Comparative Genomics. Dept. of Computer Science Comenius University in Bratislava, Slovakia

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1 Comparative Genomics Broňa Brejová Dept. of Computer Science Comenius University in Bratislava, Slovakia 1

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3 Why to sequence so many genomes? 3

4 Comparative genomics Compare genomic sequences of multiple related species find similarities and differences substitutions, indels, genome rearrangements and duplications Explore evolutionary processes neutral mutations vs. positive / negative selection Find functional regions (genes, regulatory regions etc.) often characterized by negative (purifying) selection Look for differences explaining different phenotypes, e.g. human versus other primates domesticated animals/plants vs. wild counterparts pathogenic species vs. free-living relatives adaptations to different environments and diets 4

5 Gene family evolution duplication HISTORY: GENE TREE: speciation A1 A2 A3 B1 B2 B3 speciation duplication loss SPECIES TREE: A1 A2 B1 B2 A3 B3 species 1 species 2 species 3 species 1 species 2 species 3 Homolog: shared evolutionary origin Ortholog: closest common ancestor is a speciation node (e.g.. A1/A3) Paralog: closest common ancestor is a duplication node (e.g. B1/B2, A1/B1, B1/B3, B2/B3) 5

6 Gene tree / species tree reconciliation Given species and gene tree, infer history Favor histories with fewer events (parsimony) Gene tree inferred from gene sequences, may contain errors GENE TREE: HISTORY: A1 A2 A3 B1 B2 B3 SPECIES TREE: species 1 species 2 species 3 A1 A2 B1 B2 A3 B3 species 1 species 2 species 3 6

7 Global view of gene family evolution Do not infer history for each family instead assume a global evolutionary model of family size change Find genes in genomes Assign them to families (by sequence similarity) Summarize counts for each family and each species Infer a species tree Infer overall rateλof gene gain/loss E.g. in yeasts gains and losses/gene/million years Look for families with significantly accelerated evolution [Hahn et al. 2005] 7

8 Stochastic model of gene family evolution Simplified view of how evolution might operate Imagine generating simulated data Birth and death process for gene families species 1 species 2 species 3 8

9 Stochastic model of gene family evolution Each gene can duplicate or be lost at a rateλ For a short timetwe expect2λtn events in ann-gene family For longertsome genes can be affected multiple times P(X t = c X 0 = s) = min(s,c) j=0 whereα = λt/(1+λt) Using these formulas, we can compute ( s j)( s+c j+1 s 1 probability of a history 1 3 We can also compute probability (likelihood) ) α s+c 2j (1 2α) j of observing counts in the current species We can find value ofλmaximizing probability over all gene families

10 Results on five yeast genomes [Hahn et al. 2005] Rate λ of gene gain/loss is gains and losses/gene/million years 1254 out of 3517 gene families some change in size Look for families with significantly accelerated evolution Stress response family: S.cer. S.par. S.mik. S.kud. S.bay. 10

11 Whole-genome alignments For each region of a reference genome (e.g. human) find and align corresponding parts from other genomes Human AGTGGCTGCCAGGCTG---GGATGCTGAGGCCTTGTTTGCAGGGAGGT Rhesus AGTGGCTGCCAGGCTG---GGTTGCTGAGGCCTTGTTTGCCGGGAGGT Mouse GGTGGCTGCCGGGCTG---GGTGGCTGAGGCCTTGTTGGTGGGGTGGT Dog AGTGGCTGCCCGGCTG---GGTGGCTGAGGCCTTATTTGCAGGGAGGT Horse GATGGCTGCCGGGCTG---GGCTGCCGAGGCCTTGTTCGTGGGGAGGT Armadillo AGTGGCTGCCGGGCTG---GGAGGCCAAGGCCTTGTTCGCGGGCAGGT Chicken AGTGGCTGCCAGTCTGCGCCGTGGCCGACGTCTTGCTCGGGGGAAGGT X. tropicalis AATGGCTTCCATTTTGTGCCGCTGCTGAGGTCTTGTTCTGGGGAAGAT 11

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Nets and chains from the UCSC genome browser For each region of a reference genome (e.g. human) find and align corresponding parts from other genomes (e.g. mouse, dog, chicken, etc.

