apollo - a webinar for the phascolarctos cinereus research community

An Introduction to Apollo A webinar for the Phascolarctos cinereus research community

Monica Munoz-Torres, PhD | @monimunozto

Berkeley Bioinformatics Open-Source Projects (BBOP)Lawrence Berkeley National LaboratoryJoint Genome Institute | University of California Berkeley | U.S. Department of Energy

04 August, 2015

APOLLO DEVELOPMENT

APOLLO DEVELOPERS 2

h"p : / /GenomeA r c h i t e c t . o r g /

Nathan Dunn

Eric Yao JBrowse, UC Berkeley

Deepak Unni Colin Diesh

Elsik Lab, University of Missouri

Suzi Lewis Principal Investigator

BBOP

Moni Munoz-Torres Stephen Ficklin GenSAS,

Washington State University

3

ANNOTATION PLAN

Introduction

Assembly freeze Automated Annotation

Manual annotation

Using Web Apollo

Curation freeze

Merge: automated +

manual

Genome-wide & gene-specific comparative

analyses

QC QC

Synthesis & dissemination.

OUTLINE

Web Apollo Collabora(ve Cura(on and Interac(ve Analysis of Genomes

4 OUTLINE

•  THE GENE MODEL predic(on, annota(on, cura(on

•  APOLLO

empowering collabora(ve cura(on •  APOLLO on THE WEB

becoming acquainted

•  EXAMPLE demonstra(ons

5

BY THE END OF THIS TALKyou will

v Be>er understand genome cura(on in the context of annota(on: assembled genome à automated annotaEon à manual annotaEon

v Become familiar with the environment and func(onality of the Apollo genome annota(on edi(ng tool.

v Learn to iden(fy homologs of known genes of interest in a newly sequenced genome.

v Learn about corrobora(ng and modifying automa(cally annotated gene models using available evidence in Apollo.

Introduction

REVIEW ON YOUR OWNfor manual annotation

To remember… Biological concepts to be>er understand manual annota(on

6 FOOD FOR THOUGHT

•  GLOSSARY from con$g to splice site

•  CENTRAL DOGMA

in molecular biology •  WHAT IS A GENE?

defining your goal

•  TRANSCRIPTION mRNA in detail

•  TRANSLATION

and other defini(ons

•  GENOME CURATION steps involved

7 CURATING GENOMES

What is a gene?

v  The defini(on of a gene paints a very complex picture of molecular ac(vity and it is a con(nuously evolving concept.

•  From the Sequence Ontology (SO): “A gene is a locatable region of genomic sequence, corresponding to a unit of inheritance, which is associated with regulatory regions, transcribed regions and/or other func(onal sequence regions”. “Evolving Concept” at h>p://goo.gl/LpsajQ

8 CURATING GENOMES

What is a gene?

v  In our life(me, the Encyclopedia of DNA Elements (ENCODE) project updated this concept yet again. Long transcripts & dispersed regula$on!

“A gene is a DNA segment that contributes phenotype/func(on. In the absence of demonstrated func(on, a gene may be characterized by sequence, transcrip(on or homology.”

https://www.encodeproject.org/

9 CURATING GENOMES

What is a gene?considerations

v  Consider : •  A gene is a genomic sequence (DNA or RNA) directly encoding

func(onal product molecules, either RNA or protein.

•  If several func(onal products share overlapping regions, we take the union of all overlapping genomics sequences coding for them.

•  This union must be coherent – i.e., processed separately for final protein and RNA products – but does not require that all products necessarily share a common subsequence.

Gerstein et al., 2007. Genome Res.

10 CURATING GENOMES

What is a gene?

v  “The gene is a union of genomic sequences encoding a coherent set of poten(ally overlapping func(onal products.”

Gerstein et al., 2007. Genome Res

11 CURATING GENOMES

TRANSLATIONreading frame

v  Reading frame is a manner of dividing the sequence of nucleo(des in mRNA (or DNA) into a set of consecu(ve, non-‐overlapping triplets (codons).

v  Three frames can be read in the 5’ à 3’ direc(on. Given that DNA has two an(-‐parallel strands, an addi(onal three frames are possible to be read on the an(-‐sense strand. Six total possible reading frames exist.

v  In eukaryotes, only one reading frame per sec(on of DNA is biologically relevant at a (me: it has the poten(al to be transcribed into RNA and translated into protein. This is called the OPEN READING FRAME (ORF) •  ORF = Start signal + coding sequence (divisible by 3) + Stop signal

v  The sec(ons of the mature mRNA transcribed with the coding sequence but not translated are called UnTranslated Regions (UTR); one at each end.

