and often people in the transcription field would invoke chromatin when they didn’t understand something,” he says. While at Jerry Crabtree’s Stanford lab, Rando did his PhD on the chromatin remodeling complex, which converts the energy released during ATP hydrolysis to remodel nucleosome structure. “We realized that there was no way to find lots of nucleosomes at once, and that’s what led me to start working on how to do these mapping things on a genomic scale.”

Looking ahead

A big technical challenge is describ-ing the next level of chromatin struc-

ture, Rando says. “Characterization of beads on a string is, at this point, mature. We can find nucleosomes. The folding of beads on a string — the next level of compaction — is called 30-nanometer fiber, and at this point, no one has any hint for how to think about mapping at 30-nanometer fiber. But I think it will be tremendously illuminating to do so,” he says.

In the immediate future, Rando thinks the community will move toward mapping chromatin states. “How do nucleosomes move when you turn on a gene, or, in our case, during genomic replication?”


A lingering question in Rando’s mind is why there are so many his-tone modifications. In a paper pub-lished in PLoS Biology in 2005, he and his team used a high-resolution tiling array to look at the occur-rence of combinations of 12 histone modifications across thousands of nucleosomes in growing S. cerevisiae.They found that two groups of co-occurring modifications could dis-tinguish nucleosomes at one location from another. Rando expects this to become more of a focus in the com-ing years.


Asked what he’d like to win the Nobel for, Rando says his eye isn’t on the prize. “I distrust people whose goal in science is to win prizes.” When pressed, he says, “I suppose it would be for discovering an epi-genetic therapy that cured a huge number of cancers.”

— Jeanene Swanson

Oliver Rando STRUCTURAL BIOLOGY

TITLE: Assistant Professor, Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical SchoolEDUCATION: MD/PhD, Stanford University, 2002RECOMMENDED BY: Phillip Zamore


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Parsing Nature’s Elegant Solutions

Julius Brennecke had big plans to pursue a career in medical research, but stints as a field assistant

in the Galapagos Islands and in the Serengeti convinced him to change course. “The adventures that I had [there] just reinforced my attachment to nature and to [understanding] living processes,” he says. “I realized that what I’d like to do most was to combine that passion and interest with molecular biology to basically come to an understanding of how processes function on the molecu-lar level.” What has always amazed him most about biological processes, Brennecke says, is that when he con-siders how he might have solved a particular problem, the way that nature actually solved it is “always so much more elegant and so much more functional.”

He was working on his PhD at EMBL in Heidelberg when serendip-ity struck. Brennecke was tasked with making sense of a Drosophilamutation that had proven unwilling to give up its secret. He happened to be on journal club duty when the first papers came out describing microRNAs — Brennecke followed the inspiration and, sure enough, the mutation turned out to be a member of this class of small RNAs. “We went on to explore the microRNA targeting [mechanism],” he says; the team merged computational and experimental data to demonstrate how the miRNA pairing process worked.

Brennecke is just wrapping up a postdoc with Greg Hannon at Cold Spring Harbor Laboratory, in which he took advantage of deep sequenc-ing technologies to study piwi-inter-acting RNAs. The team found that

piRNAs serve as “a sort of immune system on the RNA level,” he says — they appear to silence transposons, preventing them from hijacking the genome. It’s a system he plans to study in more detail.

At this point, he and Hannon’s team have a “bird’s eye view” of piRNAs based on “unique patterns which are highly suggestive of how the system can work,” he says. More data must be gathered, but what is completely clear so far is the importance of the piRNA pathway: when it’s deleted in model organisms, it results in full sterility, Brennecke says. “The protection against [transposons] is of vital importance for the species to keep existing.”

Looking aheadAs Brennecke makes his way to

Vienna to set up his own lab at IMBA, where he will be a group leader starting in January, he says Drosophila will continue to be his model org of choice thanks to the high level of conservation between flies and mammals in the key path-way of interest.


In April 2003, Cell published a pa-per that largely sums up Brennecke’s PhD work. “Bantam encodes a de-velopmentally regulated microRNA that controls cell proliferation and regulates the proapoptotic gene hid in Drosophila” describes the work that went into proving that a par-ticular mutation in the fly genome was actually a member of the newly discovered class of miRNAs.

Another Cell paper, this one pub-lished in 2007 and entitled “Discrete small RNA-generating loci as master regulators of transposon activity in Drosophila,” provides a glimpse of his postdoc years in Hannon’s lab, Brennecke says. During this time, he investigated piRNAs, which are heavily involved in a genome’s ability to protect itself from transposons.


Brennecke says he was once told that most Nobel laureates win for a discovery they made before the age of 25 — and, based on that, he just doesn’t think he has a chance. “I have a strong feeling this is well over [for me],” he says. “I don’t see anything I did so far as real pioneer work.”

— Meredith Salisbury

Julius Brennecke RNA INTERFERENCE

TITLE: Postdoctoral Fellow, ColdSpring Harbor Laboratory, through 2008; Group Leader, Institute of Molecular Biotechnology in Vienna, starting January 2009EDUCATION: PhD, EMBL Heidelberg and Ruprecht-Karls University Heidelberg, 2004RECOMMENDED BY: Victor Ambros


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Another Mode of Gene Silencing

Making an important discovery while still considered young is great, but not nec-

essarily in a funding climate that isn’t laying grant money at your feet for follow-up studies. This situation could describe that of Kevin Morris, who was first author on a paper de-scribing transcriptional gene silenc-ing for the first time in 2004, while he was still a postdoc in David Loo-ney’s lab at UCSD. Today, Morris is an associate professor at Scripps and his work is focused around elucidat-ing the function of that mechanism.

Transcriptional gene silencing, or TGS, differs from the more well known post-transcriptional gene silencing in that noncoding RNAs turn off transcription by interacting with a gene’s promoter. In his study, Morris introduced siRNA into the nucleus of cultured human cells and found that silencing worked through an “epigenetic mode” where the nucleosome was altered structurally, preventing RNA polymerase from binding and transcription from be-ginning. Instead of having transient silencing, though, TGS can affect cells for a longer time — a month instead of three or four days, which is common to post-transcriptional silencing — opening up the possibil-ity for more effective therapeutic gene knockdown.

“What happens is you get this sort of remodeling that occurs at the gene promoter,” Morris says. “If you were considering water and snow and rainfall, [TGS is] stopping the rain from actually falling, whereas post-transcriptional silencing is stopping the water from reaching the ocean by building a dam.”

At Scripps, Morris, along with his

two graduate students, continues to work on studying TGS. He wants to know exactly how endogenous non-coding RNA can induce the effect and how it can be therapeutically targeted to genes involved in HIV and cancer. He’s also partnered with Roche to study how noncoding RNAs are involved in stem cell differentia-tion, and whether or not TGS can be used to revert cells to a pluripotent state. “The technology doesn’t exist right now for looking at noncoding RNAs in this sense,” he says, not-ing that high-throughput sequencing could work. Roche is working on generating an array-based platform to study this.

Going into his PhD, Morris wanted to work on HIV. As a postdoc at UCSD, he worked at the Center for AIDS Re-

search on genetic-based therapy, and during the course of his studies, his curiosity about RNAi got the better of him. He designed a project separate from what he was working on be-cause he thought it’d be “interesting to see if we can just target a promoter and turn it off.” Subsequent post-graduate studies with John Rossi al-lowed him even more time to figure out how TGS was working.

Looking ahead

Morris would like to see better ways to knock down genes in hu-man cells. Right now, he relies on a combination of biotin-labeled oligos or siRNAs, pulldowns, chip assays, and “tweak[ing] protocols that are out there to fit to what we need.” If he could invent a technology, it’d be a “promoter array that could measure directionality of transcription.” This, he says, would allow him to distin-guish the role of bidirectional tran-scription in gene regulation, cancer development, and HIV infection.

Separate from transcription factors and miRNAs, noncoding RNAs have just begun to make their mark in the area of gene regulation, and Morris hopes that in five years people will be paying more attention to them in this context. “There’s a lot more RNA modes of regulation that we’re just now starting to see, and it’s really go-ing to be fascinating, in my opinion, in the near future,” he says.


As for the Nobel prize, Morris says he “would probably go the route of Jean Paul Sartre and decline to take that.”

— Jeanene Swanson

Kevin Morris RNA INTERFERENCE

TITLE: Assistant Professor, Department of Molecular and Experimental Medicine, The Scripps Research InstituteEDUCATION: PhD, University of California, Davis, 2001RECOMMENDED BY: John Rossi


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A Compelling Look at Variation

When Greg Cooper applied to two post-doc positions at the University of Wash-

ington — one with Evan Eichler, the other with Debbie Nickerson — what he really wanted was to move from mammalian genomics research to work that had more direct applica-tion in understanding human genet-ics and population characterization. Accepted by both PIs, Cooper is now doing a joint postdoc project that has put him right in the thick of the very research he found so appealing.

The project, which began in 2006, aims to connect structural variation data from the human genome to the common traits linked to them. He began by studying the genetic effects of statin response, but the rapid progress in technology for in-terrogating structural variation, as well as scientists’ understanding of the mechanisms involved in it, prompted him to expand his scope. Studying variation in the HapMap samples has, for example, become a major focus of Cooper’s lab work, he says.

A key goal is to find the real links between this kind of variation and its phenotypic effect. While much of the community has gotten tremendously excited about genome-wide associa-tion studies, Cooper notes that the problem so far is that most of the re-sults have provided “minimal infor-mation” about variations of “modest effect.” That isn’t going to radically change patient care, he says. “As a

predictive tool, that’s almost use-less. That’s not clinically relevant to tell somebody” about a variant of negligible effect, he adds. “The only way to make good on that is to learn about the biology,” he says. His goal is “coming up with ways to provide a mechanistic understanding to explain the associations that we’re observing.”

One possible reason that would explain why so many genetic variants don’t appear to have a major effect is the possibility that much of the phe-notypic variation is actually caused by rare variants, Cooper says. “Copy

number variants are a great example of this.” But tracking down the rare variants will be particularly chal-lenging, requiring not only signifi-cant drops in the cost of sequencing but also access to samples from enor-mous populations. “It’s going to come down to sequencing and picking up all those rare SNPs,” he says.

A key to coming up with the “com-pelling biology” explanations will be improved functional annotation, Cooper says. Back in his grad school days at Stanford University School of Medicine where he earned his genet-ics PhD with advisor Arend Sidow, Cooper was involved in the ENCODE project that has placed a premium on this kind of work.


To get more of a sense of his work, Cooper says it’s a good idea to check out “Systematic assessment of copy-number variant detection via genome-wide SNP genotyping,” a paper he co-authored that was pub-lished in Nature Genetics. The paper builds on previous work from the Eichler lab analyzing genomic data for nine individuals that was pub-lished earlier this year.


Not me, Cooper says. “I see genom-ics as becoming such a collaborative enterprise,” he says, “it seems like the notion of a Nobel prize being awarded to a single person for a discovery” is a paradigm that won’t work much longer for the field. “I wish I had an answer that I was on the verge of something extraordi-nary,” he laughs.


Greg Cooper STRUCTURAL VARIATION

TITLE: Senior Research Fellow, University of WashingtonEDUCATION: PhD, Stanford University School of Medicine, 2006RECOMMENDED BY:Evan Eichler, Debbie Nickerson

Finding variations of modest effect won’t radically change patient care. “As a predictive tool, that’s almost useless,” Cooper says.


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It’s All in the Eyes

Adriana Briscoe has spent the past 10 years look-ing into eyes — of but-terflies, that is. Briscoe

looks at the genes encoding visual pigments in the photoreceptor cells of butterfly eyes and has shown that much of the diversity of butterfly wing color can, in fact, be attributed to variability in what colors these pigments are sensitive to.

“My lab is studying the evolution and functional genomics of butterfly color vision,” she says. “We’re trying to understand how butterflies see the world, and what forces, natural or sexual selection, have shaped the evolution of their eyes.” To that end, she employs a range of tools, from DNA sequencing to gene expression profiling using in situ hybridization to phylogenetic computational analysis. Briscoe uses transgenic Drosophila to express butterfly visual pigments in photoreceptor cells.

As a grad student at Harvard, Briscoe says, “I had this intuition that the eyes of butterflies might be as diverse, evolutionarily, as the color of their wings. But at the time, no one knew anything about the molecular basis of vision.” So Bris-coe went on a mission to find out, publishing one of the first papers on the cloning of a visual pigment in the swallowtail butterfly. “I dis-covered that this particular species of butterfly had more rhodopsin genes than were expected based on the physiological studies that had been published,” she says.

