1. Genomic and comparative genomic analysis BIO520 BioinformaticsJim Lund

2. Comparative genomics delivers Clues as to human disease genes and evolutionary history Evidence of general trends in genome evolution Previously unknown regulatory strategies “Natural history”of species as apparent in genome records Surprises

3. Difference is in Scale and Direction One or several genes compared against all other known genes. Use genome to inform us about the entire organism. Use information from many genomes to learn more about the individual genes. Entire Genome compared to other entire genomes. Other “omics” Comparative

4. What are some questions that comparative genomics can address? How has the organism evolved? What differentiates species? Which non-coding regions are important? Which genes are required for organisms to survive in a certain environment? (prokaryotes)

5. Genomic characteristics observed in recently diverged species Time (My) F AB D E C 0 -10 -80 -150 -200 Organism-specific differences in gene regulation more apparent than difference in genome sequence or structure Relatively small amount of neutral drift Apparent positive selection Some chromosomal rearrangement Minimal species-specific gene innovation

6. Genomic characteristics observed in species that have diverged ~80MYA Time (My) F AB D E C 0 -10 -80 -150 -200 Chromosomal re-arrangements dominate organizational change. Changes in chromosome number likely. Conservation of synteny regions within rearrangements. High conservation features indicate purifying selection against drift background, therefore important genomic features in common. Protein domain arrangements largely conserved among orthologs. Species-specific gene duplication, divergence, and/or loss.

7. Genome structure has no resolvable large or small-scale homology. Cis-regulatory regions do not correspond. Greatest conservation at the functional level in some protein domains and functional RNA. Different strategies in gene organization and regulation. Apparent homology in shared-ancestral systems, such as energy processing and storage. Time (My) AE F G 0 -500 -1000 Genomic characteristics observed between species that have diverged ~1BYA

8. Different Questions Require Different Comparisons From: Hardison. Plos Biology. Vol 1 (2): 156-160

9. What is compared? Gene location Gene structure –Exon number –Exon lengths –Intron lengths –Sequence similarity Gene characteristics –Splice sites –Codon usage –Conserved synteny

10. From: Miller et al. Annu. Rev. Genom. Human. Genet. 2004.5:15-56. Millions of years

11. t Early globin gene Alpha chain Frog alpha Human alpha Human Beta Frog beta Beta chain First duplication event Second duplication event (speciation) 0 1 23 Orthologues Paralogues Reminder: Orthologues & Paralogues

12. Figure 1 Regions of the human and mouse homologous genes: Coding exons (white), noncoding exons (gray}, introns (dark gray), and intergenic regions (black). Corresponding strong (white) and weak (gray) alignment regions of GLASS are shown connected with arrows. Dark lines connecting the alignment regions denote very weak or no alignment. The predicted coding regions of ROSETTA in human, and the corresponding regions in mouse, are shown (white) between the genes and the alignment regions.

13. Example Functional elements: Gene regulation? Chromatin structure?

14. Terminologies (Cont’d) –Synteny Two or more genes that are located in the same chromosome. Relevant within a species. –Conserved synteny Orthologs of genes that are syntenic in one species are also located on a single chromosome in a second species. Gene order is irrelevant. –Conserved segments/linkages In a segment of DNA, the order of multiple orthologous genes is the same in two species.

15. Image credit: U.S. Department of Energy Human Genome Program From: http://www.macdevcenter.com/pub/a/mac/2004/06/29/bioinformatics.html

16. Q: Why do gene pairs in syntenic regions have more significant E scores?

17. VISTA A genomic alignment and visualization program http://genome.lbl.gov/vista/index.shtml VISTA automatically finds an orthologue for your input sequence and performs a VISTA similarity plot Example: Rat BAC: gj (AC097115) For alignment, uses the AVID or LAGAN programs Quickly aligns 100’s of kb Can handle sequence in draft format Uses HMM-like algorithm to find strong anchors from a collection of maximal matches Uses VISTA browser – sequence alignment visualization tool Allows easy visualization of areas with high similarit.y Visualization is scalable – allows you to zoom in/out.