12 CURATING GENOMES

TRANSLATIONreading frame: splice sites

v  The spliceosome catalyzes the removal of introns and the liga(on of flanking exons. •  introns: spaces inside the gene, not part of the coding sequence •  exons: expression units (of the coding sequence)

v  Splicing “signals” (from the point of view of an intron): •  There is a 5’ end splice “signal” (site): usually GT (less common: GC) •  And a 3’ end splice site: usually AG •  …]5’-‐GT/AG-‐3’[…

v  It is possible to produce more than one protein (polypep(de) sequence from the same genic region, by alterna(vely bringing exons together= alternaEve splicing. For example, the gene Dscam (Drosophila) has 38,000 alterna(vely spliced mRNAs = isoforms

13

"Gene structure" by Daycd- Wikimedia Commons

CURATING GENOMES

TRANSLATIONnow in your mind

14

Text for figures goes here

CURATING GENOMES

TRANSLATIONreading frame: phase

v  Introns can interrupt the reading frame of a gene by inser(ng a sequence between two consecu(ve codons

v  Between the first and second nucleo(de of a codon

v  Or between the second and third nucleo(de of a codon

"Exon and Intron classes”. Licensed under Fair use via Wikipedia

CURATING GENOMESoverview

1  PredicEon of Gene Models

2  AnnotaEon of gene models

3  Manual annotaEon

CURATING GENOMES 15

16 Gene Prediction

GENE PREDICTION

v  The iden(fica(on of structural features of the genome:

•  Primarily focused on protein-‐coding genes. •  Predicts also transfer RNAs (tRNA), ribosomal RNAs (rRNA),

regulatory mo(fs, long and small non-‐coding RNAs (ncRNA), repe((ve elements (masked), etc.

•  Two methods for iden(fica(on. •  Some are self-‐trained and some must be trained.

17 Gene Prediction

GENE PREDICTIONmethods for discovery

1) Ab ini,o: -‐ based on DNA composi(on, -‐ deals strictly with genomic sequences -‐ makes use of sta(s(cal approaches to search for coding regions and typical gene signals. •  E.g. Augustus, GENSCAN,

geneid, fgenesh, etc.

3’

Nat Rev Genet. 2015 Jun;16(6):321-32. doi: 10.1038/nrg3920

18

Nucleic Acids 2003 vol. 31 no. 13 3738-3741

Gene Prediction

GENE PREDICTIONmethods for discovery (ctd)

2) Homology-‐based: -‐ evidence-‐based, -‐ finds genes using either similarity searches in the main databases or experimental data including RNAseq, expressed sequence tags (ESTs), full-‐length complementary DNAs (cDNAs), etc.

•  E.g: fgenesh++, Just Annotate My genome (JAMg), SGP2

19

GENE ANNOTATION

Integra(on of data from computa(onal & experimental evidence with data from predic(on tools, to generate a reliable set of structural annotaEons. Involves: 1) ab ini$o predic(ons 2) assessment of biological evidence to drive the gene predic(on process 3) synthesis of these results to produce a set of consensus gene models

Gene Annotation

20

In some cases algorithms and metrics used to generate consensus sets may actually reduce the accuracy of the gene’s representa(on.

GENE ANNOTATION

Gene models may be organized into “sets” using: v  automa(c integra(on of predicted sets (combiners); e.g: GLEAN,

EvidenceModeler or

v  tools packaged into pipelines; e.g: MAKER, PASA, Gnomon, Ensembl, etc.

Gene Annotation

ANNOTATION IS NOT PERFECT automated annotation remains an imperfect art

Unlike the more highly polished genomes of earlier projects, today’s genomes usually have:

•  more frequent assembly errors, which lead to annota(on of genes across mul(ple scaffolds

•  lower coverage

No one is perfect, least of all automated annotation. 21

Image: www.BroadInstitute.org

MANUAL ANNOTATIONworking concept

Precise elucidaEon of biological features encoded in the genome requires careful

examinaEon and review.