During her postdoc, she began to spatially map the expression pat-terns of these duplicate genes, some of which were co-expressed in dif-ferent receptor cells, overturning the idea of what had been previ-

ously thought to be a one gene-one receptor pattern of expression. Since then, she’s looked at butterflies from all of the five major families, “and they all have different visual sys-tems based on the photoreceptors that are present in their eyes,” she says. “They literally see the world through different eyes.”

Because her current approach is to integrate many levels of biologi-cal analysis, she says, her biggest challenge is in the area of protein biochemistry. “We have moved into the area of protein biochemistry, and my proteins of interest, invertebrate rhodopsin-based photoreceptors, are notoriously tricky to express and functionally characterize,” she says. “That’s what I spend most of my time agonizing over.”

Looking aheadBriscoe hopes to see the field ad-

vance not just technically, but in terms of increased collaborations. “I would like to see more work in our field integrating population ge-netics, molecular evolution, protein biochemistry, neurophysiology, com-putational modeling, and behavior linking the visual worlds of animals to their predators, and in particular, linking biologically relevant signals to observers,” she says.


In a paper published in the Journal of Experimental Biology in 2006, Bris-coe and colleagues found that males and females of one species of butter-fly have two distinct sets of receptors. “[They] literally have different eyes, [and] these eyes are unique com-pared to other butterflies,” she says. They found that in the lycaenid but-terfly, a species with sexually dimor-phic wings, males have a dorsal eye with only UV- and blue-sensitive pig-ments, while dorsal eyes of females have an additional third pigment that is sensitive to long wavelengths and is co-expressed with blue-sensitive pigments. This increased visual ca-pacity has likely driven the diversity of butterfly wing coloration, Briscoe believes.


As for the Nobel, Briscoe would veer off and hope to win it for “a totally unrelated topic, curing schizophrenia, something that actu-ally would really help people.” she says.

— Jeanene Swanson

Adriana Briscoe GENE EXPRESSION

TITLE: Associate Professor, Department of Ecology and Evolutionary Biology, University of California, IrvineEDUCATION: PhD, Harvard University, 1999RECOMMENDED BY: Marcie McClure


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Imaging Gene Regulation

A s a computer scientist with a biology back-ground, Uwe Ohler knows a thing or two

about collaboration. On his web-site at Duke University’s Institute for Genome Sciences and Policy, where he’s an assistant professor in bio-statistics and bioinformatics, he’s listed several current collaborators. They range from colleagues at the IGSP to researchers at the University of Chicago and the Max Planck In-stitute. For the kind of work he does — computationally predicting and mapping gene regulatory elements — it’s essential to involve a multi-disciplinary cast.

Ohler’s lab focuses on transcrip-tion start site and microRNA target site prediction. While his lab aims to map regulatory sequences, the team is also putting these predictions to functional test using high-through-put microscopy. To study gene ex-pression in plant roots, incorporating imaging allows Ohler to “look at individual expression profiles from a single gene or a few genes, and look at that in a living organism under the microscope — and actually be able to tell exactly where and when was the gene expressed and how that could change under different conditions,” he says. “Microarrays only tell us the average story.”

Recently, Ohler won a Human Frontier Science Program. In this work, Ohler looks at individual ex-pression patterns of a handful of genes in Drosophila embryos under the microscope, and then compares those to patterns in different species and back to the regulatory DNA se-quences. “Ultimately what we hope to do is see where the sequences change and how that correlates with

a change in the expression of the genes,” he says.

Ohler started his career majoring in computer science and minoring in biology at University of Erlangen-Nuremberg in Germany. It was while working on his honors thesis that he began looking at promoter se-quences. That “got me hooked on this whole area of computational biology,” he says. During his PhD at the University of Erlangen, he spent three years as a visiting researcher for the Berkeley Drosophila Genome Proj-ect on a grant from the Boehringer Ingelheim Foundation.


In a 2006 Bioinformatics paper enti-tled “Quantification of transcription factor expression from Arabidopsis

images,” Ohler and colleagues im-aged the expression pattern of one gene, tagged with a green fluorescent marker, in many different tissues, and then mapped that data back to an atlas image in order to get relative expression values. “We have been working on a robotic platform to scale up the generation of images,” he says, “and a good property is that we can do that in living plants and thus get expression over time from a single specimen.”

Looking ahead

Ohler sees more and more people taking advantage of high-throughput microscopic image data to decipher gene networks, particularly in the areas of gene regulation, gene ex-pression, and regulatory genomics. He expects that, in time, image data will be “more high throughput and available.”

Ohler also sees next-generation se-quencing tools as having a big impact on his work. In contrast to micros-copy, “the technology’s not going to be the limiting part,” he says. “It will be more a matter of keeping up with the pace of the technology develop-ment as a computational person and adjusting our models to actually deal with that data in terms of just basic infrastructure.”


If he were to win the Nobel, Ohler suggests petitioning the Nobel com-mittee to add another category to include computational research. “I think it would be pretty astonishing in general if somebody wins for work that is mostly theoretical.”

— Jeanene Swanson

Uwe Ohler GENE EXPRESSION

TITLE: Assistant Professor, Biostatistics & Bioinformatics,Duke UniversityEDUCATION: PhD, University of Erlangen-Nuremberg, 2002RECOMMENDED BY: Hunt Willard


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A Fresh Look at Differential Expression

Sometimes taking a back-ward approach to a com-mon problem can yield surprising results. Such is

the thought process of Marian Wal-hout, who is using a roundabout way of studying differential gene expres-sion in C. elegans.

The standard operating procedure for this kind of gene expression research is usually a method centered on transcription factors, wherein one starts with a transcription factor of interest and then proceeds to locate where it binds in the genome. Wal-hout is going the other way around by taking a piece of DNA and then attempting to find out the factors that bind to that piece of DNA.

“Technically this approach is differ-ent because we use a method called the yeast one-hybrid system, whereas the method that has been used a lot is the yeast two-hybrid system, so it is different from what anybody else is doing in the field,” she says. “I’m not saying one-hybrid is better or is more important, but that it’s complementary — and this is what we need, different methods that can tackle the problem in different ways and from different angles. And that’s our unique approach to the problem that no one is doing.”

After finishing what she calls a “hard core” PhD in the biochem-istry of transcription in her native Holland, Walhout was eager to find the best place to do a postdoc in the US; Marc Vidal, who has a lab at the Dana-Farber Cancer Institute, came highly recommended. “Marc works in yeast, and that I liked, and

he was interested in understanding the human genome from the view of protein-protein interactions, and that I liked, even though it was a little enigmatic to me at the time,” she says. “Then we met and really hit it off. We are completely like-minded in science, and both of us benefited tremendously from working with each other, so it was highly syner-gistic, which was just fantastic.”

Looking ahead

Walhout says that ultimately her lab has two big genome-related goals for the long term. The first is to go one or two orders of scale more than what they are currently able to do in

C. elegans. “We are further automat-ing our pipeline and we are almost there, so in three years or so we should have much more comprehen-sive networks that we can study and get the idea of the principle for how they control gene regulation within the worm,” she says.

And if she can get the funding, Walhout says she would also like to apply the approach she has devel-oped to humans. “Because now we have set up all the technology in the worm, now we are ready to go to an even more complex problem, which is the human genome,” she says. “So that is midterm, but even longer term, it will be very exciting to start comparing those networks.”


In 2006, Walhout and her colleagues published “A gene-centered C. elegansprotein-DNA interaction network” in Cell. The team demonstrated for the first time its approach to differential gene expression, which was in oppo-sition to what the field had been doing up to that point. The authors postu-lated that highly connected transcrip-tion factors were more important for the survival of the nematode when compared to transcription factors with fewer connections, suggesting that there is a multi-layered system of gene regulation.

And the Nobel goes to ...

“I think our work is a very impor-tant contribution to the field,” she says. “And that is something I would like to be recognized for by the com-munity, even though it may not be Nobel accomplishment.”

— Matthew Dublin

Marian Walhout GENE EXPRESSION

TITLE: Associate Professor, University of Massachusetts Medical School EDUCATION: PhD, Medicine Utrecht University, The Netherlands, 1997RECOMMENDER: Marc Vidal

“This is what we need, different methods that can tackle the problem in different ways.”


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In the Informatics Trenches

As the head of the Verte-brate Genomics Group at the European Bioin-formatics Institute and

a co-leader of the data flow group for the 1,000 Genomes Project, Paul Flicek is intimately familiar with the ongoing battle to make sense out of the endless stream of next-gen sequencing data. Flicek helps guide the vertebrate genomics group in its responsibilities for providing the comparative, variation, and func-tional genomics resources within En-sembl, a joint project led by EBI and the Sanger Institute to maintain eu-karyotic genome data. And in his work for the 1,000 Genomes Proj-ect, which already has upwards of 3 terabases of data, he has the daunting task of getting the data processed so that it can be used and analyzed by the community.

Before he took up informatics arms on the front lines of next-gen data management for large-scale genome projects, Flicek was a graduate stu-dent who had his sights set on study-ing tissue engineering and artificial organ production. An introductory course at Washington University on computational molecular biology led by Sean Eddy changed all that. “I was in that course for about three weeks when I decided that I was going to do [computational biology] rather than anything else I had decided to do at the time,” Flicek says. “That was at the same time when human genome sequencing was ramping up at Washington University and the whole excitement around finishing the human genome was very real, so the first work that I did was on gene prediction and comparative genomics-based gene prediction with the program TwinScan.” Soon after

finishing his PhD, Flicek went to join EBI for his postdoctoral work, where he continued his genome annotation efforts and also became a member of the ENCODE project.

EBI is also where Flicek met Ewan Birney, who gave him a practical perspective on how to approach real-world bioinformatics problems, he says. “One of the aspects of working with Ewan and the way he thinks [helped] me understand the real im-portance of the pieces of the puzzle for bioinformatics, and as a side effect, once the large-scale pieces get built, to be able to handle data that most other people would struggle to handle,” says Flicek.

One of the biggest challenges he faces is next-gen sequence data. “I have a slide that I give that compares the number of base pairs sequenced for the Human Genome Project to

the number of base pairs that a big genome center can sequence today with next-gen technologies,” Flicek says. “It used to be that the whole Human Genome Project could be done in a week, and now it’s just a few hours. Keeping up with that and making sense of it is a big challenge because our ability to produce data is way ahead of our ability to analyze and make sense of it.”

Looking ahead

Flicek says that improvements in sequencing accuracy will be critical to accelerating how scientists can apply that data. “A massive change in the accuracy of sequencing would mean that the amount of sequenc-ing that we generate in a project like the 1,000 Genomes Project or in the ENCODE project, we could do many different things very quickly,” he says. That’ll be a big step, though: Flicek’s wish would be to get raw data quality “a million times more accurate” than it is today, “so that the chance of an error in the sequencing was than one in 3 billion.”


In 2007, Flicek and a team of re-searchers at EBI and the Wellcome Trust Sanger Institute published “Ensembl 2008” in Nucleic Acids Research. The team provided an up-date to the research community on new additions to the project, includ-ing extensive support for functional genomics data in the form of a special-ized functional genomics database, genome-wide maps of protein–DNA interactions, the Ensembl regulatory build, and other improvements.

— Matthew Dublin

Paul Flicek BIOINFORMATICS

TITLE: Team Leader, European Bioinformatics InstituteEDUCATION: PhD, Washington University, 2004RECOMMENDED BY:

Alan Guttmacher


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Carl Kingsford COMPUTATIONAL BIOLOGY

A Computer Scientist Takes on Transcription

Carl Kingsford has one foot planted firmly in the analysis of biological net-works and the other in

bacterial and viral genome organi-zation and evolution. For biological network research, Kingsford is work-ing on addressing the problem of obtaining protein-protein interaction data from cells in a high-throughput method. He says the challenge is that people want to be able to ana-lyze these large networks to figure out how they’re organized and how they can be used to learn what the proteins are doing in the cell. To this end, he is focusing on how to use mathematical programming to look at these networks and predict protein function.

