Comparative Genomics. Page 1. REMINDER: BMI 214 Industry Night. We ve already done some comparative genomics. Loose Definition. Human vs.

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1 Page 1 REMINDER: BMI 214 Industry Night Comparative Genomics Russ B. Altman BMI 214 CS 274 Location: Here (Thornton 102), on TV too. Time: 7:30-9:00 PM (May 21, 2002) Speakers: Francisco De La Vega, Applied Biosystems Gary Peltz, Roche Darren Platt, Exelixis Liping Wei, Nexus Genomics Tom Wu, Genentech Loose Definition Comparative genomics can be defined as the large scale comparison of genomes in order to understand the biology of individual genomes as well as to extract general principles applying to groups of genomes. Comparative genomics is based on the assumption that many biological sequences, structures, and functions are shared across organisms, and the signal from these organisms can be increased by combining them in analyses. We ve already done some comparative genomics Multiple alignment: teaches us more about individual molecules in the context of their homologs Gibbs Sampling: improved detection of sequence motifs using multiple sequences Structure prediction: PSI-Blast and other methods to increase signal for predicting structure Coming Phylogenetics (creation of trees describing evolution of genes/organisms) Human vs. Mouse Synteny = literally means same thread, originally used to denote genes on the same chromosome But, it has come to denote two regions of two genomes that show rough conservation of the order of genes in those regions, and thus are related by common descent.

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3 Page 3 Important issues for Comparative Genomics 1. Aligning very large sequences 2. Comparative approaches to gene finding 3. Comparative approaches to assigning function 4. Comparative approaches to identifying key regulatory regions 5. Many others are being invented daily Aligning very long sequences

4 Page 4 What s the problem? Given: two genomes (very long sequences) Produce: high quality alignment between the two genomes Standard Dynamic Programming = too expensive Made difficult by rearrangements of large homologous segments (cf human/mouse comparison slide) Most progress in aligning similar genomes Still an open problem Genome Research 10, p 950 (2000) GLASS Method for Alignment (First, FIND region that is syntenic, no transpositions allowed ) 1. Chose an integer K that is large (K = 20) 2. Find all matching K-mers in two sequences (not all possible ones are found ) 3. Create new alphabet with each letter = a K-mer found in step 2. Create two new strings from sequences. 4. Align the strings with DP scoring based on exact match plus match of flanking regions GLASS (continued) 5. Find areas with high local alignment of the K-mer strings. 6. Prune high scoring local alignments that have overlapping K-mers 7. Remaining regions represent aligned portions of the two sequences. 8. Recursively apply steps 2-7 to the regions between aligned portions of the sequence, employing smaller and smaller K (15, 12, 9, 8, 7, 6, 5). 9. Align whatever remains with regular DP. WABA Method (Kent et al) ATGCTCCATAGAATACTAT = α CCCATATAC = ρ PRE: Divide one genome into 2000 base queries that overlap by 1000 bases 1. Using BLAST-like word hashing technique to store all hits of 8-mers of form XX*XX*X from 2000 base query sequence to other genome. 2. Choose highest scoring region for 2000 base query, and do slow DP alignment of query with 5000 bases around highest scoring region. 3. Find overlapping alignments, and merge.

5 Page 5 Comparative approaches to gene finding New Gene in conserved region ROSETTA Method for Comparative Gene Calling 0. Use alignment generated by GLASS 1. Build simple gene model (single strand, initial exon, intron, internal exons, internal introns, terminal exon) similar to GENSCAN. 2. Find highest scoring parse on both genomes using DP with scoring based on: splice site locations, codon usage, amino acid similarity, exon length. ftp://theory.lcs.mit.edu/pub/cb/rosetta/table117.xls

6 Page 6 ROSETTA Performance Trained on 117 orthologous genes. 95% sensitivity at nucleotide level 97% specificity at nucleotide level Comparative approaches to assigning function (!!!!!!) Could incorporate more sophisticated gene finding (ala GENSCAN) to improve performance. Phylogenetic Profiles Problem: Assign function to proteins of unknown function. Insight: Proteins that work together in pathways should occur/not-occur in correlation Resulting Algorithm: Look for patterns of presence/absence in genomes. Matching patterns should *correlate* with function Steps for analysis 1. For each gene, use BLAST to assess presence of homologs in other species. 2. Create a binary matrix indicating which homologs present in which organisms. 3. Cluster rows of matrix (for genes) to find genes with similar pattern of occurrence. 4. Find clusters that are removed by 1, 2, 3, etc bit flips. 5. Clustered rows are hypothesized to have common function probably.

7 Page 7 Evaluation 1. Evaluate by comparing keywords in SwissProt for genes with their clustering. 2. Evaluate by assessing metabolic function (as stored in EcoCYC) of genes that are clustered. Result: Clustered genes much more likely than random to have shared function. But not 100% (For (1): 18% vs. 4% overlap)

8 Page 8 Protein-Protein Interactions Problem: Assign function to proteins of unknown function. Insight: Over evolution, some proteins that interact with one another fuse their genes to create a single protein. Resulting Algorithm: Compare genomes to find genes that have fused in some organisms, but not in others. Use this to predict that they are involved in same function probably. Method for Detecting Fusion Proteins 1. Prepare a list of all known protein domains (using pfam or prodom databases). 2. For each protein in SwissProt, compute all the domains it contains. 3. Find triplets of SwissProt entries in which two (A,B) have nonhomologous domains, and third (C) has both of these domains together. 4. For a given genome (e.g. E. coli) look for evidence of A & B homologs, and predict an interaction (physical or functional) based on C. Find SwissProt Triplets (A B C) Enright et al, Nature Nov 4;402(6757):23, 25-6.

9 Page 9 Evaluation 1. Compare SwissProt annotations for genes of known function with their predicted functions. (32% vs. 14% for random) 2. Use DIP (Database of Interacting Proteins) as gold standard (6.4% of entries discovered by this method). 3. Correlate results of phylogenetic profile work. (8X more likely to occur in both than random interactions). Putting it all together Putting it all together Nature 402 (1999), p 83 Non-coding regulatory regions Comparative approaches to finding key regulatory regions Problem: How do we identify areas of noncoding DNA that are important for regulating gene expression? Insight: Related species may conserve important non-coding DNA in much the same way they conserve exons (vs. introns and other non-regulatory non-coding DNA which evolve more rapidly) Resulting Algorithm: Look for conserved non-coding regions as guide for search for regulatory regions.

10 Page 10 Genome Research 12, p 832 (2002) Conclusions 1. Genome-scale alignment is still difficult, and there is room for improvement. 2. Comparative analysis of genomes can yield insight into location of genes and location of regulatory regions. 3. Comparative analysis of genomes can yield insight into evolutionary history of protein function, which is useful for function prediction/annotation.