Schiex et al. Nucleic Acids 2003 (31) 13: 3738-‐3741

Automated Predictions

Experimental Evidence

Manual Annotation – to the rescue. 22

cDNAs, HMM domain searches, RNAseq, genes from other species.

The manual annotator evaluates all available evidence and corroborates or modifies genome element predic(ons.

BUT, MANUAL CURATIONdoes not always scale

Researchers on their own; may or may not publicize results; may be a dead-‐end with very few people ever aware of these results.

Elsik et al. 2006. Genome Res. 16(11):1329-‐33.

MANUAL ANNOTATION 23

Too many sequences and not enough hands.

A small group of highly trained experts (e.g. GO).

1 Museum

A few very good biologists, a few very good bioinforma(cians camping together for intense but short periods of (me.

Jamboree 2

Co"age 3

24

MANUAL ANNOTATIONobjectives

IdenEfies elements that best represent the underlying biology and eliminates elements that reflect systemic errors of automated analyses.

Assigns funcEon through compara(ve analysis of similar genome elements from closely related species using literature, databases, and experimental data.

MANUAL ANNOTATION

h>p://GeneOntology.org

1

2

GENOME ANNOTATIONan inherently collaborative task

Researchers oren turn to colleagues for second opinions and insight from those with exper(se in par(cular areas (e.g., domains, families).

APOLLO 25

We need annota$on edi$ng tools to modify and refine the precise loca$on and structure of the genome elements that

predic$ve algorithms cannot yet resolve automa$cally.

“Incorrect and incomplete genome annota$ons will poison every experiment that uses them”.

-‐ M. Yandell

APOLLOcollaborative genome annotation editing tool

26

v  Web based, integrated with JBrowse. v  Supports real (me collabora(on! v  Automa(c genera(on of ready-‐made computable data. v  Supports annota(on of genes, pseudogenes, tRNAs, snRNAs,

snoRNAs, ncRNAs, miRNAs, TEs, and repeats. v  Intui(ve annota(on, gestures, and pull-‐down menus to create and

edit transcripts and exons structures, insert comments (CV, freeform text), associate GO terms, etc.

APOLLO

h>p://GenomeArchitect.org

APOLLO ARCHITECTUREsimpler, more flexible

APOLLO 27

Web-‐based client + annota(on-‐edi(ng engine + server-‐side data service

REST / JSON Websockets

Annotation Engine (Server)

Shiro

LDAP

OAuth

JBrowse Data Organism 2

Annotations

Security

Preferences

Organisms

Tracks

BAM BED VCF GFF3 BigWig

Annotators

Google Web Toolkit (GWT) / Bootstrap

JBrowse DOJO / jQuery JBrowse Data Organism 1

Load genomic evidence for selected organism

Single Data Store PostgreSQL, MySQL,

MongoDB, ElasticSearch

Apollo v2.0

We train and support hundreds of geographically dispersed scien(sts from diverse research communi(es to conduct manual annota(ons, to recover coding sequences in agreement with all available biological evidence using Web Apollo. v  Gate keeping and monitoring. v  Tutorials, training workshops, and “geneborees”.

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DISPERSED COMMUNITIES collaborative manual annotation efforts

APOLLO

LESSONS LEARNED

What we have learned: •  Collabora(ve work dis(lls invaluable knowledge •  We must enforce strict rules and formats •  We must evolve with the data •  A li>le training goes a long way •  NGS poses addi(onal challenges

LESSONS LEARNED 29

Apollo h>p://genomearchitect.org/web_apollo_user_guide

1.  Select a chromosomal region of interest, e.g. scaffold.

2.  Select appropriate evidence tracks to review the gene model.

3.  Determine whether a feature in an exis(ng evidence track will provide a reasonable gene model to start working. -‐  select and drag the feature to the ‘User-‐created Annota(ons’

area, creaEng an iniEal gene model. If necessary use edi(ng func(ons to adjust the gene model.

4.  Check your edited gene model for integrity and accuracy by comparing it with available homologs.

Becoming Acquainted with Web Apollo 31 |

Always remember: when annota(ng gene models using Apollo, you are looking at a ‘frozen’ version of the genome assembly and you will not be able to modify the assembly itself.

