1. MEME homework: probability of finding GAGTCA at a given position in the yeast genome, based on a background model of A = 0.3, T = 0.3, G = 0.2, C = 0.2 (0.2)(0.3)(0.2)(0.3)(0.2)(0.3) = 2.16 x 10 -4 This is the probability that ANY 6 mer will be this sequence by chance How many instances within 1,000 bp upstream of 6,000 genes? Number of 6mers per 1,000 bp: 1000 bp – 5 bp (account for 6mer start position) 6000 genes * 995 = 5.97 x 10 6 possible 6mers total P that any one is your sequence: 2.16 x 10 -4 x 5.97 x 10 6 = 1290 sites = 995 6mers per gene upstream region BUT … can also have the reverse complement (i.e. site on other strand) = 2X possible sites (because of our bg model) = 2580 possible matches 1

2. An alternative approach: Phylogenetic footprinting Rather than look at multiple, different regulatory regions from one species, look at one region but across multiple, orthologous regions from many species. Hypothesis: functional regions of the genome will be conserved more than ‘ nonfunctional ’ regions, due to selection. Therefore, simply look for regions of sequence that are conserved above background. 2

3. Simplest case: stretches of very highly conserved sequence Kellis et al. 2003 “ Sequencing and comparison of yeast species to identify genes and regulatory elements ” Sequenced 4 closely related Saccharomyces genomes & identified conserved sequences in multiple alignments of orthologous sequences from the four species. 3

4. Position bits Information Profile: Incorporating evolutionary models can improve motif finding Remember that evolution acts on functionally important base pairs … Also remember from our motif finding exercise that not all contiguous base pairs are equally important (information content). 4

5. Incorporating evolutionary models can improve motif finding Remember that evolution acts on functionally important base pairs … Also remember from our motif finding exercise that not all contiguous base pairs are equally important (information content). Moses et al. 2003 “ Position specific variation in the rate of evolution in transcription factor binding sites ” Rate of evolution (ie. degree of conservation) within a motif is inversely proportional to the information content … important base pairs evolve slower 5

6. Sinha et al. 2004 “ PhyME: A probabalistic algorithm for finding motifs in sets of orthologous sequences ” Moses et al. 2004 “ Monkey: identification of transcription factor binding sites in multiple alignments using a binding site-specific evolutionary model Siddharthan et al. 2005 “ PhyloGibbs: A Gibbs sampling motif finder that incorporates phylogeny. ” Wang & Stormo. 2003 (PhyloCon) “Combining phylogenetic data with co-regulated genes to identify regulatory motifs” Prakash et al. 2004. (OrthoMEME) “Motif discovery in heterogeneous sequence data Multiple motif finding methods now work on multiple alignments of regulatory regions of coregulated genes. Given: 1) group of regulatory regions of coregulated genes 2) orthologs of each region, in the form of multiple alignments 6

7. Keep in mind that the relevant evolutionary models are specific for what one is looking for (TF binding sites, ncRNA, etc) Moses et al. 2003 “ Position specific variation in the rate of evolution in transcription factor binding sites ” Rate of evolution (ie. degree of conservation) within a motif is inversely proportional to the information content … important base pairs evolve slower 7

8. VISTA suite for visualizing conservation in global alignments Pre-computed multiple global alignments of mammalian genomes, visualized by conservation level. -- Uses BLAT local alignment tool to find seeds of high sequence similarity, then these seeds are used for global single- or multiple-genome alignment Frazer et al. 2004 “ VISTA: computational tools for comparative genomics ” 8

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10. Which species to compare? Balance between: -- species closely related enough that: 1) There ’ s enough similar sequence to get confident pairwise alignments 2) The sequences of interest and their corresponding functions have been conserved -- species distantly enough related that: 1) nonfunctional sequence has had time to diverge 10

11. The above approaches have focused on using similarity/conservation to identify important regions of the genome … A large focus in genomics is understanding the differences in genome sequences and what accounts for the vast diversity in phenotypes within a population. Analysis of single nucleotide polymorphisms (SNP) within populations, Analysis of variations in gene expression within and between populations, Analysis of quantitative trait loci (QTLs) accounting for differences in gene expression. 11

12. Connecting phenotype to genotype -- Large variations in size, shape, health, etc in human populations -- Much of that variation has to do with disease susceptibility -- A major goal of genetics (and now genomics) is understanding the consequences of genetic variation. A major force in genomics is to identify and annotate SNPs in human populations, and identify those related to disease ~2800 disease-associated genes known, mostly from positional cloning & mapping studies Done by linkage analysis: pattern of marker inheritance in families with heritable diseases 12

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14. Each base-pair position on human chromosome 21 is interrogated 8 times (4 in forward & 4 in reverse orientations) GGAGATGAGTTC G ATTACTCTTAGG GGAGATGAGTTC A ATTACTCTTAGG GGAGATGAGTTC T ATTACTCTTAGG GGAGATGAGTTC C ATTACTCTTAGG 1.7 x 10 8 oligos total on eight Affy wafers were used to identify SNPs on human Chromosome 21 from 21 different individuals. Array-based methods of SNP detection & Haplotype mapping 14

15. Each row = single SNP Each column = Ch 21 Blue = major allele Yellow = minor allele Much of the chromosomal variation is explained with relatively limited haplotype diversity. 80% of haplotype structure can be captured with only 10% of the SNPs in that block (need only 2SNPs to type) Haplotype length can vary from a few kb to mega bases. 15

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17. Phenotypic variation (including disease susceptibility) are often linked to copy # changes This is especially true of numerous types of cancers, where local amplifications and translocations increase the copy number of cell proliferation regulators, etc. 17

18. Amplifications in breast cancer lines increase the copy # of specific regulators.. 18

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