And on the bacterial genome analy-sis side, he is developing software to look at bacteria and the influenza virus. There are various projects un-der that umbrella, one of which is a freely available program he and his colleagues developed called Trans-TermHP. This tool enables research-ers to predict particular sequences of transcription terminators in bacteria. “Basically it’s a fundamental feature of the organization of the bacterial genomes that divide up the genome into genes that get transcribed at the same time. … This is a computa-tional way of finding those signals,” Kingsford says. “The bacterial and viral genome is a grab bag of a few different things, but that’s the main one that we’ve done.”

Although the biological network research is a relatively new area for Kingsford, the genome organization research is a direct result of his earlier training as a computer sci-

entist. In his graduate studies, he was mostly focused on computational biology and protein structure predic-tion that honed his algorithm and coding skills. This is the same skill set he uses to address the chal-lenge of extremely noisy data from biological network studies. “It’s a technical issue [that] we’re think-ing about mostly, and it’s how we can deal with this extremely use-ful, but also very noisy, data,” he says. “And that’s where computer scientists can excel, because we’ve developed a lot of methods for deal-ing with uncertainty of data sets over the years.”

Kingsford credits his PhD advisor Mona Singh, a professor of com-puter science at Princeton University,

for teaching him the value of due diligence in research and in put-ting papers together. “She has a very high standard for something being publishable, and that’s something that has influenced me greatly,” he says. “You don’t publish the smallest publishable unit. You work through all the possible bugs and different ways of looking at something until you’re really sure you understand it.” He also credits Steven Salzberg with encouraging him to keep a focus on practical, high-impact tools that are really useful for biologists looking to solve specific problems.

One thing that would help his studies would be technology that would allow for high-throughput, low-noise assays capable of identify-ing two proteins and tracking when and where they interact. “That would make a lot of the questions we try to answer about the evolution and organization of biological networks easier to answer,” he says, “but it would take some of the fun out it because it would be like giving you the answer.”


In a paper published in Genome Biology (“Rapid, accurate, compu-tational discovery of Rho-indepen-dent transcription terminators il-luminates their relationship to DNA uptake”), Kingsford and his col-leagues describe TransTermHP and its ability to detect Rho-independent transcription terminators. Using the program, the team predicted the locations of terminators in 343 prokaryotic genomes, representing the largest collection of predictions available.

— Matthew Dublin

TITLE: Assistant Professor, Department of Computer Science, University of MarylandEDUCATION: PhD, Princeton University, 2005RECOMMENDED BY: Steven Salzberg

“That’s where computer scientists can excel.”


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Researching to Help Researchers

James Taylor is one research-er dedicated to making life easier for other research-ers. Taylor is addressing

what he views as a severe lack of user-friendly interfaces for genomic data management and effective ways to make data analysis reproducible and easily shared. The UCSC Ge-nome Browser is a beautiful thing, for instance, but what is an investigator to do after downloading millions of SNPs and gigabytes’ worth of align-ments?

To help overwhelmed scientists get organized and make the most out of all the data, Taylor and his colleagues are developing Galaxy, a Web-based genome analysis tool set that allows users to conduct on-the-fly analy-sis of multiple genome data sets in any format by integrating a range of bioinformatics tools and link-ing them out to various data ware-houses. Galaxy lets users save and share every step of their analysis and workflow with other users, and also allows people to analyze multiple alignments, compare genomic an-notations, and profile metagenomic samples, among other tasks.

The bigger picture for Taylor is not just to provide tools and infrastruc-ture with projects like Galaxy, but also to facilitate a cultural change among biologists and computer sci-entists. “In the future, experimental and computational biology are only going to become more intertwined and even inseparable,” Taylor says. “What I hope to see is a culture that absolutely requires rigorous peer review of both aspects, that understands that software used in a published analysis needs to be open, needs to be delivered with the sup-porting information to allow review-

ers to verify derived results, and in general that if it isn’t transparent and reproducible, it isn’t science.”

Taylor says it was his exposure to the disconnect between those de-veloping computational tools and those generating the data early in his career that set him on his present crusade. “During the course of my PhD work, I came to notice how big a gap there is between people who are experimentalists by training and people who are computational by training, and how inefficient that relationship is both in terms of how people developing methods provide those methods to the community and people who want to use those meth-ods and what resources are available to them to actually analyze their own data,” he says. Taylor also believes that his work is very timely due to the growth in data generation with

next-gen sequencing platforms and density increases in tiling arrays.

Looking ahead

Considering future plans, Taylor says his goal is for disparate research groups to communicate more effec-tively through an informatics infra-structure. “I think a lot of the data storage and the databases are pretty ad hoc, so five years down the road we are going to face difficulties figur-ing out how things were done in the past,” he says. “What I’d really like to see is the community develop a lot of these communication standards and really have a focus on the question of both reproducibility and openness in research.”


Last year in Current Protocols in Bioinformatics, Taylor published a paper entitled “Using Galaxy to perform large-scale interactive data analyses,” which presented the Gal-axy interface to the bioinformatics user community. The paper shows how Galaxy can be used to help researchers find the top 100 protein-coding exons in the human genome with the most density of SNPs by accessing Galaxy’s URL and follow-ing a specific protocol.

And the Nobel goes to…

Taylor says that if he were to win a Nobel, he would like it to be for the discovery of some “new form of heritable information, without which we are completely stumped now, but which makes everything completely obvious.”

— Matthew Dublin

James Taylor COMPUTATIONAL BIOLOGY

TITLE: Assistant Professor, Emory UniversityEDUCATION: PhD, Pennsylvania State University, 2006RECOMMENDER: Ross Hardison


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Pushing Statistics to the Limit

One might wonder how Wolfgang Huber, a trained theoretical physicist, got into bioinformatics.

Blame it on math — and the serious amount of coding that physics PhDs have to do in the course of getting their doctorates. Programming “is pretty much part of [the] skills you have to pick up when you do phys-ics,” Huber says. And in reality, he adds, what he’s doing with biological data isn’t so far from what he origi-nally trained to do. “In a way, physics is describing nature with numbers, with mathematics, and I’m still doing that,” he says.

Huber, whose PhD degree is in quantum stochastics, moved in-to computational biology during a postdoc at the German Cancer Research Center in Heidelberg, and he now heads up a bioinformatics group at the European Bioinformatics Institute. There, he applies statistical computing and analysis to every-thing from microarrays to automated microscopy. “We apply [it] to emerg-ing technologies in genomics, in par-ticular new sequencing, like Solexa and 454 … and then automated phenotyping of cells and possibly model organisms using automated high-throughput microscopy.” On one hand, sequencing can yield a huge amount of genotype informa-tion, while on the other, imaging can give “very subtle and interesting phenotype variation,” he says. “In both cases, we’re really interested in the relationship between genotype and phenotype.”

Huber’s interest in biology co-incided, he says, with the emergence of the genomic era in the ’90s. With the Human Genome Project under-way and microarrays hitting the

scene as one of the first affordable ways to examine genomic informa-tion on a large scale, Huber took his part-time interest to the next level. Prior to his postdoc, Huber worked as a programmer at the clinic affili-ated with the University of Freiburg, which led him to start taking classes and learning about the new technol-ogy. He began to see biology as “a much more dynamic and exciting field,” which he compares to the state of physics in the early 20th century. “Completely surprising discoveries are being made,” he says.

Looking ahead

As for the future, Huber hopes tools will continue to improve to allow him to see images across four dimensions, including space and time. Viewing cells in real time is the ultimate goal,

he says. “It gives us the possibility to watch single cells and single mol-ecules do their job. Right now most of the data that we have is population averages,” he says. Another tool that would help is deep sequencing. But managing and analyzing all that new data is a formidable challenge; after all, finding people who are good at math and also interested in biology isn’t easy. “These people are very rare, very precious,” he says.


In a paper published in Nature,Huber and colleagues looked at genome-wide recombination events in S. cerevisiae, mapping crossovers, crossover-associated gene conver-sion, and non-crossover gene conver-sion across 56 yeast meioses. Their maps are the first high-resolution, genome-wide characterizations of the multiple outcomes of recombination for any organism.

“We used tiling arrays to detect the combination events at very, very high resolution — an unprecedented reso-lution,” Huber says. Achieving this near-base pair resolution required Huber to bring in the big guns, statistically speaking. Like all ex-perimental tools, he says, stats can be optimized, or tweaked, to make the tool experimentally better and stron-ger. “We had to push the limits of statistical data analysis much further than had been done before to achieve this high accuracy of our genotype calls. If we had used existing meth-ods, the data would have been much too noisy,” he says. “It’s almost like building a new telescope or a new mi-croscope. We can suddenly see things that we couldn’t see before.”

— Jeanene Swanson

Wolfgang Huber COMPUTATIONAL BIOLOGY

TITLE: Group Leader, European Bioinformatics InstituteEDUCATION: PhD, University of Freiburg, 1998RECOMMENDED BY: Janet Thornton


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From Signal Transduction to Cystic Fibrosis

It’s not every scientist who can successfully juggle basic research with clinical studies, but Xudong Yao has found

a way. His lab at the University of Connecticut has an ongoing col-laboration with the Cystic Fibrosis Foundation, but he also maintains an emphasis on the more fundamen-tal side with a focus on phospho-rylation and understanding signal transduction.

Yao came to the US in 1995 after earning his bachelor’s and master’s degrees at Nanjing University in China. He completed a PhD in 2000 at the University of Maryland, and stayed at the school for another few years to carry out a postdoc with Catherine Fenselau. The Fenselau lab has a long tradition of combining “chemistry strategies with mass spec-trometry to study biomedical prob-lems,” Yao says. His goal there was to work on approaches that would allow scientists to perform “selective analyses of certain numbers of pro-tein targets in order,” he says, noting that as proteomics advances, people are looking for a greater level of con-trol over how peptides or proteins are targeted for analysis.

But Yao’s path to UConn wasn’t a direct one. He “took a detour,” he says — he headed to industry for a short time, working first at GeneProt and then at Millennium Pharmaceu-ticals. While he’s glad to be back in academia, his time in industry was a stroke of luck: through GeneProt, he met Diana Wetmore, now a leader at the Cystic Fibrosis Foundation and Yao’s connection for establishing a

partnership with the group.Through the collaboration with the

foundation, Yao’s lab uses “a mass spec assay to monitor the plasma membrane expression” of CFTR, the critical gene linked to cystic fibrosis. His time in industry has given Yao an awareness of the importance of getting great tools out to potential users, and he hopes that his work with the assay for CFTR will be one example of this. “Hopefully this can be expanded to related proteins,” he says, “so that it can be applied to personalized medicine.”

On the basic research side, Yao and his team are busy trying to get at the sequence of phosphopeptides that

govern regulation of signal transduc-tion. “We try to use a chemical method in addition to gas-phase reaction [to enable] ultra-high specificity and a very high yield of certain ions,” he says. “By doing this we can increase the sensitivity by tens of times.”

Looking ahead

Going forward, Yao says he hopes to “continue to strengthen our re-lationship with the Cystic Fibrosis Foundation.” That involves applying new mass spec methods “to help find a cure of this disease,” he adds. Meantime, he will continue the focus on basic research as well, trying to figure out “how to increase the in-trinsic sensitivity and selectivity of the analysis of those molecules,” he says. He also expects that his lab will move more in the direction of what he calls “pathway phosphoproteom-ics.” This concept could eventually help in the clinical realm as well, he says, noting that many proteomic biomarkers tend to be difficult cases for mass spec analysis.


Last year, Yao co-authored a paper entitled “Oxygen isotopic substitu-tion of peptidyl phosphates for mod-ification-specific mass spectrometry,” which was published in Analytical Chemistry. The paper reports “the first method of isotopic substitution of a nonbridging oxygen atom in pre-existing phosphates on peptides,” which, according to the authors, solves “a long-standing, challeng-ing issue in the sample preparation of phosphopeptides,” the abstract states.


TITLE: Assistant Professor, University of ConnecticutEDUCATION: PhD, University of Maryland, 2000RECOMMENDED BY:

Catherine Fenselau

“Hopefully this can be expanded ... so that it can be applied to personalized medicine.”

Xudong Yao PROTEOMICS


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From Detection to Drugs

Giselle Knudsen wants to find a better way to de-tect proteins in cells. To this end, she is develop-

ing Raman spectroscopy, a detection method based on how the sample scatters light from a laser, for the life sciences, particularly proteom-ics and cancer drug target develop-ment. “It’s extremely sensitive. It’s going to enable us to quantify very small ratios of two different protein species at very low concentrations,” she says.

