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[BejeranoWinter12/13] 1 MW 11:00-12:15 in Beckman B302 Prof: Gill Bejerano TAs: Jim Notwell & Harendra Guturu CS173 Lecture 12:

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Presentation on theme: "[BejeranoWinter12/13] 1 MW 11:00-12:15 in Beckman B302 Prof: Gill Bejerano TAs: Jim Notwell & Harendra Guturu CS173 Lecture 12:"— Presentation transcript:

1 http://cs173.stanford.edu [BejeranoWinter12/13] 1 MW 11:00-12:15 in Beckman B302 Prof: Gill Bejerano TAs: Jim Notwell & Harendra Guturu CS173 Lecture 12: Chains & Nets, Conservation & Function

2 http://cs173.stanford.edu [BejeranoWinter12/13] 2 Announcements HW2 Due Today As are project assignments Coming monday 2/25 lecture has been moved to LK101 (building next door – we’ll post instructions)

3 http://cs173.stanford.edu [BejeranoWinter12/13] 3 Inferring Genomic Mutations From Alignments of Genomes

4 http://cs173.stanford.edu [BejeranoWinter12/13] 4 Terminology Orthologs : Genes related via speciation (e.g. C,M,H3) Paralogs: Genes related through duplication (e.g. H1,H2,H3) Homologs: Genes that share a common origin (e.g. C,M,H1,H2,H3) Species tree Gene tree Speciation Duplication Loss single ancestral gene

5 What? Compare whole genomes Compare two genomes Within (intra) species Between (inter) species Compare genome to itself Why? Comparison reveals functional and neutral regions Homologous regions most often have similar functions Modification of functional regions can reveal Disease susceptibility Adaptation How? http://cs173.stanford.edu [BejeranoWinter12/13] 5

6 6 Every Genome is Different DNA Replication is imperfect – between individuals of the same species, even between the cells of an individual....ACGTACGACTGACTAGCATCGACTACGA... chicken egg...ACGTACGACTGACTAGCATCGACTACGA... functional junk TT CAT “anything goes” many changes are not tolerated chicken

7 Sequence Alignment -AGGCTATCACCTGACCTCCAGGCCGA--TGCCC--- TAG-CTATCAC--GACCGC--GGTCGATTTGCCCGAC Similarity is often measured using “%id”, or percent identity %id = number of matching bases / number of alignment columns Where Every alignment column is a match / mismatch / indel base Where indel = insertion or deletion (requires an outgroup to resolve) AGGCTATCACCTGACCTCCAGGCCGATGCCC TAGCTATCACGACCGCGGTCGATTTGCCCGAC

8 What to expect from genome comparisons? http://cs173.stanford.edu [BejeranoWinter12/13] 8 human lizard Objective: find local alignment blocks, that are likely homologous (share common origin) O(mn) examine the full matrix using DP O(m+n) heuristics based on seeding + extension trades sensitivity for speed

9 9 “Raw” (B)lastz track (no longer displayed) Protease Regulatory Subunit 3 Alignment = homologous regions

10 Chaining co-linear alignment blocks http://cs173.stanford.edu [BejeranoWinter12/13] 10 human lizard Objective: find local alignment blocks, that are likely homologous (share common origin) Chaining strings together co-linear blocks in the target genome to which we are comparing. Double lines when there is unalignable sequence in the other species. Single lines when there isn’t.

11 Reference genome perspective, The Use of an Outgroup ABC DE Outgroup Sequence ABC DE Human Sequence ABC DE Mouse Sequence B’ In Human Browser Implicit Human sequence Mouse chains B’ … … DE DE In Mouse Browser Implicit Mouse sequence Human chains … … DE 11

12 Gap Types: Single vs Double sided ABC DE Ancestral Sequence ABC DE Human Sequence ABC DE Mouse Sequence B’ In Human Browser Implicit Human sequence Mouse chains B’ … … DE DE In Mouse Browser Implicit Mouse sequence Human chains … … DE 12

13 http://cs173.stanford.edu [BejeranoWinter12/13] 13 Conservation Track Documentation

14 http://cs173.stanford.edu [BejeranoWinter12/13] 14 Chains join together related local alignments Protease Regulatory Subunit 3 likely ortholog likely paralogs shared domain?

15 Note: repeats are a nuisance http://cs173.stanford.edu [BejeranoWinter12/13] 15 human mouse If, for example, human and mouse have each 10,000 copies of the same repeat: We will obtain and need to output 10 8 alignments of all these copies to each other. Note that for the sake of this comparison interspersed repeats and simple repeats are equal nuisances. Also note that simple repeats, but not interspersed repeats, violate the assumption that similar sequences are homologous. Solution: 1 Discover all repetitive sequences in each genome. 2 Mask them when doing genome to genome comparison. 3 Chain your alignments. 4 Add back to the alignments only repeat matches that lie within pre-computed chains.