13 Nets and chains from the UCSC genome browser For each region of a reference genome (e.g. human) find and align corresponding parts from other genomes (e.g. mouse, dog, chicken, etc.) Local alignments align exons and other conserved elements, but many parts of genomes have changed too much For duplicated regions decide which pairs are orthologs This can be done using synteny: a chain of local alignments in the same order and orientation in the two genomes 13

14 Nets and chains from the UCSC genome browser Start with local alignments Connect them to chains, where we allow big indels and unaligned regions, but require the same order and orientation in both genomes Selects some chains to form a hierarchical net: choose chains with highest score that do not overlap in reference We can use only parts of some chains Parts of non-reference genome can be used more than once (if reference duplicated) 14

15 Scale chr13: chr k Level 1 Level 2 Level 3 Level 4 Level 5 Level 6 2 kb Mouse Chained Alignments chr k chr k chr k chr k chr k Mouse Alignment Net 15

16 Negative (purifying) selection Important parts of a genome accumulate mutations more slowly Find conserved elements in genomes Many correspond to known functional elements (genes, regulation) Conserved elements not overlapping these are interesting targets for future research SC Genes Based on RefSeq, UniProt, GenBank, CCDS and Comparative Genom UCSC Genes 1 _ Placental Mammal Conservation by PhastCons Mammal Cons 0 _ Multiz Alignments of 46 Vertebrates Gaps 2 Human C A AGA CGAGA C AGG T A A A T C T C A T GAGC T T T A T T C T A T A T T T Chimp C A AGA CGAGA C AGG T A A A T C T C A T GAGC T T T A T T C T A T A T T T Mouse C A AGGCGGGA C AGG T GAGCC T CC T GCGC T GCGC T C T C T GC T T Dog C A AGGCGAGA C AGG T A A AGC T C A T GAGA T T T A T T C T A T A T T T Chicken C A AGGCGAGA C AGG T A A T T C T T A T GAGA T T T CGA C T G T A C T T 16

17 Substitution models Jukes-Cantor model: basexmutates to some other basey at rateα(rate the same for allx Y) Probability of change fromatocover timet: Pr(X t = C X 0 = A) = 1 4 (1 e 4 3 αt ) Includes possibility of multiple mutations C We can compute probability C of a history A A C We can also compute likelihood given only current sequences A A C We can estimate bestαfor a given alignment 17

18 More complex substitution models Jukes-Cantor model assumes each mutation equally likely In general, substitution rateµ xy from basexto basey Substitution rate matrix µ A µ AC µ AG µ AT µ CA µ C µ CG µ CT µ GA µ GC µ G µ GT µ TA µ TC µ TG µ T Pr(X t = C X 0 = A) does not have in general closed formula but can be computed by algebraic methods Equilibrium frequenciesπ A,π C,π G,π T stay stable in the model 18

19 HKY model [Hasegawa, Kishino a Yano 1985] A lower number of parameters µ A βπ C απ G βπ T βπ A µ C βπ G απ T απ A βπ C µ G βπ T βπ A απ C βπ G µ T Transition rateα:c T,A G Transversion rateβ:{c,t} {A,G} Five parameters:π A,π C,π G,α,κ = α/β 19

20 Back to conserved elements Basic idea: infer two ratesα c for slow evolving andα n for fast evolving alignment columns For each column try to determine, which rate more likely But: one column may look conserved purely by chance (not enough information) Combine information from a short window or use a phylogenetic hidden Markov model (phylohmm) 20

21 PhastCons: detection of conserved elements using phylohmms 21

22 PhastCons results Whole-genome alignments of human, mouse, chicken and fugu 22

23 Conserved elements in 29 mammals [Lindblad-Toh et al. 2011] Four binding sites of NRSF transcription factor 23

24 Comparative gene finding Improve genome annotation using whole-genome alignments Look for specific signatures typical for genes (synonymous substitutions, indels preserving reading frame) Lin et al

25 Comparative gene finding Comparative genomics also helps to find special cases e.g. stop codon readthrough selenoproteins (UGA to selenocysteine) RNA editing (adenine na inosine) Lin et al

26 Human Accelerated Regions [Pollard et al 2006] We are looking for genomic regions which: were mutating slowly for a long time (negative selection) in human lineage they change very fast (positive selection) Details: Consider regions of length 100 with 96% sequence identity between chimpanzee and mouse/rat (35,000) Compare with other mammals, select those that have many mutations in human and few elsewhere Probabilistic model which allows scaling of human branch 49 statistically significant regions, 96% of them non-coding 26

Human Accelerated Regions: HAR1 Region of length 118 bases 300 mil.

years Clovek C T G A A A T G A T G G G C G T A G A C G C A C G T C A G C G G C G G A A A T G G T T T C T A T Simpanz C T G A

T A G A C A C A T G T C A G C A G T G G A A A T A G T T T C T A T Rezus C T G A A A T T A T A G G T G T A G A C A C A T G T