31

GENERAL PROCESS OF CURATIONsteps to remember

32

APOLLOannotation editing environment

BECOMING ACQUAINTED WITH APOLLO

Color by CDS frame, toggle strands, set color scheme and highlights.

Upload evidence files (GFF3, BAM, BigWig), add combinaEon and sequence search tracks.

Query the genome using BLAT.

Naviga(on and zoom.

Search for a gene model or a scaffold.

Get coordinates and “rubber band” selec(on for zooming.

Login

User-‐created annota(ons. Annotator

panel.

Evidence Tracks

Stage and cell-‐type specific transcrip(on data.

REMOVABLE SIDE DOCKwith customizable tabs

HIGHLIGHTED IMPROVEMENTS 33

Annotations Organism Users Groups Admin Tracks Reference Sequence

EDITS & EXPORTSannotation details, exon boundaries, data export


1 2

Annotations

1

2


Reference Sequences

3

FASTA

GFF3

EDITS & EXPORTSannotation details, exon boundaries, data export

3

36 | 36 Becoming Acquainted with Web Apollo.

USER NAVIGATION

Annotator panel.

•  Choose appropriate evidence tracks from list on annotator panel. •  Select & drag elements from evidence track into the ‘User-created Annotations’ area.

•  Edge-matching.

•  Hovering over annotation in progress brings up an information pop-up.

37 | 37

USER NAVIGATION

Becoming Acquainted with Web Apollo.

•  Annotation right-click menu

38

Annota(ons, annota(on edits, and History: stored in a centralized database.

38

USER NAVIGATION


39

The Annota(on InformaEon Editor

DBXRefs are database crossed references: if you have reason to believe that this gene is linked to a gene in a public database (including your own), then add it here.

39

USER NAVIGATION


40

The Annota(on InformaEon Editor

•  Add PubMed IDs •  Include GO terms as appropriate

from any of the three ontologies •  Write comments sta(ng how you

have validated each model.

40

USER NAVIGATION


41 |

Zoom in/out with keyboard: shir + arrow keys up/down

41

USER NAVIGATION


•  ‘Zoom to base level’ op(on reveals the DNA Track.

•  Color exons by CDS from the ‘View’ menu.

•  Toggle reference DNA sequence and translaEon frames from either direc(on.

Annota(ng

“Simple case”: -‐ the predicted gene model is correct or nearly correct, and

-‐ this model is supported by evidence that completely or mostly agrees with the predic(on.

-‐ evidence that extends beyond the predicted model is assumed to be non-‐coding sequence.

The following are simple modifica(ons.

43 | 43

ANNOTATING SIMPLE CASES

Becoming Acquainted with Web Apollo. SIMPLE CASES

44 |

•  A confirma(on box will warn you if the receiving transcript is not on the same strand as the feature where the new exon originated.

•  Check ‘Start’ and ‘Stop’ signals arer each edit.

44

ADDING EXONS


If transcript alignment data are available and extend beyond your original annota(on, you may extend or add UTRs.

1.  Right click at the exon edge and ‘Zoom to base level’.

2.  Place the cursor over the edge of the exon un$l it becomes a black arrow then click and drag the edge of the exon to the new coordinate posi(on that includes the UTR.

45 |

To add a new spliced UTR to an exis(ng annota(on follow the procedure for adding an exon.

45

ADDING UTRs


1.   Zoom in to clearly resolve each exon as a dis(nct rectangle.

2.  Two exons from different tracks sharing the same start and/or end coordinates will display a red bar to indicate matching edges.

3.  Selec(ng the whole annota(on or one exon at a (me, use this ‘edge-‐matching’ func(on and scroll along the length of the annota(on, verifying exon boundaries against available data. Use square [ ] brackets to scroll from exon to exon.

4.  Check if cDNA / RNAseq reads lack one or more of the annotated exons or include addi(onal exons.

46 | 46

CHECK EXON INTEGRITY


To modify an exon boundary and match data in the evidence tracks: select both the offending exon and the feature with the expected boundary, then right click on the annota(on to select ‘Set 3’ end’ or ‘Set 5’ end’ as appropriate.

