Genome Sequence Informatics&

Comparative GenomeSequence Analysis

Niclas JareborgAstraZeneca R&D Södertälje

Genome sequencing projects• Aim: Better understanding of biology• Bioinformatics

• Manage data• Cut corners• Generate and test new hypotheses

• Make the most of the data• comparative analysis

gttaaaattcagcaggcagaatgaaaataaatgtcaataattttttattttaaaatattcatgttttactattttgatataatttttaaagaaaaaggcagaaaccactgcttattagaaggcagattttattgattttatacccctagacttgttgcatatcaaacctatgtaaaaacatctataaatcaaatcattaattgcacctagtataataattctatatatggaggtaatgtttgattcttcaggagctttaataacttgaagcccgtttgattgctttaaaatgatttctcattgtatttgtttatattgtatcattaagcaaaagtacagagtaagcaattagtgtgattaattcctcttccataatacagtaaagcactgcctccatagaccaattctctgggatccctggaaaacatctggcatccagcaagtcttgacccctctttagaaagccatggagaaactggaggcaattctgttaattatttgccctctagaggcaattgggttaattaccctcccttccctatccatgacacaatttctccagttacatgtagaatgctgttatgtgtctcctgaccagaccccttatttcatagatgtggaaactgaggccatgaaggatgaggtgactgttcacaatccacatggctagttagtgtccagagcctggcctggacttctctcttgttctggggccttgagttctctccctcttctttagtacatatggccacaggtaacgtaatctgcgtaccacatttgcatttggagtgcatctgttttgcattcatttaatcttgttgagatggtttgcttgttgacctactcagtcagttatcttttcacctttgtgagttgagagctttgtgtattaaatctgtaaaactttgcatcgtggaaagtgacataatctgtagcagacccatgctgtttttagatgcatcttcattgtggtagtgacagtgattgagaaactttacat

Where are the functional elements?Where are the functional elements?

Features in genome sequences• Genes

• Exons, introns, promoters• RNA genes• CpG islands• Enhancers• Other functional elements

• e.g. Replication origins, Nuclear matrix association

• Repeats

How to find genomic features• Repeats, CpG islands, RNA gene

• Bioinformatics programs• Genes

• Homology to known sequences• Bioinformatics prediction programs

• Transcription regulatory regions• Bioinformatics prediction programs

Finding genes by homology• Database searches – BLAST, BLAT, SSAHA

• EST and cDNA sequences• Protein sequencesHigh accuracy, misses unknown sequences

• caveat: junk EST sequences

Genewise (Birney & Durbin)Alignment of DNA to protein (or HMM) allowing for Alignment of DNA to protein (or HMM) allowing for splicingsplicing

Uses dynamic programming with extra states for intronsUses dynamic programming with extra states for introns

pkinase.hmm 1 YELGEKLGEGA GKVYKAKHK---TGKIVAVKILKKESLSLL REIQI ++ LG + G+ Y+A + ++I+ + +K + + + E+ + INIKNLLGGDT GCLYMAPKVQATKQQIYKLCFIKIKTFVLQ TELNLHSU71B4 -27753 aaaaactgggaGTGTGAGTA Intron 1 CAGTgtttagcagcgaaccatatttaaaaatgccAGGTCACTA Intron 2 CAGGagcac tataattggac <2-----[27718:22469]-2> ggtatccataccaaataatgttatacttta <2-----[22375:21185]-2> catat atcatggtata acatgaaaaaaaaaattagcctaaattgta tacct

pkinase.hmm 45 LKRLN-HPNIVRLLGVFED-----SKDHLY LVLEYMEGGDLFDYLRRKG--PLSEKEAKKIALQILR L++++ H+NIV ++G+F L+ +V+E++ G+ D++R+ L E+++ +I ++IL+ LRKYSFHKNIVSFYGAFFKLSPPGQRHQLW MVMELCAAGSVTDVVRMTSNQSLKEDWIAYICREILQHSU71B4 -21168 caatttcaaagtttggttacaccgccccctGTATGTT Intron 3 CAGagagttgggtgagggaaaaacataggtagtatcgacc tgaactaaattctagcttatgccgagaatg<0-----[21078:15667]-0>tttatgccgctcattgtcgaagtaaagtcatggatta gggctccactgcctaatcggtcttggcatg ggggataatgcttagagcttgtaaatgtttccaactg