Knudsen, who originally trained as an enzymologist, made the switch to proteomics when she realized that looking at proteins in a vial doesn’t necessarily reflect how those proteins behave in a cell. “I realized that we can do reductionist biology only so long. We have to start looking at what things actually do in the real cell,” Knudsen says. Along with her postdoc advisor, Jo Davisson at Pur-due University, who also trained in enzymology, Knudsen is adapting to a world where “enzymologists are becoming proteomics experts and they are interested in classical phar-macology — but they don’t call it that anymore.”

First, Knudsen is focused on working on Raman spectroscopy, which she says is more sensitive than fluorescence-based detection methods and is competitive with other detection methods used in bi-ology. “I can measure the spectrum of these two species in the same measurement. That means that I re-duce the noise, the background, so I can get very accurate and precise

numbers,” she says.Once the technique is refined,

Knudsen plans to apply it to study-ing the diversity of protein isoforms found in cells, especially cancer cells. “We’re realizing in proteomics that it’s not just one enzyme that’s inside of a cell, it’s actually probably 10 or 15 different forms of the same enzyme. Each of those 10 or 15 dif-ferent forms have different, unique functions,” she says. “In cancer biol-ogy, for example, we want to be able to target just one of those 15.” By targeting that one protein, she says scientists will be better able to define that protein as a drug target and thus reduce the number of side effects or nonspecific effects seen when that

drug is used in patients.


Knudsen’s work on Raman spec-troscopy was recently published in Bioconjugate Chemistry. In it, Knud-sen and her colleagues were able to use their Raman spectroscopy-based technique to accurately and precisely quantify human GMP synthetase. In the single-point determination mode, synthetase with mass ranging from 1 μg to 1 ng could be measured with between a one and six percent error and as an imaging application with a relative standard deviation of 16 percent.

Looking ahead

Knudsen hopes that Raman spec-trometry will be applicable in a clini-cal setting. “The only reason I am working on this methodology is be-cause I really want to use it. The next step is, once I get people to accept the method, I really want to start solving problems. It’s going to have to get much more clinical, I think,” she says.


Knudsen doesn’t approach her work thinking about Nobel prizes — she looks at it as wanting to change the way people do experiments, particu-larly how they measure molecules, to be more accurate. “If I can be recognized in the future for doing biomolecular detection, that would be great,” she says. “But I see myself as being very spread among different fields, so I don’t know if that’s going to work for me.”

— Ciara Curtin

Giselle Knudsen PROTEOMICS

TITLE: Research Scientist,University of California, San FranciscoEDUCATION: PhD, University of California, San Francisco, 2003RECOMMENDED BY: Charles Buck

“We have to start looking at what things actually do in the real cell.”


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Step by Step, a Better Mass Spec

For Wei-Jun Qian, the appeal of a place like the Pacific Northwest Nat ional Laboratory

comes down to technology develop-ment. “This place is really so technology-oriented,” he says, “it’s a great place for me to integrate lots of different techniques.”

Qian has long been involved in de-veloping ways to improve technology. Proteomics has always been known for its difficulty with quantification, and that is one area where Qian has spent a good deal of time. Par-ticularly for clinical studies, he says, good quantification practices have been tough to come by. “Proteomics is so complicated, so complex,” he says, noting that practitioners must be able to detect proteins of very low abundance in a sample for studies involving, say, a protein biomarker for disease.

Qian’s goal is to marry the benefits of high-throughput biology with the advantages of a more targeted ap-proach. “I’m trying to link global discovery with target validation,” he says. This work relies on a triple-quadrupole mass spectrometer. Qian says he also works a lot on chemistry and labeling approaches to try to im-prove quantification as well.

At PNNL, his mandate is to use mass spec-based proteomics and to develop new methods for apply-ing the technology. The idea is to improve scientists’ ability to study post-translational modifications, cell signaling, and disease biomarkers by finding ways to see proteins more clearly and precisely.

In one of his main projects, Qian is getting ready to analyze samples from hundreds of patients over many time points to get a better grasp of diabetes. “We initially spent almost two years trying to work out the techniques” to prepare for this study, he says. His next challenge will be to help determine “which protein is the most interesting,” he adds.

Looking ahead

In the next few years, Qian says that he will continue the diabetes work and expects to focus his tech-nology improvement efforts specifi-cally on studying and understanding that disease. The proteomic analysis

of pancreatic islets could help de-termine why diabetes patients fre-quently lose these cells, and possibly even how to help recover them, he hopes. He says he would also like to get involved in developing a cancer biomarker discovery project.


A review paper in Molecular & Cel-lular Proteomics that came out in 2006 entitled “Advances and challenges in liquid chromatography-mass spec-trometry-based proteomics profiling for clinical applications” offers a good overview of the hurdles yet to be overcome in the field, Qian says.

Qian was lead author on a paper published in the Journal of Proteome Research in 2005. “Probability-based evaluation of peptide and protein identifications from tandem mass spectrometry and SEQUEST analy-sis: The human proteome” reports the findings of Qian and collabora-tors’ study of how to assess false positive rates in the identification of peptides by analyzing three human proteome samples. The authors note that false positive rates were higher for peptides identified from human plasma samples than for those identi-fied from human cell lines, and they suggest new filtering criteria to im-prove confidence in peptide calls.


Qian says that he would feel that he had earned a Nobel prize if he could “really identify a protein [that was] a huge therapeutic target, or something resolving some sort of cure for a dis-ease like diabetes — using proteom-ics, of course.”


Wei-Jun Qian PROTEOMICS

TITLE: Senior Scientist, Pacific Northwest National LaboratoryEDUCATION: PhD, University of Florida at Gainesville, 2002RECOMMENDED BY: Dick Smith

“I’m trying to link global discovery with target validation.”


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A Better Approach to Interpretation

Shamil Sunyaev’s inter-ests run the gamut. One of them is in mutations, which he studies from

three different points of view: func-tion, evolution, and medicine — all without picking up a pipette. In his lab at Harvard Medical School, Sun-yaev is developing computational and statistical tools to analyze muta-tions and genetic variations. Also, he is developing bioinformatics tools to deal with the coming sequencing and proteomics data.

Sunyaev and his lab are working on methods to predict molecular func-tion from sequence data. For this, Sunyaev takes a comparative genom-ics approach and collaborates with functional genomics groups. Then, they also take a look at that same data with an eye toward evolution-ary genetics and how the mutations they see affect fitness. In particular, Sunyaev is interested in evolutionary patterns and what happens to those variants over time. Finally, he col-laborates with medical geneticists to analyze how these genetic variants affect phenotypes.

Sunyaev is also preparing for the coming glut of sequence data and the information about rare alleles that will be hidden in these. “Human genome sequencing is becoming so accessible that we will have many, many human genomes,” he says. “You have very large amounts of data and sequencing will discover very many variants — and many of them will be rare in the population.”

In his lab, Sunyaev is working on developing methods to analyze those sequences and plan for upcoming resequencing projects. “Right now, in this regard, we’re trying to do population simulations…to look at

the available data and try to learn population genetic models and then simulate very large resequencing studies,” Sunyaev says. This will help indicate “what strategies are going to be best — what is possible, what is not possible to do, and so forth, to inform future studies and to develop tools to be used for these studies,” he adds.

Another objective in the Sun-yaev lab is developing computational methods for proteomics. One such project has Sunyaev comparing pro-tein interactions and protein com-plexes across yeast species. Another is part of the SysCode consortium which has the goal of engineering mammalian organs, but in a way that is informed by developmental proteomics.

Sunyaev says the challenges fac-ing his lab include incorporating all

the different disciplines they cover. “There’s so many nice projects, I think we just do too much,” he says.

Looking ahead

The field is moving very quickly, Sunyaev says. “This is what I’m bank-ing on: I think that we will have a lot of genomic data and something will come out of that or not come out [de-pending] on how it is interpreted,” he says.

In the 1960s through the 1980s, Sunyaev says, there were a lot of nice analyses developed but there was no data for them. Soon, he says, “the position will be reversed.” He expects that there will be an abundance of data, and the bottleneck will be in-terpreting it all.


A 2001 paper of Sunyaev’s in Human Molecular Genetics estimated that a single human genome contains 1,000 deleterious mutations by examining the effects that amino acid replace-ments have on protein structure and function. In a more recent paper in the American Journal of Human Genet-ics, Sunyaev and his colleagues stud-ied the role of low-frequency genetic variants in disease and found that 70 percent of low-frequency missense alleles are mildly deleterious, mean-ing that they are associated with a loss in fitness.

And the Nobel goes to...

Sunyaev would like to win for “a model completely explaining the inheritance of complex traits and the molecular architecture of the traits.”

— Ciara Curtin

Shamil Sunyaev POPULATION GENETICS

TITLE: Assistant Professor, Harvard Medical SchoolEDUCATION: PhD, Moscow Institute of Physics and Technology, 1997RECOMMENDED BY: Peer Bork


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Elucidating Evolution, Virus by Virus

It may be a far cry from elu-cidating the finer points of spacetime, black holes, and quantum mechanics, but Raul

Rabadan is thoroughly content with his decision to leave the world of theoretical physics for a research post working on RNA viruses. Just two years ago, Rabadan was hard at work publishing papers on string theory as a postdoc at the Institute for Advanced Study at Princeton, when he began to take notice of the school’s new center for systems biol-ogy. “I started talking to a colleague of mine, another string theorist who was working in biology, and found that what they were doing was rather interesting,” says Rabadan. “Then, I decided to collaborate with them, and then at some point, I just de-cided to switch fields.”

Rabadan says he likes the fact that, from a basic scientific point of view, viruses are excellent mod-els for understanding how living organisms work and evolve. “Even if we sequence every human on the Earth, the question becomes, how can we deduce the history and the pressures? How do we deduce selection for some particular genes and how these genes are evolving in disease, given the data?” says Raba-dan. “But with viruses, you can see how they are evolving with time be-cause they are evolving very fast. So I think viruses can give us a very good understanding of evolution — not to mention they are burdens to our society, and understanding

how they work is something very important.”

Rabadan’s current area of focus is the analysis of RNA viral evolution, which happens at a much faster rate than many other model systems. He and his colleagues are currently working on ways to analyze thou-sands of viral RNA genome datasets — with particular targets of HIV and influenza — in order to better understand their evolution and epi-demiology. “We have a lot of samples, such as the 1918 flu, which, almost a century later, has changed almost 15

percent of its genome,” he says. “So if we take a mammalian example, that’s like comparing a mouse to a human, so we have an idea of how evolution is working — population genetics, mutation, and selection.”

In order to find the changes that take place over time in RNA viral genomes, which can run from 1,000 to 100,000 base pairs in length, he and his fellow investigators have developed algorithms capable of large-scale comparative analyses of human and avian viral genomes. Through such methods, they have demonstrated evidence supporting the hypothesis that the H1N1 strain of the influenza virus entered the human population before the actual 1918 outbreak, possibly as early as 1910.


In a 2006, Rabadan published a paper entitled “Comparison of hu-man and avian influenza A viruses reveals a mutational bias on the viral genomes” in the Journal of Virology. In it, Rabadan and his colleagues pre-sented what he calls a very “nice and simple idea” that aims to improve understanding of viral outbreaks. The aim of their research was to un-cover and characterize the changes a virus undergoes as it jumps from one host to another — in this case, from bird to human — usually resulting in pandemics such as the 1918 influenza virus. After the analysis of several genomes of different viruses living in birds and humans, the research-ers discovered that viruses evolving in different species’ host organisms have very distinctive mutation pat-terns.

— Matthew Dublin

TITLE: Assistant Professor, Department of Biomedical Informatics, Columbia University College of Physicians and SurgeonsEDUCATION: PhD, Universidad Autonoma de Madrid, Spain, 2001RECOMMENDED BY: Bud Mishra

“Even if we sequence every human on the Earth, the question becomes, how can we deduce the history and the pressures?”

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Cancer in Dogs, Cats, and People

Dogs and people aren’t that different, especially if you take a long view — go back far enough

and there’s a mammalian ancestor common to both. That’s why North Carolina State University’s Rachael Thomas is studying dog lymphoma with an eye toward how it is similar to human lymphoma, particularly focusing on genetic abnormalities correlated with disease subtype. “Human lymphoma and dog lym-phoma effectively share an absolutely extraordinary level of conservation,” Thomas says.