47 |

In some cases all the data may disagree with the annota(on, in other cases some data support the annota(on and some of the

data support one or more alterna(ve transcripts. Try to annotate as many alterna(ve transcripts as are well supported by the data.

47

EXON STRUCTURE INTEGRITY


Flags non-‐canonical splice sites.

Selec(on of features and sub-‐features

Edge-‐matching

Evidence Tracks Area

‘User-‐created Annota(ons’ Track

Apollo’s edi(ng logic (brain): §  selects longest ORF as CDS §  flags non-‐canonical splice sites

48

ORFs AND SPLICE SITES


49 |

Exon/intron junc(on possible error

Original model

Curated model

Non-‐canonical splices are indicated by an orange circle with a white exclama(on point inside, placed over the edge of the offending exon. Most insects, have a valid non-‐canonical site GC-‐AG. Other non-‐canonical splice sites are unverified. Web Apollo flags GC splice donors as non-‐canonical.

Canonical splice sites:

3’-‐…exon]GA / TG[exon…-‐5’

5’-‐…exon]GT / AG[exon…-‐3’ reverse strand, not reverse-‐complemented:

forward strand

49

SPLICE SITES


Zoom to review non-‐canonical splice site warnings. Although these may not always have to be corrected (e.g GC donor), they should be flagged with the appropriate comment.

Web Apollo calculates the longest possible open reading frame (ORF) that includes canonical ‘Start’ and ‘Stop’ signals within the predicted exons.

If ‘Start’ appears to be incorrect, modify it selec(ng an in-‐frame ‘Start’ codon further up or downstream, depending on evidence (protein database, addi(onal evidence tracks).

It may be present outside the predicted gene model, within a region supported by another evidence track.

In very rare cases, the actual ‘Start’ codon may be non-‐canonical (non-‐ATG).

50 | 50

‘START’ AND ‘STOP’ SITES


Evidence may support joining two or more different gene models. Warning: protein alignments may have incorrect splice sites and lack non-‐conserved regions!

1.  In ‘User-‐created AnnotaEons’ area shir-‐click to select an intron from each gene model and right click to select the ‘Merge’ op(on from the menu.

2.  Drag suppor(ng evidence tracks over the candidate models to corroborate overlap, or review edge matching and coverage across models.

3.  Check the resul(ng transla(on by querying a protein database e.g. UniProt. Add comments to record that this annota(on is the result of a merge.

51 | 51

Red lines around exons: ‘edge-‐matching’ allows annotators to confirm whether the evidence is in agreement without examining each exon at the base level.

COMPLEX CASES merge two gene predictions on the same scaffold

Becoming Acquainted with Web Apollo. COMPLEX CASES

One or more splits may be recommended when: -‐ different segments of the predicted protein align to two or more different gene families -‐ predicted protein doesn’t align to known proteins over its en(re length

Transcript data may support a split, but first verify whether they are alterna(ve transcripts.

52 | 52

COMPLEX CASES split a gene prediction


DNA Track

‘User-‐created AnnotaEons’ Track

53

COMPLEX CASES correcting frameshifts, single-base errors, and selenocysteines


1.  Web Apollo allows annotators to make single base modifica(ons or frameshirs that are reflected in the sequence and structure of any transcripts overlapping the modifica(on. Note that these manipula(ons do NOT change the underlying genomic sequence.

2.  If you determine that you need to make one of these changes, zoom in to the nucleo(de level and right click over a single nucleo(de on the genomic sequence to access a menu that provides op(ons for crea(ng inser(ons, dele(ons or subs(tu(ons.

3.  The ‘Create Genomic InserEon’ feature will require you to enter the necessary string of nucleo(de residues that will be inserted to the right of the cursor’s current loca(on. The ‘Create Genomic DeleEon’ op(on will require you to enter the length of the dele(on, star(ng with the nucleo(de where the cursor is posi(oned. The ‘Create Genomic SubsEtuEon’ feature asks for the string of nucleo(de residues that will replace the ones on the DNA track.

4.  Once you have entered the modifica(ons, Web Apollo will recalculate the corrected transcript and protein sequences, which will appear when you use the right-‐click menu ‘Get Sequence’ op(on. Since the underlying genomic sequence is reflected in all annota(ons that include the modified region you should alert the curators of your organisms database using the ‘Comments’ sec(on to report the CDS edits.