pkinase.hmm 104 GLEYLHSNGIVHRDLKPENILLDENGTVKI DFGLAKLLK-SGEKLTTFV GL++LH ++++HRD+K +N+LL++N VK+ DFG++++++ ++++++F+ GLAHLHAHRVIHRDIKGQNVLLTHNAEVKL DFGVSAQVSRTNGRRNSFIHSU71B4 -15555 GTGAGTC Intron 4 CAGgtgcccgccgaccgaagcagccacagggacGGTAAGTT Intron 5 CAGTTgtggagcgaaaagaaaata <0-----[15555:14066]-0>gtcatacagttagatagaatttcaacatat <1-----[13974:10915]-1> atgtgcatggcagggagtt catctcacaatcgccatgtgggttttaaag ttagtcggcattaagttct

pkinase.hmm 153 GTPWYMMAPEVILKG-----RGYSTK VDVWSLGVILYELLTGKL FPG-D GTP++M APEV + R Y+ + +DVWS+G++ +E++ G + + GTPYWM-APEV-IDCDEDPRRSYDYR SDVWSVGITAIEMAEGAP LCNLQHSU71B4 -10855 gactta gcgg agtgggcacttgtaGTGAGTG Intron 6 CAGaggttggaagagaggggcCGTGAGTA Intron 7 CAGCTctacc gccagt ccat tagaaacggcaaag<0-----[10783: 8881]-0>gatgctgtcctatcagcc <1-----[8825 : 4234]-1> tgata gaacgg atgg tcttgcaaccttca ttggtgattctagtaact gtcta

pkinase.hmm 196 PLEELFRIKKRLRLPLPPNC SEELKDLLKKCLNKDPSKRPTAKELLEHPW PLE+LF I+++ ++ + ++ S+ + +++KC K+ RPT +L+HP+ PLEALFVILRESAPTVKSSG SRKFHNFMEKCTIKNFLFRPTSANMLQHPFHSU71B4 -4214 ctggctgatcgtgcagatagTGGTAAAGA Intron 8 TAGGtcatcatagataaaatctccatgaacccct ctactttttgacccctacgg <2-----[4154 : 3085]-2> cgataattaagctaatttgccccattaact cgatccttggattcacacca ctgcctcgagtgaatcgtttttacgtacat

+3bp - 6bp +12bp

-20bp

- 8bp- 66bp - 1bp

0bp - 3bp -1 bp

+1bp

+2bp

+1bp

Gene prediction methods• ATGs• Stop codons• ORFs• Coding preference• Splice sites

• profiles, statistical methods, neural networks etc.

High coverage, low accuracy

easy

hard

Accuracy of gene-finding programs for 1.4 MB genomic region BRCA2 on humanchromosome 13q

Region includes 159 true exons exact match overlap exons 5'- splice site 3'- splice site NE N acc cov N acc cov N acc cov N acc covfgenesh.masked 169 110 0.65 0.69 125 0.74 0.79 118 0.70 0.74 116 0.69 0.73fgenesh 190 109 0.57 0.69 126 0.66 0.79 117 0.62 0.74 117 0.62 0.74fgenes.masked 238 103 0.43 0.65 132 0.55 0.83 114 0.48 0.72 118 0.50 0.74fgenes 281 104 0.37 0.65 136 0.48 0.86 116 0.41 0.73 120 0.43 0.75genscan 292 105 0.36 0.66 129 0.44 0.81 116 0.40 0.73 115 0.39 0.72fgeneh 381 68 0.18 0.43 101 0.27 0.64 79 0.21 0.50 87 0.23 0.55mzef 623 95 0.15 0.60 122 0.20 0.77 106 0.17 0.67 107 0.17 0.67

fgeneshm+genescan 118 97 0.82 0.61 106 0.90 0.67 101 0.86 0.64 101 0.86 0.64fgeneshm+fgenes 89 83 0.93 0.52 86 0.97 0.54 86 0.97 0.54 83 0.93 0.52

acc - specificity (true predicted/all predicted) cov - sensitivity (true predicted/true)NE - number of predicted exons

data provided by Tim Hubbard and Richard Bruskiewich (Sanger Centre)

Repetitive elements• 1/3 of the human genome • Transposable elements

• LINEs (Long Interspersed Nuclear Elements), 6-8 kb • SINEs (Short Interspersed Nuclear Elements, e.g. Alu), 100-

400 bp • Retrovirus-like elements, 1.5-10 kb (LTRs 300-1000 bp) • DNA transposons, 80 bp-3 kb