Currently, Thomas is focusing on developing resources so that she and her colleagues can compare the ge-netic profiles of human and canine lymphomas. She has made a high-resolution genomic microarray for the dog containing a panel of genetic markers that have been mapped back to the dog genome. “We can now directly convert information we have obtained on chromosome abnormali-ties to establish, by looking at the genome sequence assembly, which genes are impacted by those genomic abnormalities and, in turn, translate that back into the corresponding regions of the human genome to see whether the human and the dog counterparts of the same disease ac-tually share the effects of same-gene frequency,” Thomas says.

Dogs are a population of highly inbred animals that have predisposi-tions to certain cancers, making their population easier to study than hu-

mans. “It [is] far easier for us to gen-erate large amounts of genomic infor-mation that we can actually translate back and forth,” Thomas says.

Looking ahead

For the future, Thomas is looking to move beyond the dog to study lym-phoma in the cat. In cats, lymphoma is associated with inflammation, par-ticularly in response to the feline immunodeficiency virus. This, Thomas says, is also a naturally oc-curring system to study the lympho-ma that people with HIV develop, as well as how viral infections and

cancer are intertwined. “By which I mean,” she adds, “working out what is actually the trigger that causes the transition from a benign, chronic form of inflammation into full-blown malignancy.”

As she did in dog, Thomas is developing the tools to study genetic variation in feline lymphoma. “My hope is that in the next few years, I can develop this area of feline genomics so that ... we can actually really get a handle on some of these factors which are a big feature of hu-man cancers but for which there is no other feasible model that we can study,” she says.


Thomas’s work on microarrays for the dog genome was just published in Cytogenetic and Genome Research.But her work on the dog genome goes back to her undergraduate days, and she is one of the many authors on the dog genome sequence assembly paper published by Nature in 2005. “That is something that I will always be proud of,” she says.


“What I would really love to be able to do is ... translate our discoveries in genomics into some form of tool that really does represent a genuine, meaningful advancement in cancer diagnosis, disease management, for both humans and veterinary spe-cies,” Thomas says. “That really exemplifies this whole ‘one medi-cine’ concept that we have, that the combination of those two fields has far more impact than just looking at them separately.”

— Ciara Curtin

Rachael Thomas COMPARATIVE GENOMICS

TITLE: Research Assistant Professor, North Carolina State UniversityEDUCATION: PhD, University of Leicester, 2000RECOMMENDED BY: Barbara Sherry

“We can now directly convert information we have obtained on chromosome abnormalities ... to establish which genes are impacted.”


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Investigating the Perils of Obesity

As a pediatrician, the University of Michigan’s Carey Lumeng has been watching the rise of obe-

sity among children. Along with obe-sity come diseases such as asthma and diabetes, among others, but ex-actly why too much fat is so bad for people is still not known. Lumeng suspects it has something to do with inflammation. “There have been a wide variety of genomic studies that have also validated the idea that inflammatory markers are strongly associated with obesity as well as diabetes,” Lumeng says.

Some of those screens found that macrophages are a component of the inflammatory response, and other studies have shown that macrophages invade fat tissue. “I think the bulk of the literature now has demonstrated pretty strongly that … these mac-rophages actually [are] required to generate inflammation with obesity and, in fact, if you get rid of them or block their function, the mice don’t get diabetes,” he says.

But macrophages are also found in other, normal tissues where they do not cause problems. The question there, Lumeng says, is: “Why is one in one context maybe OK and in the other context maybe bad for you?” Indeed, there are different types of macrophages, M1 and M2, as Lu-meng and his colleagues published a little more than a year ago. Lean mice, he says, have M2 macrophages and obese ones have M1 types, but what triggers the switch between the types is not yet known, nor is it clear whether the findings in mice will be

smoothly translated to people.“My lab’s going to be looking at

this in human tissue and, in ad-dition, we’re going to be trying to understand what regulates one type of macrophage, the good macrophage for example the M2s, and what regu-lates the bad guys, the M1s,” Lumeng says.

One method that his lab is already using to study these macrophages is a new imaging approach. Fat, Lumeng says, is difficult to cut and tends to auto-fluoresce. To get a better look, his team has been using confocal microscopy on whole pieces of tissue. “It just lets us get a depth of imaging that looks at all the different cells in three dimensions at a much higher

resolution,” he says. As his lab gets up and running and starts collecting samples from patients — including those who have undergone bariatric surgery — and from different types of fat tissue, Lumeng hopes to be able to use high-throughput drug screen-ing and genomics.


It was in the January 2007 issue of the Journal of Clinical Investigation that Lumeng and his colleagues first proposed their model of different types of macrophage activation and how that activation is altered in obe-sity. In a Diabetes paper published online in October, Lumeng starts to address the mechanisms behind this by using flow cytometry, immuno-fluorescence, and expression analysis.

Looking ahead

Lumeng says that the next few years will accelerate how people under-stand the role of inflammation in obesity, especially in diabetes. Al-ready, Lumeng says that some diabe-tes drugs that were thought to target fat cells have been found to actually focus on macrophages. He also says there are more diabetes drugs in the pipeline that target inflammation.


If Lumeng were to win, he hopes it would be because he found a way to stop the negative effects of obesity by addressing inflammatory changes. “Clearly this is going to be a huge issue in the next decades because all the obese children are going to be-come obese adults,” Lumeng says.

— Ciara Curtin

Carey Lumeng TRANSLATIONAL RESEARCH

TITLE: Assistant Professor,University of MichiganEDUCATION: MD/PhD, University of Michigan, 2000RECOMMENDED BY: Alan Saltiel

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Large-Scale Screens to Study Diabetes

Run n ing l a rge- s c a le chemical screens isn’t easy, even if you’re at a world-class facility like

the Broad Institute. As a research fellow at the Broad’s chemical biology program for four years and now the group leader for pancreatic cell biology and metabolic disease, Bridget Wagner spends most of her days optimizing the basics of her small molecule screens, with an eye on drug targets for type 1 dia-betes.

Her research focuses on finding ways to stop or slow down the de-struction of pancreatic beta cells, which, in diabetes, cease producing insulin and die off. Wagner’s ap-proach takes advantage of large-scale biology tools and the Broad’s exten-sive screening facility. “What we’re trying to do is see if we can identify compounds that can be used to pro-mote beta cell growth and health in the case of type 1 diabetes,” she says.

Her approach to stimulate the body to regenerate beta cells is three-pronged. The first tack is to “stimu-late the beta cell itself to divide,” she says, which would restore insulin production. The second is to try to prevent cell death in the first place by identifying “compounds that could overcome the autoimmune-based attacks on beta cells,” she says. The third method is through “trans-differentiation of other cell types in the pancreas.” To this end, her lab develops cell-based assays to observe different aspects of beta cell function and health, and then performs screens with small organic synthetic compounds. “All three of these approaches use high-through-put screening as one of their main

technological foci,” she notes.Wagner says she’s lucky to have the

Broad’s resources, which include an enviable screening facility. “There’s a very large push towards synthesiz-ing libraries of compounds here that are all slightly structurally related to each other, and that gets us a little bit closer to understanding why some of the chemistry is having an impact in the cells.”

One of the toughest challenges is identifying a positive assay readout to serve as a control. “It’s often useful if you already have a compound that does what you want or a particular cell culture condition that mimics the state that you want to have,” she says. Another hurdle is finding a good model for the beta cell. “There are a number of mouse cell lines, called in-sulinoma cell lines,” she says, “[and]

they produce insulin, they act like a beta cell, but they’re a poor substitute for an actual beta cell.”

Wagner says that while current screening technology works well, her work would be greatly acceler-ated with improved culturing tech-niques for pancreatic islet cells to bring that process into the high-throughput realm. The ideal sce-nario, she says, would involve being able to specify cell identity on com-mand. “The same way that geneti-cally it’s now possible to turn a hu-man skin cell into an iPS cell, we’d love to have small molecules [where we] can say, ‘All right, you’re start-ing out to be a pancreatic exocrine cell, but now you’ll be a beta cell,’ or vice versa,” she says.


Her work promises much clinical application. In a collaboration with fellow Broad member Vamsi Mootha this year that was published in Nature Biotechnology, Wagner used cell-based assays to screen almost 2,500 small molecules to probe mitochondrial gene expression pat-terns. What she found was, among other things, cholesterol-lowering statins resulted in mitochondrial toxicity. “It’s important because sta-tins cause a kind of rare but very serious muscle myopathy as a side effect,” she says.


As for the Nobel, Wagner would like to take her current research focus all the way. “I would think for curing diabetes, but that’s fairly ambitious,” she says.

— Jeanene Swanson

Bridget Wagner TRANSLATIONAL RESEARCH

TITLE: Group Leader in Pancreatic Cell Biology and Metabolic Disease, Broad Institute of Harvard & MIT EDUCATION: PhD, Harvard University, 2003RECOMMENDED BY: Stuart Schreiber


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‘Cells In, Disease Out’

Coming from a chemical engineering background, the University of Illi-nois at Urbana-Cham-

paign’s Charles Schroeder also wanted to do something practical for human health. He is now developing a microfluidics-based tool to trap single DNA molecules so they can be genotyped. “The overarching goal of this project is to develop an integrat-ed microfluidics device ... where we take cells in and we get a genotype or some information about disease on the output,” Schroeder says. “It’s cells in, disease out.”

The microfluidics tool he and his lab are working on traps and stretch-es single DNA molecules using het-erodynamic, or flow, forces. “Using this approach, we have a really nice way to just stretch out and set our mass-trap for linear analysis of their backbone,” Schroeder says.

Once the DNA molecule is held by the device, a bit of biochemistry comes into play. The scientists add fluorescent, sequence-specific DNA-binding proteins so the DNA se-quence can then be determined. “Just like a barcode sequence, you can read out the position of these fluorescent probes along the stretch of the DNA molecules,” Schroeder says.

By using techniques from single molecule analysis, biochemistry, and physics, Schroeder and his group are developing a novel tool. “We really are doing some new technol-ogy development, so there are some significant barriers to overcome,” he says. “Whenever you do something for the first time or try to figure out a new technology, it’s challenging,” he says.

Another challenge, though, is get-ting the projects underway. “At this

stage in my career, [the challenge is] basically getting a good momentum in my own research group,” Schroeder says. His research covers a variety of fields and his lab has to have the proper mix of expertise. “As a man-ager or a PI, it’s doing the right hiring and making sure that your group will have the skills that it needs to be successful in an interdisciplinary field,” he says.

Schroeder has learned persever-ance, in its many forms, from one of his graduate school mentors, Steven Chu. One of Chu’s lessons was that “there [is] more than one way to solve a problem,” Schroeder says. “You can’t get discouraged if the first or second way doesn’t work.”

Schroeder says he has also been inspired by Chu’s career trajectory; Chu began as an atomic physicist and now focuses on pure biology.

“It was a lifelong learning for him. It wasn’t just turning the crank or doing the next sort of incremental thing. Everything he approached, he tried to make his mark on and do in a revolutionary type of way,” he says.

Looking ahead

Schroeder is encouraged by the current explosion of genome-wide association studies that are pinning genes to disease. But he says this is only the beginning. He hopes that the next few years will bring more detailed studies and resequencing of putative disease genes and investiga-tion into rare alleles. “I view what’s been done — while they are really interesting and amazing studies — [as] almost, at this point, proof-of-principle,” Schroeder says. He says that researchers need to look beyond SNPs and study how copy number variations and larger rearrangements affect human health.

Publication of note

Much of Schroeder’s current work deals with genotyping, but his back-ground is in single-molecule research. A 2003 Science paper from his gradu-ate career typifies his earlier work. “That’s a really nice demonstration of a polymer physics study [and] this manipulation of single DNA chains, or DNA chains at the single molecule level,” Schroeder says.


Schroeder would like to win for developing “a technology that would advance human health in some major way.”

—Ciara Curtin

TITLE: Assistant Professor, University of Illinois at Urbana-ChampaignEDUCATION: PhD, Stanford University, 2004RECOMMENDED BY: Alan Guttmacher

Charles Schroeder TRANSLATIONAL RESEARCH


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The Physicist Who Tackled Cancer

Gad Getz was a physi-cist with biology envy. After earning degrees in physics and mathemat-

ics, he realized that he was interested in applying some of these concepts, such as statistical mechanics, to the biology realm.