5.  In special cases such as selenocysteine containing proteins (read-‐throughs), right-‐click over the offending/premature ‘Stop’ signal and choose the ‘Set readthrough stop codon’ op(on from the menu.

54 | 54 Becoming Acquainted with Web Apollo. COMPLEX CASES

COMPLEX CASES correcting frameshifts, single-base errors, and selenocysteines

Follow the checklist un(l you are happy with the annota(on!

And… –  Comment to validate your annota(on, even if you made no changes to an exis(ng model. Think of comments as your vote of confidence.

–  Or add a comment to inform the community of unresolved issues you think this model may have.

55 | 55

Always Remember: Web Apollo cura(on is a community effort so please use comments to communicate the reasons for your

annota(on (your comments will be visible to everyone).

COMPLETING THE ANNOTATION


Checklist

1.  Can you add UTRs (e.g.: via RNA-‐Seq)?

2.  Check exon structures

3.  Check splice sites: most splice sites display these residues …]5’-‐GT/AG-‐3’[…

4.  Check ‘Start’ and ‘Stop’ sites

5.  Check the predicted protein product(s) –  Align it against relevant genes/gene family. –  blastp against NCBI’s RefSeq or nr

6.  If the protein product s(ll does not look correct then check: –  Are there gaps in the genome? – Merge of 2 gene predic(ons on the same scaffold

– Merge of 2 gene predic(ons from different scaffolds

–  Split a gene predic(on –  Frameshies

–  error in the genome assembly? –  Selenocysteines, single-‐base errors, etc

57 | 57

7.  Finalize annota(on by adding: –  Important project informa(on in the form of

comments –  IDs from public databases e.g. GenBank (via

DBXRef), gene symbol(s), common name(s), synonyms, top BLAST hits, orthologs with species names, and everything else you can think of, because you are the expert.

–  Whether your model replaces one or more models from the official gene set (so it can be deleted).

–  The kinds of changes you made to the gene model of interest, if any.

–  Any appropriate func(onal assignments of interest to the community (e.g. via BLAST, RNA-‐Seq data, literature searches, etc.)

THE CHECKLIST for accuracy and integrity

MANUAL ANNOTATION CHECKLIST

Example

Example

Example 59

A public Apollo Demo using the Honey Bee genome is available at h>p://genomearchitect.org/WebApolloDemo

-‐ Demonstra(on using the Hyalella azteca genome (amphipod crustacean).

What do we know about this genome?

•  Currently publicly available data at NCBI: •  >37,000 nucleo(de seqsà scaffolds, mitochondrial genes •  300 amino acid seqsà mitochondrion •  53 ESTs •  0 conserved domains iden(fied •  0 “gene” entries submi>ed

•  Data at i5K Workspace@NAL (annota(on hosted at USDA) -‐ 10,832 scaffolds: 23,288 transcripts: 12,906 proteins

Example 60

PubMed Search: what’s new?

Example 61

PubMed Search: what’s new?

Example 62

“Ten popula(ons (3 cultures, 7 from California water bodies) differed by at least 550-‐fold in sensiEvity to pyrethroids.”

“By sequencing the primary pyrethroid target site, the voltage-‐gated sodium channel (vgsc), we show that point muta(ons and their spread in natural popula(ons were responsible for differences in pyrethroid sensi(vity.”

“The finding that a non-‐target aqua(c species has acquired resistance to pes(cides used only on terrestrial pests is troubling evidence of the impact of chronic pesEcide transport from land-‐based applica(ons into aqua(c systems.”

How many sequences for our gene of interest?

Example 63

•  Para, (voltage-‐gated sodium channel alpha subunit; Nasonia vitripennis).

•  NaCP60E (Sodium channel protein 60 E; D. melanogaster). –  MF: voltage-‐gated ca(on channel ac(vity (IDA, GO:0022843).

–  BP: olfactory behavior (IMP, GO:0042048), sodium ion transmembrane transport (ISS,GO:0035725).

–  CC: voltage-‐gated sodium channel complex (IEA, GO:0001518).

And what do we know about them?

Retrieving sequences for sequence similarity searches.