• Tandem repeats• Simple repeats/Microsatellites (1-5bp)n, e.g. caacaacaa • Minisatellites (6-1000s bp)n

• Low complexity regions

Repeat masking• Repeats disturb analysis

• Homology searching • Gene prediction

• Masking exchange repeat region with N's. Will be ignored by analysis programs

• RepeatMasker (Smit & Green)• LINEs, SINEs, LTR transposons, DNA transposons,

Simple repeats, Low complexity regions• trf (Benson)

• Tandem repeats

Predicting regulatory regions• Transcription Factor Binding Sites (TFBSs)

have very low information content• Given a long enough sequence a binding site

will be predicted• Combination of TFBSs• Even the best algorithms will overpredict

CpG islands• Associated with transcribed genes

• House keeping genes + ~50% of other genes• Often in 5' ends of genes

• >200 bp• GC content >50% • obs/exp CpG >0.6

Gene Ontology

• “Controlled vocabulary that can be applied to all organisms even as knowledge of gene and protein roles in cells is accumulating and changing.”

“Biologists would rather share a toothbrush than a gene name”- Michael Ashburner

Gene Ontology• Organizing principles

• Molecular function• Biological process• Cellular component

• Hierarchical structure

Genome resources• Genome sequence centered

• Ensembl• http://www.ensembl.org

• NCBI• http://www.ncbi.nlm.nih.gov

• UCSC Human genome browser • http://genome.ucsc.edu

• All based on NCBI assembly• Gene centered

• SOURCE• http://source.stanford.edu

• GeneLynx • http://www.genelynx.org

• GeneCards• http://bioinformatics.weizmann.ac.il/cards/

Ensembl

Ensembl – Map view

Ensembl – Contig view

Ensembl – Contig view

Ensembl – Gene view

Ensembl – Gene view

Ensembl – Gene view

NCBI Genome resources

NCBI Map View

NCBI Locus Link

NCBI Sequence view

UCSC – Genome browser

UCSC – Genome browser

UCSC – Genome browser

Gene-centered resources

• Genomic resources• Transcripts• Protein sequences• Protein structure and

domains• Protein function and

disease links• Homologs• Functional/GO

classifications• Physical clones• etc

Comparative Genomic Sequence Analysis

• Aid in finding functional regions• Coding regions• Regulatory regions

Comparative Genomic Sequence Analysis

• Compare corresponding genomic sequences from different species

• Potential protein coding and/or regulatory regions can be identified by their conservation

• “Phylogenetic footprinting”

Why it works

Synteny maps• Maps corresponding regions in different genomes• Large-scale relationships• Based on

• genetics• sequence

• Available for • Human vs.

• Mouse• Rat• Dog• Chimp• etc…

• Mouse vs Rat

Ensembl synteny views

• Protein sequence based

NCBI comparative maps• Based on

genetics• Several genetic

maps

Human/vertebrate sequence comparisons (80-450 Myrs)

• Coding sequences generally well conserved• Non-coding regions show highly variable levels

of conservation• Conservation of non-coding regions imply a

functional role• promoters• other transcriptional regulators• replication origins• chromatin condensation• matrix association

Model organisms for vertebrate comparative analysis

• Not too evolutionary close• Impossible to identify functional regions through

conservation• Mouse 3000 Mb 80 Myrs

Genetics Sequence ”finished”

• Chicken 1200 Mb 300 Myrs Micro-chromosomes (~75% of genes) Prioritized for sequencing

• Fugu (Puffer fish) 400 Mb 450 Myrs Small genome, shorter introns and intergenic regions More or less the same gene content as higher vertebrates Sequence finished

What are we comparing?• Homologue

• common ancestor, may have similar function • Orthologue

• the “same” sequence, generated by a speciation event, probably same function

• Paralogue• similar sequence within species, generated by

a gene duplication event, may have similar function

Globins (I)

Globins (II)

Finding conserved regions• Dot plot

• Dotter• Similarity search programs

• Blast• Alignment programs

• DBA (Jareborg et al)• blastz (Schwartz et al.)• Dialign (Morgenstern et al.)• WABA (Kent & Zahler)• Avid (Bray et al.)• others

Dotter (Sonnhammer & Durbin)

• Graphical dot plot program for detailed comparison of two sequences

• Features • dynamic greyscale ramp for stringency cut-off• alignment viewer• zooming.