He started out “doing cluster-ing of protein structure and then gene expression data” and eventu-ally found himself on a path to the Broad Institute, where a postdoc with Todd Golub landed him squarely in the middle of microRNA research, among other projects. By last year, Getz became head of the cancer ge-nome analysis group at the Broad and had established himself as a full-fledged member of the systems biology community.

Today, Getz’s goal is to combine as much data as possible to paint a comprehensive picture of cancer. He gathers information from sequence reads, copy number, gene expres-sion, methylation status, genome-wide analysis studies, and more. All of that gets fed into GISTIC, an algorithm Getz started writing as a postdoc to scan through reams of cancer data and pick out the muta-tion patterns that mark the onset of cancer from all the other complex changes that take place in a tumor cell but do not actually cause cancer. Getz refers to this as “distinguishing the drivers from the passengers” on the road from normal cell to tumor. The algorithm is designed to analyze all the data, looking for mutations occurring at a higher frequency than you’d expect by chance, he says.

But picking out cancer-linked genes is just a stepping-stone to the real goal of figuring out which pathways are implicated in tumorigenesis. Getz

suspects that it won’t be the individ-ual genes as much as the pathways they belong to that could be the crucial factor in cracking the cancer puzzle.

Getz is a member of the Cancer Genome Atlas consortium, for which he’s continuing his past work of “de-veloping these methods that take all of these genomic data and come out with biologically significant events,” he says.

Looking ahead

Getz hopes to take his knowledge of cancer analysis and put it to work across the tumor development pro-cess. He will use mouse models to study several phases of cancer, sam-pling tumors over time to find out “which events occurred first and

which are later,” he says. Eventu-ally, he hopes to gather enough data to begin applying population genet-ics methods to tumor sample col-lections, but he believes that will require a breakthrough in single-cell interrogation abilities. “We still don’t necessarily have the technology to do all of these things,” he says. He adds that integration of large-scale data sets remains a problem; dealing with different modalities of data could be-come more and more arduous unless this challenge is faced.


Two papers in particular sum up Getz’s more impressive endeavors. In “Assessing the significance of chromosomal aberrations in cancer: methodology and application to gli-oma” in PNAS, Getz was a co-lead au-thor describing GISTIC, or Genomic Identification of Significant Targets in Cancer, the algorithm he devel-oped to scan data from tumor cells and generate predictions of which mutations are cancer-inducing in-stead of caused by the cancer.

In the other paper, Getz was part of the Cancer Genome Atlas project, which published findings in Naturethis fall regarding its study into lung adenocarcinoma. Researchers found 26 genes that appear to be highly linked to the onset of this type of lung cancer, a significant increase in the number of genes suspected to play a role in the disease.


This question doesn’t delay Getz at all. His dream achievement? “Curing cancer,” he says.


Gad Getz TRANSLATIONAL RESEARCH

TITLE: Head of Cancer Genome Analysis, Broad InstituteEDUCATION: PhD, Weizmann Institute of Science, 2003RECOMMENDED BY: Alan Guttmacher


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http://www.tri-conference.com

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We comb through more than 60 blogs torecommend the most interesting posts.

Highlights from journals such as PLoSBiology and PNAS, plus weekly reports ofthe news and papers in Nature and Science.

Can’t get to every major newspaper andmagazine? We scan the mainstream mediato keep you up on the news you need.

Plug in for the Weekly Scan, our podcast of news and blog highlights.

We pose a new question each week. Castyour vote and see what your peers thinkabout issues of the day.

Don’t miss the young investigator of theweek, chosen through recommendationsfrom leading scientists in the field.


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Finding Function in Dark Matter

Len Pennacchio takes ge-netic variation seriously. As head of the programs for genetic analysis and

genomic technologies at the US De-partment of Energy’s Joint Genome Institute and a senior staff scientist at Lawrence Berkeley National Labora-tory, Pennacchio focuses on genetic variation, especially of the regula-tory parts of the genome. Specifically, he looks at how noncoding regions called enhancers promote gene ex-pression during development, and how this “dark matter” of the genome might play a role in disease risk.

He uses a combination of sequenc-ing and comparative analysis to pin-point possible enhancers, and then goes one step further into functional analysis. “We’re very interested in testing sequences in animals,” Pen-nacchio says, “so we make our pre-dictions and then immediately go in and test the sequences to see if they can turn on gene expression in mice.”

Using a beta-galactosidase reporter gene, he’s determined the activity of nearly 1,000 potential enhancers in humans. While most of the ele-ments were selected based on their conservation with other vertebrates, conservation alone doesn’t say what the gene is doing. “You can look at that sequence and try and predict until you’re blue in the face, but you’ll never most likely have any real clue as to what it’s doing” until functional analysis is performed. In the end, animal readout lets him see when and where a given gene is expressed. His lab primarily focuses on the developing brain and nervous system.

Linking genetic variation to disease is a looming goal, and he hopes to

throw next-gen sequencing at the problem. Pennacchio, who got his PhD with Rick Myers and did a postdoc in Eddy Rubin’s lab, still devotes most of his own research time to health-related issues. “We’re very interested in sequencing indi-vidual genomes and trying to un-derstand what are sequence variants and how do they contribute to hu-man disease,” he says. “We also set up a pipeline so that we can brute-force sequence genes in thousands of people [to] better understand the relationship of human variation to disease phenotypes.”

Looking ahead

Pennacchio sees the biggest chal-

lenge for his field to be a techno-logical one. While JGI has next-gen sequencers up and running — “and they’re exponentially more powerful than the old capillary sequencers,” he says — it’s still too expensive and labor-intensive to get individual hu-man genome sequences.

And even with all that sequencing data, the true power resides in func-tional testing. “I think now it’s time to try and figure out, does noncoding DNA play a large role in disease and what makes each of us who we are, or is it involved in protein encoding parts of the genome?” he says. In the next five years, Pennacchio predicts that research will move toward func-tional analysis of noncoding DNA to better understand the role of regula-tory elements in human disease.


In 2006 Pennacchio was first author on a Nature paper that characterized the in vivo enhancer activity of a large group of noncoding elements in the human genome that were conserved across humans and pufferfish or ultra-conserved across humans, mouse, and rat. Using his transgenic mouse assay, he found that 45 percent, or 75 of the 167 conserved segments, were enhancers of gene expression during embryonic development, and that the majority of the 75 enhancers directed expression to various regions of the developing nervous system.


Pennacchio says he’d like to win the Nobel for “trying to drive func-tional insights into the noncoding portion of the genome.”

— Jeanene Swanson

TITLE: Senior Staff Scientist, Lawrence Berkeley NationalLaboratory; Head, Genetic Analysis and Genomic Technologies Programs, Joint Genome InstituteEDUCATION: PhD, Stanford University, 1998RECOMMENDED BY: Eddy Rubin

Len Pennacchio REGULATORY ELEMENTS


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Where Collaborations Are King

Zhaolei Zhang remembers very clearly what set him on his course to becom-ing a scientist. He grew

up in China in the early 1970s, a time when the country was just “opening up to the rest of the world,” he recalls. At the time, being involved in science was suddenly very exciting and honorable, he recalls. “Nobel prize winners were national heroes,” he says. He and many of his friends wound up pursuing scientific degrees.

After majoring in biophysics, Zhang packed his bags and set out for Cali-fornia, where he earned his PhD with Sung-Hou Kim while studying protein crystallography and struc-tural biology. “I thought it was a really cool idea to use chemistry to study proteins,” he says. His research with Kim involved determining the structure and functional mechanism of complexes of membrane proteins that play a role in cell respiration. He was particularly involved in a project to establish the structure of a specific cytochrome complex.

In 2001 he packed up and headed to Yale University, where he complet-ed a postdoc with Mark Gerstein and got his introduction to yeast genetics. “I really enjoyed my time in Mark’s lab,” Zhang says. The timing couldn’t have been better: “It was at the time the genome age started to emerge.” During the three years he spent in Gerstein’s lab, Zhang learned com-

putational techniques and focused on pseudogene evolution as well as genome annotation.

Today, Zhang has his own lab at the University of Toronto, where the spotlight is on regulatory elements as well as yeast genetics and finding computational ways to understand the organism better. Zhang says his is a “dry” lab, but that he has made a point of establishing collaborations with scientists in experimental labs to put his computational work to the test. He and his team are inves-tigating the genomic and proteomic

attributes of yeast, with a specific eye on evolution, transcription, non-coding RNAs, and protein-protein interactions. The broad goal is to be able to pinpoint function of specific genes and proteins to understand not only what they do, but also how they work together. Zhang, who began at Toronto in the fall of 2004, has appointments in both the medical research and in the medical genetics and microbiology departments.


Earlier this year in the Genome Research journal, Zhang and his col-leagues published a paper called “The extensive and condition-dependent nature of epistasis among whole-genome duplicates in yeast.” He says that the study, which included a number of authors from a range of collaborating labs, involved looking “at how duplicated genes in yeast can rescue each other in stress and other conditions.” One method of analysis in particular used protein interaction data to determine the functional overlap between epistatic and non-epistatic genes.


Were he to be honored with a No-bel one day, Zhang says he hopes it would be for “finding a cure for AIDS.” HIV itself is a fairly small organism, he notes, and with the amount of money being poured in-to AIDS research from a variety of different funding organizations, he believes that this should be a trac-table problem. “I think it’s going to happen in the next 10 or 20 years,” he says.


Zhaolei Zhang REGULATORY ELEMENTS

TITLE: Assistant Professor, University of TorontoEDUCATION: PhD, University of California, Berkeley, 2000RECOMMENDED BY: Mark Gerstein

Zhang’s Genome Research paper, a study with a number of collaborators, looks “at how duplicated genes in yeast can rescue each other in stress and other conditions.”


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The Functional Perspective

Laura Elnitski will tell you that her early-career suc-cess comes from luck, but observers might notice

that her status as a young but highly regarded investigator at the National Human Genome Research Institute stems instead from her hard work and ability to balance backgrounds in cell biology and computational biology.

Elnitski began her studies at Penn State, where she earned her PhD working in the molecular and cel-lular biology department with Ross Hardison and followed up there in a computationally focused postdoc with Webb Miller. As a member of Miller’s group, Elnitski helped build a computational approach to model-ing genome regulation. Using a pair-wise alignment between human and mouse, the algorithm would “look at the entire genome and predict which regions looked more regulatory and which looked more like neutrally evolving sequences,” she says.

In 2005, Elnitski headed to NHGRI, where she studies noncod-ing functional genomic elements. “The idea is to discover them and then to understand how the dis-ruption of their sequences relates to human disease,” she says. In her lab, she and her team are using both computational and experimental ap-proaches to track various regulatory elements — such as promoters or ex-onic splicing enhancers — with the goal of better understanding whole classes of them. With that informa-tion, she could look for “similarities in them or features that we can capi-talize on to use for future prediction and annotation,” she says.

Her work is a natural fit with the ENCODE project, and Elnitski has

been a member of the consortium for a number of different efforts. One project analyzes bidirectional promoters, which involves a lot of annotation but also may help sci-entists get a handle on tumor sup-pressors, since these promoters have been linked to regulation of these suppressor genes. In another project for ENCODE, Elnitski and her team are studying negative regulatory ele-ments by developing “functional as-says [and] screening sequences to find more of them,” she says.

Looking ahead

As her work ramps up, Elnitski says she’d especially like to “tie in the epi-genetic modifications on the DNA or histones to try and understand what are the characterizing features of each of these types of functional elements,”

she says. She expects that each cell type will have its own signature panel of epigenetic markings, and that being able to use next-gen sequencing will put scientists several steps closer to being able to figure out cell identity and how the genome is regulated. She and her team are already looking into methylation of promoter regions and how that phe-nomenon is linked to tumor cells.

Elnitski is also eager to move from what she calls the “linear perspective of the DNA” toward a more three-dimensional look at what’s going on within the cell — but that’s going to be a very significant effort, she says. “I won’t get there by myself.”


In a Genome Research paper from June entitled “Detection and charac-terization of silencers and enhancer-blockers in the greater CFTR lo-cus,” Elnitski and her colleagues demonstrate what she refers to as novel regulatory elements — that is, negative regulatory elements that have been undercharacterized so far, she says. She is involved with the ENCODE consortium on the project to assess and annotate negative regu-latory elements.