Example 64

>vgsc-‐Segment3-‐DomainII RVFKLAKSWPTLNLLISIMGKTVGALGNLTFVLCIIIFIFAVMGMQLFGKNYTEKVTKFKWSQDGQMPRWNFVDFFHSFMIVFRVLCGEWIESMWDCMYVGDFSCVPFFLATVVIGNLVVSFMHR

BLAT search

Example 65

>vgsc-‐Segment3-‐DomainII RVFKLAKSWPTLNLLISIMGKTVGALGNLTFVLCIIIFIFAVMGMQLFGKNYTEKVTKFKWSQDGQMPRWNFVDFFHSFMIVFRVLCGEWIESMWDCMYVGDFSCVPFFLATVVIGNLVVSFMHR

BLAT search

Example 66

Customizations: high-scoring segment pairs (hsp) in “BLAST+ Results” track

Example 67

Available Tracks

Example 68

Creating a new gene model: drag and drop

Example 69

•  Apollo automatically calculates ORF. In this case, ORF includes the high-scoring segment pairs (hsp).

Get Sequence

Example 70

http://blast.ncbi.nlm.nih.gov/Blast.cgi

Also, flanking sequences (other gene models) vs. NCBI nr

Example 71

In this case, two gene models upstream, at 5’ end.

BLAST hsps

Review alignments

Example 72

HaztTmpM006234

HaztTmpM006233

HaztTmpM006232

Hypothesis for vgsc gene model

Example 73

Editing: merge the three models

Example 74

Merge by dropping an exon or gene model onto another.

Merge by selec(ng two exons (holding down “Shir”) and using the right click menu.

or…

Editing: correct boundaries, delete exons

Example 75

Modify exon / intron boundary: -‐  Drag the end of the

exon to the nearest canonical splice site.

-‐  Use right-‐click menu.

Delete first exon from M006233

Editing: set translation start

Example 76

Editing: modify boundaries

Example 77

Modify intron / exon boundary also at coord. 78,999.

Finished model

Example 78

Corroborate integrity and accuracy of the model: -‐ Start and Stop -‐ Exon structure and splice sites …]5’-‐GT/AG-‐3’[… -‐ Check the predicted protein product vs. NCBI nr

Information Editor

•  DBXRefs: e.g. NP_001128389.1, N. vitripennis, RefSeq

•  PubMed iden(fier: PMID: 24065824

•  Gene Ontology IDs: GO:0022843, GO:0042048, GO:0035725, GO:0001518.

•  Comments.

•  Name, Symbol.

•  Approve / Delete radio bu>on.

Example 79

Comments (if applicable)

APOLLOdemonstration

Apollo demo video available at: h>ps://youtu.be/VgPtAP_fvxY

DEMO 81

•  Berkeley BioinformaEcs Open-‐source Projects (BBOP), Berkeley Lab: Web Apollo and Gene Ontology teams. Suzanna E. Lewis (PI).

•  § Chris$ne G. Elsik (PI). University of Missouri.

•  * Ian Holmes (PI). University of California Berkeley.

•  Arthropod genomics community: i5K Steering Commi>ee (esp. Sue Brown (Kansas State)), Alexie Papanicolaou (UWS), and the Honey Bee Genome Sequencing Consor(um.

•  Apollo is supported by NIH grants 5R01GM080203 from NIGMS, and 5R01HG004483 from NHGRI; by Contract No. 60-‐8260-‐4-‐005 from the Na(onal Agricultural Library (NAL) at the United States Department of Agriculture (USDA); and by the Director, Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-‐AC02-‐05CH11231.

•  Insect images used with permission: h>p://AlexanderWild.com

•  For your a"enEon, thank you! Thank you. 82

Web Apollo

Nathan Dunn

Colin Diesh §

Deepak Unni §

Gene Ontology

Chris Mungall

Seth Carbon

Heiko Dietze

BBOP

Web Apollo: h>p://GenomeArchitect.org

i5K: h>p://arthropodgenomes.org/wiki/i5K

GO: h>p://GeneOntology.org

Thanks!

NAL at USDA

Monica Poelchau

Christopher Childers

Gary Moore

HGSC at BCM

fringy Richards

Dan Hughes

Kim Worley

JBrowse Eric Yao *