• Unix & Windows• http://www.cgb.ki.se/cgb/groups/sonnhammer/Dotter.html

DBA (Jareborg, Birney & Durbin)

• DNA Block Aligner• Finds co-linear blocks with high similarity• Does not try to align the sequences

between these blocks• Divides blocks into four different

categories• approx. 60-70%, 70-80%, 80-90%, 90-

100%

Comparison-based functional prediction • Gene prediction• Regulatory region predictions

”Comparative” gene prediction programs• Twinscan• Doublescan• SGP-1

http://genes.cs.wustl.edu/

http://www.sanger.ac.uk/Software/analysis/doublescan/

http://195.37.47.237/sgp-1

Regulatory region prediction• Consite

• Detection of TFBS conserved in corresponding genomic sequences from different species

www.phylofoot.org/consite

ConSite

Visualisation• Easier to grasp large data volumes• Programs

• Dot plot (e.g. Dotter)• PIP• Alfresco• VISTA

• Genome comparative resources• VISTA genome browser• UCSC• Ensembl

PIP - Percent Identity Plot

Oeltjen et al. (1997)Genome Research 7:315

Alfresco

• Over-all control of comparative analysis• Display and summarize results from external

analysis programs

Tool for comparative genome sequence analysisTool for comparative genome sequence analysis

Jareborg & Durbin Genome Research 10:1148–1157

Alfresco FeaturesAlfresco Features• Interactive graphical interfaceInteractive graphical interface• Uses external programs for analysisUses external programs for analysis

• Dotter - interactive dotplot programDotter - interactive dotplot program• Blastn alignments - finds conserved blocksBlastn alignments - finds conserved blocks• DBA - detects and aligns conserved blocksDBA - detects and aligns conserved blocks• Cpg - detects CpG islandsCpg - detects CpG islands• RepeatMasker - identifies repeatsRepeatMasker - identifies repeats• Genscan - gene predictionGenscan - gene prediction• GeneWise - gene prediction using homologous protein GeneWise - gene prediction using homologous protein

sequence sequence • est_genome - gene prediction using homologous RNA est_genome - gene prediction using homologous RNA

sequencesequence

Alfresco

Vista Genome Browser• Human – Mouse - Rat comparisons• VISTA viewer• http://pipeline.lbl.gov/

VISTA genome browser

UCSC Genome browser

• Human - Mouse• Twinscan

predictions• Conservation

profiles• Quantitative

Ensembl contig viewer

• Human-Mouse match locations

• Qualitative

• Twinscan predictions

• Move between Human and Mouse contig views

Comparative Analysis Examples• Interspecies non-coding regions conservation• Coding region predictions• Regulatory region predictions

Comparative Analysis of Noncoding Regions of 77 Mouse and Human Gene PairsJareborg, Birney, and Durbin.(1999)

Genome Research 9:815

• How conserved are non-coding regions between mouse and human?

• Measure of conservation?• % identity• fraction conserved

A “typical” intron

mouse/human data set• Genomic sequences from the EMBL database

containing 78 pairs of mouse-human orthologous genes

• Features as defined in feature tables• Corresponding features aligned with DBA:

• Fraction covered by blocks >60 % identical:• Upstream regions: 36 %• 5’ UTRs: 49 %• Introns: 23 %• 3’ UTRs: 56 %

• Sizes:• 20 - 700 bp

Jareborg, Birney & Durbin. Genome Research 9:815-824

Analysis example - coding region predictionUTY

Analysis example - cont.

Analysis example - Regulatory regions

• BTK - Bruton’s Tyrosine Kinase• agammaglobulinemia• Expression

• early stages of B-cell differentiation• myeloid cell lines• not in T cells

BTK region PIP

Oeltjen et al. (1997)Genome Research 7:315

Alfresco - BTK 5’end

Promoter constructs

mye

loid

B-c

ell

T-ce

ll

Oeltjen et al. (1997)Genome Research 7:315

2.5 kb conserved region in first intron contributes to cell-lineage specific expression

Comparative AnalysisIssues for the future

• Faster/better algorithms for aligning vertebrate genomes

• Multiple alignments• Comparing several species can give clues to which

regulatory sequences are of a basic nature, and which are lineage specific

• Cataloguing of comparative data• Better visualisation

• Whole syntenic region <> nucleotide level• Multiple genome sequences

Future Issues - cont.• Genome evolution

• macro scale• molecular evolutionary rates• repeats

• Transcriptional regulatory regions• definition/modelling

• identification of combinations of conserved TFBSs coupled with gene expression data

• prediction

Fin