Were she to be preparing for a flight to Sweden, Elnitski says she hopes her accomplishment would be “hav-ing some impact into understanding the molecular mechanisms of cancer. I think that ultimately that would be a gift to the medical community and in its own way it would be a very important thing to me personally.”


Laura Elnitski REGULATORY ELEMENTS

TITLE: Investigator, National Human Genome Research InstituteEDUCATION: PhD, Pennsylvania State University, 1998RECOMMENDED BY: Elaine Ostrander


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Steps to Start Your CareerExperts from academia and industry offer advice to make your career path as smooth as possible. By Meredith Salisbury

Advanced degrees

Is a PhD necessary or a waste of time? Would I be better off hav-ing the years of work experience instead of investing those years in the degree?

If your goal is to be in academia, this is simple: a PhD is a requirement. But if you’re looking to work in industry, there’s more nuance to the issue. “In a lot of places, people don’t get hung up on degrees and you rise to your own level,” says Michael Finney, a co-founder of MJ Research who now runs Finney Capital. “I know several people who had no graduate degree at all who did just fine. But you have to be better to do that.”

The PhD serves as evidence that you “have the ability to work towards a long-term goal,” which can give com-panies more confidence in a person, Finney adds. And there’s a problem with just getting a master’s degree: “A number of places give master’s as kind of a booby prize for people who dropped out of a PhD program,” he says. If you went through a degree program with the specific aim of getting a master’s, you should find a way to point that out so potential employers don’t simply assume you couldn’t cut the PhD, he adds.

Chris Bouton, computational biol-ogy group leader at Pfizer, says that a PhD isn’t required in industry: “If you’re very skilled in certain types of

activities, whether it’s programming or certain types of statistics, and you can demonstrate that skill time and time again, then you don’t necessar-ily need the PhD.” Still, he notes, not having it can limit your career path or make it harder to get your foot in the door.

What are the most valuable ad-vanced degrees for this field?

Jason Liu, director of business operations at Applied Biosystems, has a PhD and an MBA — a combina-tion that he finds very valuable and sees as a trend in the community, especially as more universities offer joint PhD/MBA programs. Having an MD/PhD or even just an MD is also very useful, he says. The MD could especially help if you have an interest in the clinical diagnostics industry, he adds.

Do companies avoid hiring PhDs for positions that are advertised as requiring a master’s?

This can indeed be the case, says Angela Wallace, a recruiter with Affinity Scientific. In some cases, it’s because the group leader is not a PhD, and the company might be “concerned about … having someone with a more senior degree report to them,” she says. Generally, though, the situation is simply that the job itself isn’t as challenging, and the

company doesn’t want to hire some-one who will get bored quickly and leave. “They want someone who can do that job on a long-term basis,” she says. “Turnover is really the biggest concern.”

Are universities overproducing PhDs? Is that increasing grant competition?

“When I was in grad school 10 years ago we were already over-producing PhDs,” Liu says. “I don’t think the situation has changed that much.” Combine that trend with the dwindling grant funding available and you’ve got some stiff competition on your hands, he adds.

The job search

I’m searching for my first job, and I don’t have many publications. What do I do?

That’s a question Wallace hears all the time, and she says often trouble arises because scientists may feel uncomfortable “selling” themselves in the job search process. It doesn’t pay to play it close to the vest in these situations, she says. “If they are truly interested in the job when they walk out of the interview, they should let the interviewers know.” As for a lack of publications or experience, make sure your résumé is as specific as possible, and don’t assume it’s safe to

Careers PROFESSIONAL LIFE


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skip over technical details. The first pass of résumés for a job opening might be done by an HR person who doesn’t have a scientific background, so you’ll need to market yourself well there, too.

Anything you can do to demonstrate your skills will help, says Bouton. If you’re applying for a bioinformatics position, for instance, don’t just talk about the classes you’ve taken in that area — try your hand at building a small database or website to show potential employers that you have the ability and follow-through.

The conventional wisdom is that you get the biggest promotions and raises by changing jobs, but is that still advisable given a bad economy? What are other avenues to getting a promotion or raise?

This depends on the scientific area someone’s involved in, says Wallace. There are some skill sets that are just so hard to find that people who have them can move anywhere, anytime. If you would like to stay with your employer but are hoping for a pro-motion, take on the burden of laying out goals that would be valuable to the organization; talk to your boss about them, and then establish them as milestones for your own devel-opment. Certain milestones should be tied to a raise or a promotion. Otherwise, it’s too vague — and that makes it easy for an employer to push off these benefits. “Giving very clear expectations for time periods and milestones is definitely the most effective way to negotiate a promotion or salary increase,” Wallace says.

I’m a postdoc. At what point should I be looking for a faculty position?

“You should always, in a sense, be looking,” says Ken Dewar, an asso-ciate professor of human genetics at

McGill University. But balance that with loyalty to your postdoc — you don’t want to leave your PI high and dry by ditching early. Dewar passes on advice that has served him well in his career: “The best training for your next job is how you act in your present one,” he says. This can help establish that “you do good work, you can be trusted, you do well with other people.”

Is it better to be research-track at a well known institution or tenure-track at a lesser-known one?

That depends on your own career goals, says Dewar. He warns against going someplace so small that there’s not a core group of scientists in your field already there; you’ll need access to good people and technologies, after all. But going to a lesser-known place doesn’t mean the community will lose track of you — establish-ing collaborations with colleagues at other institutions can help keep you connected. “I don’t think you have to be from Broad, Baylor, WashU, or U Washington to be successful,” he says.

How do I know when it’s time to leave my job and go somewhere else?

First things first, says Wallace: ask yourself why you’re thinking about leaving. A number of issues — such as feeling unchallenged or becoming interested in another aspect of busi-ness or science — may be solved simply by talking to your manager and asking for new or different re-sponsibilities. If that doesn’t work, or if you’ve “found that the culture or the people … is not a fit,” Wallace

says, then it’s time to start looking for another position.

Academia v. industry

I’m in academia and would like to pursue a career in industry. Where do I start?

Make sure you highlight any work you’ve done where skills you’ve acquired would be transferable to the job you’re applying for, says Wallace. If you’re considering a job in drug discovery at a pharma or biotech and some of your research has involved working with drug candidates or dis-covery technologies, point that out.

Also, it pays to know the dif-ferences between academia and in-dustry before you make the switch, Finney says, to avoid a “rude awak-ening” later. Industry scientists lack the “proprietary ownership of a proj-ect” that academics are used to; they can be moved from project to project regardless of their own interests, he says. On the bright side, he adds, industry scientists usually enjoy bet-ter hours, which can be more com-patible for family life.

Liu says if you decide you want to go to industry, do it as early as possible. “From the industry stand-point,” he says, “the longer you’ve stayed in academia, you will have a harder time to adjust to the industry setting and pace to the R&D work-flow.” Liu went straight from his PhD to working at a company, skipping a postdoc.

If I go to industry, is it possible to return to academia?

It may not be easy, but Bouton says he’s certainly seen many people

“The best training for your next job is how you act in your present one.”


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make the leap back. If you think you might want to move to academia at some point, it’s always a good idea to keep publishing, “no matter whether your employer values publications or not,” he says. When it’s time to make the move, consider a transi-tional step such as a core lab, which is more industrial than academia but more academic than industry. “It’s certainly easier for somebody coming out of industry to get a job at a core facility,” Bouton says. Liu adds that he’s seen people manage this by moving back to an academic postdoc position, and using that as a stepping-stone to the faculty path.

The business side

How do I make the transition from the research side of my organiza-tion to the business or manage-ment side?

This isn’t as tough as it might seem, says Finney. Start by talking to your boss and getting someone to report to you — a lab technician, for instance, who can help with your projects. If that goes well, you’ll get more reports, and soon enough it will be clear to your company whether you’re good as a manager. Consider taking a management class or two

at a local college, or at least pick up some books of advice from the busi-ness literature.

If you’re looking to move to another area of the company, such as market-ing or business development, Finney recommends asking your boss for re-sponsibilities in that area. “If they’re a good boss, they want to see you succeed and they want to help you out,” he says.

ABI’s Liu accomplished this by earning his MBA through a part-time program, a path he recommends to others. He says earning the degree while working was a challenge, but helped him appreciate the business lessons in a real-world setting.

The content here is excerpted from Genome Technology’s annual salary survey, published in July/August.

Consider taking a management class or two at a local college, or at least pick up some books.


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http://www.covarisinc.com






Grant OpportunitiesA number of funding agencies and other organizations offer grants targeted specifically at scientists who are just beginning their careers. By Jeanene Swanson

ORGANIZATION: The National Science FoundationAWARD: $400,000 over five years, minimum awardDETAILS: The NSF Faculty Early Career Development Program supports junior faculty who exemplify the role of teacher-scholars. Women, minorities, and people with disabilities are encouraged to apply.CONTACT: www.nsf.gov/funding

ORGANIZATION: National Alliance for Research on Schizophrenia and DepressionAWARD: $30,000 per year, for up to two yearsDETAILS: NARSAD’s Young Investigator Award Program provides support for promising young scientists in neurobiological research. Basic and or clinical investigators are supported, but research must be relevant to serious psychiatric disorders such as schizophrenia, mood disorders, anxiety disorders, or child and adolescent psychiatric disorders.CONTACT: www.narsad.org/research/apply/young/

ORGANIZATION: Prostate Cancer FoundationAWARD: $75,000 per year for three yearsDETAILS: This funding program supports early career prostate cancer researchers who are doing high-

impact prostate cancer research, preferably in a clinical or translational setting.CONTACT:

www.prostatecancerfoundation.org

ORGANIZATION: Infectious Diseases Society of America Education and Research Foundation and the National Foundation for Infectious Diseases Joint Research Awards AWARD: $30,000 per year for two yearsDETAILS: The Wyeth Young Investigator Award in Vaccine Development provides funding for outstanding research in vaccine development, either through clinical or laboratory investigation. The candidate must have a demonstrated commitment to vaccinology as a career.CONTACT: www.idsociety.org

ORGANIZATION: Office of Naval Research AWARD: As much as $170,000 annually for up to three yearsDETAILS: The ONR’s Young Investigator Program funds early-career scientists interested in basic and applied life science research, especially in marine life science, neuroscience, biorobotics, and biosensors. CONTACT: www.onr.navy.mil

ORGANIZATION: Alzheimer’s Association AWARD: As much as $100,000 total for up to two yearsDETAILS: The Alzheimer’s Association New Investigator Research Grant Program funds young investigators with less than 10 years of research experience but who are not postdoctoral fellows. CONTACT: www.alz.org

ORGANIZATION: Pew Scholars Program in the Biomedical Sciences AWARD: $70,000 per year for up to four yearsDETAILS: The Pew award funds researchers with a full-time appointment as an assistant professor, or equivalent, who have not held that position for more than three years and who are interested in basic or clinical biomedical research. CONTACT: www.futurehealth.ucsf.edu/pewscholar.html

ORGANIZATION: The McKnight FoundationAWARD: $75,000 annually for three yearsDETAILS: The McKnight Scholar Award is open to young scientists, typically one year into their faculty positions but not more than four years, who study learning and memory disorders. CONTACT: www.mcknight.org

Funding


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http://www.nsf.gov/funding

http://www.narsad.org/research/apply/young/

http://www.prostatecancerfoundation.org

http://www.idsociety.org

http://www.onr.navy.mil

http://www.alz.org

http://www.futurehealth.ucsf.edu/pewscholar.html

http://www.mcknight.org






ORGANIZATION: Arnold and Mabel Beckman FoundationAWARD: $300,000 over three yearsDETAILS: The Beckman Young Investigators Program supports chemists and life scientists on the tenure track at academic or nonprofit institutions who are not more than three years into their appointment. CONTACT: www.beckman-foundation.com

ORGANIZATION: Burroughs Wellcome FundAWARD: $500,000 over five yearsDETAILS: Burroughs Wellcome’s Career Awards at the Scientific Interface funds researchers with a PhDin mathematics, physics, chemistry, computer science, statistics, or engineering who study genomics, quantitative structural biology, or model complex systems. The grant funds researchers for two years of postdoctoral training plus three years as a faculty member. CONTACT: www.bwfund.org

ORGANIZATION: W.M. Keck FoundationAWARD: Up to $1,000,000 over five yearsDETAILS: The Keck Foundation’s Distinguished Young Scholars in Medical Research Program funds researchers focusing on the mechanisms of disease. The scholars must have held a full-time, tenure-track appointment for at least one year but not more than four years. CONTACT: www.wmkeck.org