20. Gene: CARP – cardiac ankyrin repeat protein

21. There are many genomic alignment and visualization tools: BLASTZ/PipMaker : http://bio.cse.psu.edu/ AVID/VISTA: http://www-gsd.lbl.gov/vista/ LAGAN/Multi-LAGAN: http://lagan.stanford.edu AVID: http://baboon.math.berkeley.edu/mAVIDhttp://baboon.math.berkeley.edu/mAVID BLAT: http://www.genome.ucsc.edu/ SSAHA: http://bioinfo.sarang.net/wiki/SSAHA CONREAL:http://conreal.niob.knaw.nl/ MUMmer: http://www.tigr.org/software/mummer.

22. Example output from PipMaker

23. Q: What general patterns can be seen? Q: Why do some of the factors correlate w/ gene density? Genomic view of simple sequence categories

24. Multi-species conservation

25. Conserved Non-Coding Sequences

26. What are those MCS? Regulatory –Transcription factor binding sites –miRNAs or miRNA target sites –Chromosome structure –Insulator sequences Structural –Replication –Recombination –Chromosome structure

27. Between-proteome comparisons Used to identify orthologs. Protein alignments involving a search of one protein from species A against the proteome of a species B Several different bioinformatic approaches have been used to make the comparison. High scoring reciprocal best hits. COGs (and KOGs) Genome-wide phylogenetic analysis

28. Using High scoring reciprocal best hits High scoring reciprocal best hits with the same domain structure are most likely orthologs –share common ancestry –likely to have the same function –Function likely to be more essential (replication, etc) –Genes are not unique to either organism. –E-value should be >0.01 and alignment should stretch over >60% of each protein High scoring hits with slightly different domain structures may be orthologous, but it difficult to tell due to common, conserved domains that have complicated histories Cluster analysis can help sort this out

29. Cut-off p-value:<e-10<e-20<e-50<e-100 Total num seq groups 1171984552236 Num groups w/ > 2 members 56044223079 Num (%) of all (6217) yeast proteins in groups 2697 (40)1848 (30)888 (14)330 (5) Num (%) of all worm proteins in groups 3653 (19)2497 (13)1094 (6)370 (2) Worm v. yeast sequences

30. What is COG? The database of Clusters of Orthologous Groups of proteins (COGs) represents an attempt on a phylogenetic classification of the proteins encoded in complete genomes. Each COG group consists of individual orthologous proteins or orthologous sets of paralogs from at least 3 lineages and thus corresponds to an ancient conserved domain. http://www.ncbi.nlm.nih.gov/COG

31. A shortcut for identifying orthologs ---the genomic-specific best hit (BeT) Given a gene from one genome, the gene from another genome with the highest sequence similarity (the BeT) is the ortholog.

32. Algorithm of clustering orthologous groups (overview) All-against-all sequence comparison (gapped-BLAST) Merge triangles Input protein sequences paralogs Ortholog triangleCOG database Quality control Graph of BeTs

33. The ortholog triangle Multiple alignment A(a) C(c)B(b) Comparing pairwise alignments of AC and AB, we deduce the alignment of BC. Comparing the calculated and deduced alignment of BC; if the two alignments are consistent, the BeTs triangle is a triangle of orthologs and can initiate a new COG group.

34. Algorithm – merging triangles Merging triangles that had a common side until no new ones can be joined. A simple COG with two yeast paralogs isoleucyl-tRNA synthetase The candidates of orthologous sets were detected.

35. Functional and phylogenetic patterns E, E. coli; H, H. influenzae; G, M. genitalium; P, M. pneumoniae; C, Synechocystis sp.; M, M. jannaschii; Y, S. cerevisiae.

36. Phyletic patterns of COGs (2003) 74% of COGs show scattered distribution, which reflect frequent lineage-specific gene loss and horizontal gene transfer in prokarytic evolution. ~500 COGs

37. Representation of the 7 analyzed eukaryotic species in KOGs KOG: eukaryotic orthologous groups

38. Phylogenetic patterns of KOGs All, include representatives from each of the 7 analyzed species; All-Ec, include representatives from each of 6 species other than Encephalitozoon cuniculi; All animals, include representatives from three animal genomes only; All fungi, include representatives from two fungal genomes only.