T G G T T T C T A T Krava C T G A A A T T A T A G G T G T A G A C A C A T G T C A G C A G T G G A A A C C G T T T C T A T

27 Human Accelerated Regions: HAR1 Region of length 118 bases 300 mil. years 18 changes between human and chimpanzee medzi 2 changes between chimpanzee and chicken 6 mil. years Clovek C T G A A A T G A T G G G C G T A G A C G C A C G T C A G C G G C G G A A A T G G T T T C T A T Simpanz C T G A A A T T A T A G G T G T A G A C A C A T G T C A G C A G T G G A A A T A G T T T C T A T Gorila C T G A A A T T A T A G G T G T A G A C A C A T G T C A G C A G T G G A A A T A G T T T C T A T Rezus C T G A A A T T A T A G G T G T A G A C A C A T G T C A G C A G T G G A A A T A G T T T C T A T Mys C T G A A A T T A T A G G T G T A G A C A C A T G T C A G C C G T G G A A A T G G T T T C T A T Krava C T G A A A T T A T A G G T G T A G A C A C A T G T C A G C A G T G G A A A C C G T T T C T A T Pes C T G A A A T T A T A G G T G T A G A C A C A T G T C A G C G G T G C A A A C A G T T T C T A T Sliepka C T G A A A T T A T A G G T G T A G A C A C A T G T C A G C A G T A G A A A C A G T T T C T A T 27

28 What is the function of HAR1? Overlaps RNA genes HAR1R and HAR1F HAR1F is expressed in neocortex in 7 and 9 week old embryos, later also in other parts of the brain (in human and other primates) 28

29 What is the function of HAR1? Mutations change RNA structure 29

30 Functional enrichment Results of whole-genome studies often in the form of a list of significant genes In comparative genomics e.g. families with accelerated gain and loss, human accelerated regions, genes under positive selection etc. Also from other studes, e.g. differential expression analysis How to use such lists? look manually at the most significant candidates try to find common characteristics of the whole set 30

31 Gene ontology Hierarchical structure of biological terms describing functions of genes GO: biological process GO: localization GO: establishment of localization GO: transport GO: ion transport GO: ion transmembrane transport Databases contain gene ontology terms for many proteins Is some function enriched in our gene set? 31

32 Example [Kosiol et al 2007] n = genes overall n i = 70 genes with innate immune response term (0.4% of all genes) n p = 400 genes with positive selection overall n ip = 8 of them innate immune response (2% of genes with pos.sel.) Contingency table Pos.sel. No pos.sel. Total Immunity 8 (n ip ) (n i ) Other Total 400 (n p ) (n) 32

33 Null hypothesis Genes in our list were randomly selected from all genes Whole genome hasn i /n = 0.4% immunity genes Our list should contain aboutn p (n i /n) immunity genes We expect 1.7 genes, get 8 genes But purely by chance the number can be larger or smaller Urn withn i white andn n i black balls Randomly selectn p balls, how many are white?x ip Hypergeometric distribution Pr(X ip = n ip ) = ( ni n ip )( n ni n p n ip ) ( ) n / n p P-value:P(X ip 8) =