ORGANIZATION: PhRMA FoundationAWARD: $30,000 per year for up to two yearsDETAILS: PhRMA Foundation’s Research Starter Grants fund faculty just beginning their careers with a focus on areas important to the

pharmaceutical industry, including health outcomes, informatics, pharmacology/toxicology, and pharmaceutics. CONTACT: www.phrmafoundation.org

ORGANIZATION: Ellison Medical FoundationAWARD: Up to $100,000 per year for four yearsDETAILS: Ellison Medical Foundation’s New Scholar Program in Aging funds basic biology research into development and age-related disorders. Researchers must be in the first three years of their career after their postdoctoral fellowship.CONTACT: www.ellisonfoundation.org

ORGANIZATION: Sidney Kimmel Foundation for Cancer ResearchAWARD: $100,000 per year for two yearsDETAILS: The Kimmel Scholar Award supports young investigators who are dedicated to cancer research. They must have been elected to an assistant professorship or equivalent within the last three years.CONTACT: www.kimmel.org

ORGANIZATION: Alliance for Cancer Gene TherapyAWARD: Up to $500,000 over three yearsDETAILS: The ACGT Young Inves-tigators Award promotes basic and pre-clinical gene therapy research. Scientists should have an MD, MPH, PhD, or equivalent degree and be a tenure-track assistant professor within five years of appointment to this role.CONTACT: www.acgtfoundation.org

ORGANIZATION: American Society for Mass SpectrometryAWARD: $25,000DETAILS: The ASMS Research Award

supports academic research by young scientists in mass spectrometry. Open to academic scientists within four years of joining a tenure-track faculty position or equivalent in a North Amer-ican university.CONTACT: www.asms.org

ORGANIZATION: The James S. Mc-Donnell FoundationAWARD: Up to $450,000 over three to six yearsDETAILS: The James S. McDonnell Foundation’s 21st Century Science Initiative Brain Cancer Research Award supports novel research into brain cancer. Proposals from junior faculty and those with neuroscience, genet-ics, molecular pathology, and tumor immunology backgrounds are encour-aged to apply.CONTACT: www.jsmf.org

ORGANIZATION: Kinship Foundation Searle Scholars ProgramAWARD: $100,000 per year for three yearsDETAILS: The Searle Scholars Pro-gram supports junior faculty pursu-ing independent research careers in biochemistry, cell biology, genetics, immunology, neuroscience, pharma-cology, and related areas in chemistry, medicine, and the biological sciences. Applicants must have been appointed to a tenure-track assistant professor-ship within the past two years.CONTACT: www.searlescholars.net

ORGANIZATION: Esther A. and Joseph Klingenstein FundAWARD: $150,000 over three yearsDETAILS: The Fellowship Award in the Neurosciences supports young inves-tigators who hold tenure-track faculty positions and are engaged in basic or clinical research that will lead to a bet-ter understanding of epilepsy.CONTACT: www.klingfund.org


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http://www.bwfund.org

http://www.wmkeck.org

http://www.phrmafoundation.org

http://www.ellisonfoundation.org

http://www.kimmel.org

http://www.acgtfoundation.org

http://www.asms.org

http://www.jsmf.org

http://www.searlescholars.net

http://www.klingfund.org

http://www.beckman-foundation.com






Next-Gen Tomorrow’s PIsWe asked last year’s roster of promising scientists to tell us about the next generation of stars — grad students and scientists taking their first steps. By Matthew Dublin

NAME: Michelle ChanAFFILIATION: The Broad Institute of the Massachusetts Institute of Technology and Harvard UniversityRECOMMENDED BY: Aviv RegevRESEARCH: Michelle Chan is currently working on her PhD in Aviv Regev’s lab, where she is focused on reconstructing the evolution of gene regulation from comparative expression data. Chan has already won some acclaim for an algorithm she developed which identifies all the regulatory modules from multiple species in a phylogeny and recon-structs their evolutionary history.

NAME: Don ConradAFFILIATION: The Wellcome Trust Sanger InstituteRECOMMENDED BY: Matthew HurlesRESEARCH: Don Conrad is currently a postdoc in Matthew Hurles’ lab, where he has already been first author on two papers in Nature Genetics from his graduate studies. According to Hurles, Conrad is show-ing tremendous promise with solid research projects on SNP and CNV variation in the human genome. Conrad is also very interested in developing and applying statistical methods for the measurement and analysis of structural variation in human populations.

NAME: Brian GregoryAFFILIATION: Salk Institute RECOMMENDED BY: Todd MichaelRESEARCH: As a postdoc in Joseph Ecker’s lab at the Salk Institute for Biological Studies, Brian Gregory is interested in taking what can be learned from RNA silencing pathways in plants and applying it to cancer chemotherapy. He hopes one day to use small RNAs to antagonize the effect of genes that protect cancer cells from being killed by chemother-apy. After his postdoc is completed, Gregory is slated to take an assistant professorship position at the Univer-sity of Pennsylvania.

NAME: Elinor KarlssonAFFILIATION: Harvard FAS Center for Systems BiologyRECOMMENDED BY: Pardis SabetiRESEARCH: In addition to being the first postdoc of the Sabeti lab, ElinorKarlsson is also gaining attention for her efforts to find genes related to disease susceptibility in African populations by applying new mapping methods she developed by working with genomics in dogs. Karlsson’s doctoral research focused on the genetic causes of diseases in pure-bred dogs in a collaborative project between the bioinformatics program at Boston University and the Broad Institute.

NAME: Vincent PlagnolAFFILIATION: The Cambridge Institute for Medical ResearchRECOMMENDED BY: Matthew HurlesRESEARCH: Vincent Plagnol is a postdoc in David Clayton’s group at the Cambridge Institute for Medical Research, where he is focusing on a broad range of statistical problems in population genetics and association studies. One area of focus for Plagnol involves his work with the Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflam-mation Laboratory, through which he aims to understand the genetics of type 1 diabetes.

NAME: Ilan WapinskiAFFILIATION: Harvard UniversityRECOMMENDED BY: Aviv RegevRESEARCH: Wapinski is primarily focused on the evolutionary histories of genes and their transcriptional regulatory programs in yeasts. Wapinski has already accomplished much in his young career, including winning two “Best Poster” awards at ISMB in 2004 and 2005 which presented SYNERGY, a novel algo-rithm he designed to reconstruct evolutionary histories of all genes in a large species group.

FRESH FACES Looking Ahead


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A Year LaterHow has the Tomorrow’s PIs Class of 2007 fared? Quite successfully, as it turns out. A glimpse of what the scientists have been up to in the past year. By Matthew Dublin


Greg Crawford has published several papers on high-resolution genome mapping and transcrip-tional regulators in the human genome.

Colleen Delaney produced and hosted a webcast for the Seattle Cancer Center on cord blood trans-plantation and its treatment appli-cations for autoimmune diseases in children.

Laurence Etwiller co-authored a paper describing a bioinformatics method for rapid identification of PAX2/5/8 direct downstream targets.

Lars Feuk has been awarded the Fu-ture Research Leaders grant by the Swedish Foundation for Strategic Research and will start his own re-search group at Uppsala University in early 2009.

Matt Hurles and his team released CNVtools, an R package for case-control association testing with copy number variation.

Amy Kiger is concentrating on elucidating the genetic basis for cell shape in Drosophila.

Dana Crawford is currently working on refining Vanderbilt University’s multiplexed arrhythmia genotyping platforms.

Rui Chen published several papers dealing with human female repro-ductive genetics and a genetic large population study of Saudi Arabians.

Hooman Allayee published several papers in 2008 dealing with genetic risk factors for cardio-vascular disease.

Claudine Bartels published a review of current trends in molecular diagnostics for breast cancer and leukemia.

Gill Bejerano received a research grant award from the Okawa Foundation and a Young Investiga-tor Award from the Human Frontier Science Program.

Philip Bradley is in the process of starting his lab, which involves applying to his first NIH grant, recruiting grad students, and set-ting up a computing infrastructure.

Martha Bulyk recently published a paper describing UniPROBE, an online database of protein binding microarray data on protein-DNAinteractions.

Class of 2007 WHERE ARE THEY NOW?


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Jonathan Sebat has been devel-oping new approaches to gene discovery in mental illnesses such as schizophrenia based on analysis of rare structural variants.

Jun Li published research in Naturedemonstrating that the greater the geographic distance between a population and its African ances-tors, the more changes accumulate in its genes.

Sarah Trimpin is now an assistant professor of chemistry at Wayne State University.

Vince Magrini is now a research assistant professor at Washington University’s Genome Sequencing Center.

Annie Tsong is developing a graphical model to quantify conservation and systematically identify changes in promoter com-ponents of S. cerevisiae.

Jarrod Marto and his team are working on establishing an infor-matics environment that enables self-directed discovery and cus-tomization for mass spectrometry data analysis.

Jernej Ule received the ERC Young Investigator Grant and a Human Frontier Science Program grant; he also wrote a review article on RNA-binding proteins in neuro-degeneration.

Josh Mendell became a Leukemia and Lymphoma Society Scholar and published work describing how the Myc oncogene represses tumor-suppressing microRNAs in cancer cells.

Yuntao Wu and his team completed a major study identifying an essential role of the HIV-1 envelope-chemokine coreceptor signaling in facilitating HIV-1 latent infection of resting CD4 T cells.

Kun Zhang spent his first year as a junior faculty member winning R01 grants under the Human Microbiome Program and the epigenomics initiatives.

Madhugiri Rao authored numerous scientific journal articles and chap-ters; he also acted as a judge to the State Science and Engineering Fair in Florida.

Heng Zhu continued his research efforts looking at expanding protein chip technology to various model systems.

Aviv Regev established a compu-tational framework to study regulatory networks. She also won a Sloan Fellowship and the NIH Director’s 2008 Pioneer Award to support her research.

Pardis Sabeti continues to perform with her band, Thousand Days, and parse out evidence of selection in the human genome.

Anuj Kumar published several pa-pers in 2008 and completed large-scale studies of yeast filamentous growth which revealed hundreds of genes previously unexplored in this cellular process.

Todd Michael is using his newly acquired SOLiD sequencer to find mutants, SNPs, and structural vari-ants; he also uses it to perform de novo assembly of small genomes and to measure gene expression.


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The GenomeWeb Intelligence Network.

Connecting the dots for researchers worldwide.

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Thanks to Today’s PIs

Victor AmbrosUniversity of Massachusetts Medical School

Eddy RubinJoint Genome Institute

Robert AustinPrinceton University

Peer BorkEuropean Molecular Biology Laboratory

Charles BuckPurdue University Bindley Bioscience Center

James CollinsBoston University

Sean EddyJanelia Farm,Howard HughesMedical Institute

Evan EichlerUniversity of Washington

Catherine FenselauUniversity of Maryland

Mark GersteinYale University

Alan GuttmacherNational Human Genome Research Institute

Ross HardisonPennsylvania State University

Mary-Claire KingUniversity of Washington

Marcie McClureMontana State University

Bud MishraNew York University

Debbie NickersonUniversity of Washington

Elaine OstranderNational Human Genome Research Institute

Vijay PandeStanford University

John RossiCity of Hope

Alan SaltielUniversity of Michigan Life Sciences Institute

Steven SalzbergUniversity of Maryland

Stuart SchreiberBroad Institute

Dick SmithPacific Northwest National Laboratory

Janet ThorntonEuropean Bioinformatics Institute

Doug TurnerUniversity of Rochester

Marc VidalDana-Farber Cancer Institute

Hunt WillardDuke Institute for Genome Sciences and Policy

Rick Wilson Washington University School of Medicine

Phillip ZamoreUniversity of Massachusetts Medical School

Recommenders


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Blunt End HUMOR, WE HOPE

Genome Technology (ISSN 1530-7107) is published ten times a year (monthly except combined issues in Jul/Aug and Dec/Jan) by GenomeWeb LLC, 125 Maiden Lane, New York,NY 10038. Periodicals postage paid at New York,NY,and additional mailing offices. Genome Technology is sent free of charge to qualified professionals in life sciences research. Non-qualified rate is $149 per year. POSTMASTER:Send address changes to GenomeTechnology,Peck Slip Station,PO Box 998,New York,NY 10273.


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http://www.sigma-aldrich.com






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