34 Our research: ancestral gene orders in mitochondrial genomes [Valach et al. NAR, 2011, Kovac, Brejova, Vinar WABI 2011] 4 0 (0-1) 2 (0-2) 1 (1-3) 5 (4-5) 2 (0-2) 4 (3-4) 0 (0-1) C. parapsilosis 2 (1-3) 1 (0-1) C. orthopsilosis 0 0 (0-1) C. orthopsilosis 1 (0-1) C. jiufengensis 9 (8-9) L. elongisporus 2 (0-2) C. tropicalis 2 (2-3) 5 (5-7) C. sojae 0 1 (0-1) C. viswanathii 2 C. frijolesensis 1 0 C. neerlandica 5 (5-6) C. albicans 11 (11-12) C. maltosa 5 (4-5) C. alai 2 (2-3) 3 C. subhashii 5 (4-5) D. hansenii 3 (3-4) P. sorbitophila nad3 nad2 cob cox2 rnl cox1 nad4 rns atp9 nad6 nad1 cox3 nad4l nad5 atp8 atp6 nad3 nad2 cob cox2 rnl cox1 nad4 rns atp9 nad6 nad1 cox3 nad4l nad5 atp8 atp6 nad3 nad2 cob cox2 rnl cox1 nad4 rns atp9 nad6 nad1 cox3 nad4l nad5 atp8 atp6 nad3 nad2 cob cox2 rnl cox1 nad4 rns atp9 nad6 nad1 cox3 nad4l nad5 atp8 atp6 nad3 nad2 cob cox2 rnl cox1 nad4 rns atp9 nad6 nad1 cox3 nad4l nad5 atp8 atp6 nad3 nad2 cob atp9 rns nad4 cox1 rnl cox2 nad6 nad1 cox3 nad4l nad5 atp8 atp6 cox1 nad4 rns atp9 cob nad2 nad3 rnl cox2 nad6 nad1 cox3 nad4l nad5 atp8 atp6 nad3 nad2 cob atp9 rns nad4 cox1 rnl cox2 nad6 nad1 cox3 nad4l nad5 atp8 atp6 cob rnl cox2 nad6 nad1 nad2 nad3 nad4 rns atp9 cox1 atp8 atp6 cox3 nad5 nad4l nad3 nad2 cob atp9 rns nad4 cox1 rnl cox2 nad6 nad1 cox3 atp8 atp6 nad5 nad4l rns atp9 nad2 nad3 cob cox1 rnl cox2 nad6 nad1 cox3 atp6 atp8 nad4 nad4l nad5 nad3 nad2 atp9 rns cob cox3 nad1 nad6 cox2 rnl cox1 nad5 nad4l nad4 atp8 atp6 cox1 nad3 nad2 atp9 rns cob cox2 rnl nad6 nad1 cox2 cob rns atp9 nad2 nad3 nad4 cox3 nad3 nad2 atp9 rns cob cox1 rnl cox2 nad6 nad1 cox3 atp6 atp8 nad4 nad4l nad5 cox3 cox1 cob rns atp9 nad2 nad3 nad5 nad4l nad4 atp8 atp6 rnl cox2 nad6 nad1 nad3 nad2 atp9 rns cob cox1 rnl cox2 nad6 nad1 cox3 atp6 atp8 nad4 nad4l nad5 rnl cox2 nad6 nad1 cox3 cox1 cob rns atp9 nad2 nad3 nad5 nad4l nad4 atp8 atp6 nad3 nad2 atp9 rns cob cox1 rnl cox2 nad6 nad1 cox3 atp6 atp8 nad4 nad4l nad5 nad3 nad2 atp9 rns cob cox1 rnl cox2 nad6 nad1 cox3 atp6 atp8 nad4 nad4l nad5 cox1 cob rns atp9 nad2 nad3 cox3 rnl cox2 nad6 nad1 nad4l nad5 nad4 atp6 atp8 cox1 nad2 nad3 cob rns nad4l nad5 nad4 cox3 rnl cox2 nad6 nad1 cox3 atp9 atp6 atp8 cox1 nad4 cob rns atp9 nad2 nad3 cox3 rnl cox2 nad6 nad1 nad4l nad5 atp6 atp8 nad2 nad3 cox1 nad4 nad1 nad6 atp6 atp8 cox2 rnl cob nad5 nad4l rns atp9 cox3 cox2 rnl cox1 nad4 rns atp9 cob nad2 nad3 atp8 atp6 nad5 nad4l cox3 nad1 nad6 cob nad1 nad6 cox2 nad4 rnl cox3 cox1 atp6 atp8 atp9 rns nad4l nad5 nad2 nad3 atp8 a nad3 nad2 nad5 nad4l rnl cox2 nad6 nad1 cox3 cox1 nad4 rns atp9 cob atp6 atp8 cob atp9 rns nad4 nad1 nad6 cox2 rnl nad4l nad5 cox1 cox3 nad2 nad3 atp8 atp6 cox3 cob nad3 nad2 nad1 nad6 cox2 rnl rns nad4 cox1 atp9 atp8 atp6 nad4l nad5 rns nad4 cox1 rnl cox2 nad6 nad1 atp9 atp8 atp6 nad2 nad3 cob cox3 nad4l nad5 nad2 nad3 nad4 rns cob cox3 nad4l nad5 cox1 rnl cox2 nad6 nad1 atp9 atp8 atp6 34

35 Our research: history inference for duplicated gene clusters [Vinar, Brejova, Song, Siepel. JCB 2010] human IFN cluster, chr 9 35

36 Conclusion Comparative genomics can help us annotate genomes and study their evolution Evolution typically characterized by stochastic models We can estimate parameters of these models from data to study typical patterns of evolution We can also detect atypical elements Gene family evolution, conserved elements, accelerated elements Next: positive selection in protein coding genes 36

Lecture 4: Evolutionary Models and Substitution Matrices (PAM and BLOSUM)

Bioinformatics II Probability and Statistics Universität Zürich and ETH Zürich Spring Semester 2009 Lecture 4: Evolutionary Models and Substitution Matrices (PAM and BLOSUM) Dr Fraser Daly adapted from