16 http://cs173.stanford.edu [BejeranoWinter12/13] 16 Chains a chain is a sequence of gapless aligned blocks, where there must be no overlaps of blocks' target or query coords within the chain. Within a chain, target and query coords are monotonically non- decreasing. (i.e. always increasing or flat) double-sided gaps are a new capability (blastz can't do that) that allow extremely long chains to be constructed. not just orthologs, but paralogs too, can result in good chains. but that's useful! chains should be symmetrical -- e.g. swap human-mouse -> mouse- human chains, and you should get approx. the same chains as if you chain swapped mouse-human blastz alignments. chained blastz alignments are not single-coverage in either target or query unless some subsequent filtering (like netting) is done. chain tracks can contain massive pileups when a piece of the target aligns well to many places in the query. Common causes of this include insufficient masking of repeats and high-copy-number genes (or paralogs). [Angie Hinrichs, UCSC wiki]

17 http://cs173.stanford.edu [BejeranoWinter12/13] 17 Before and After Chaining

18 http://cs173.stanford.edu [BejeranoWinter12/13] 18 Chaining Algorithm Input - blocks of gapless alignments from blastz Dynamic program based on the recurrence relationship: score(B i ) = max(score(B j ) + match(B i ) - gap(B i, B j )) Uses Miller’s KD-tree algorithm to minimize which parts of dynamic programming graph to traverse. Timing is O(N logN), where N is number of blocks (which is in hundreds of thousands) j { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/13/3992763/slides/slide_18.jpg", "name": "http://cs173.stanford.edu [BejeranoWinter12/13] 18 Chaining Algorithm Input - blocks of gapless alignments from blastz Dynamic program based on the recurrence relationship: score(B i ) = max(score(B j ) + match(B i ) - gap(B i, B j )) Uses Miller’s KD-tree algorithm to minimize which parts of dynamic programming graph to traverse.", "description": "Timing is O(N logN), where N is number of blocks (which is in hundreds of thousands) j

19 http://cs173.stanford.edu [BejeranoWinter12/13] 19 Netting Alignments Commonly multiple mouse alignments can be found for a particular human region, particularly for coding regions. Net finds best match mouse match for each human region. Highest scoring chains are used first. Lower scoring chains fill in gaps within chains inducing a natural hierarchy.

20 http://cs173.stanford.edu [BejeranoWinter12/13] 20 Net highlights rearrangements A large gap in the top level of the net is filled by an inversion containing two genes. Numerous smaller gaps are filled in by local duplications and processed pseudo-genes.

21 http://cs173.stanford.edu [BejeranoWinter12/13] 21 A Rearrangement Hot Spot Rearrangements are not evenly distributed. Roughly 5% of the genome is in hot spots of rearrangements such as this one. This 350,000 base region is between two very long chains on chromosome 7.

22 http://cs173.stanford.edu [BejeranoWinter12/13] 22 Nets attempt to capture the ortholog (they also hide everything else)

23 http://cs173.stanford.edu [BejeranoWinter12/13] 23 Retroposed Genes and Pseudogenes Pseudogenes (“dead genes”): Genomic sequences that resemble (originated from) genes that no longer make proteins. Retrogenes (“retrotranscribed”): Protein coding RNA that was reverse transcribed and inserted back into the genome. The RNA can be grabbed at any stage (partial/full transcript, before/during/after all introns are spliced).

24 http://cs173.stanford.edu [BejeranoWinter12/13] 24 Useful in finding pseudogenes Ensembl and Fgenesh++ automatic gene predictions confounded by numerous processed pseudogenes. Domain structure of resulting predicted protein must be interesting! gene pred.

25 http://cs173.stanford.edu [BejeranoWinter12/13] 25 Nets/chains can reveal retrogenes (and when they jumped in!)

26 http://cs173.stanford.edu [BejeranoWinter12/13] 26 Nets a net is a hierarchical collection of chains, with the highest-scoring non-overlapping chains on top, and their gaps filled in where possible by lower-scoring chains, for several levels. a net is single-coverage for target but not for query. because it's single-coverage in the target, it's no longer symmetrical. the netter has two outputs, one of which we usually ignore: the target- centric net in query coordinates. The reciprocal best process uses that output: the query-referenced (but target-centric / target single- cov) net is turned back into component chains, and then those are netted to get single coverage in the query too; the two outputs of that netting are reciprocal-best in query and target coords. Reciprocal- best nets are symmetrical again. nets do a good job of filtering out massive pileups by collapsing them down to (usually) a single level. GB: for human inspection always prefer looking at the chains! [Angie Hinrichs, UCSC wiki]

27 http://cs173.stanford.edu [BejeranoWinter12/13] 27 Before and After Netting

28 http://cs173.stanford.edu [BejeranoWinter12/13] 28 Convert / LiftOver "LiftOver chains" are actually chains extracted from nets, or chains filtered by the netting process. LiftOver – batch utility

29 http://cs173.stanford.edu [BejeranoWinter12/13] 29 What nets can’t show, but chains will

30 http://cs173.stanford.edu [BejeranoWinter12/13] 30 Same Region… same in all the other fish

31 Drawbacks Inversions not handled optimally > > > > chr1 > > > < < < < chr1 < < < < < < < < chr5 < < < < Chains Nets > > > > chr1 > > > < < < < chr5 < < < < 31

32 Drawbacks High copy number genes can break orthology 32

33 http://cs173.stanford.edu [BejeranoWinter12/13] 33 Self Chain reveals paralogs (self net is meaningless)

34 http://cs173.stanford.edu [BejeranoWinter12/13] 34 Conservation and Function

35 http://cs173.stanford.edu [BejeranoWinter12/13] 35 Evolution = Mutation + Selection Mistakes can happen during DNA replication. Mistakes are oblivious to DNA segment function. But then selection kicks in....ACGTACGACTGACTAGCATCGACTACGA... chicken egg...ACGTACGACTGACTAGCATCGACTACGA... functional junk TT CAT “anything goes” many changes are not tolerated chicken Conservation implies function! (But what function?)

36 http://cs173.stanford.edu [BejeranoWinter12/13] 36 Vertebrates: what to sequence? [Human Molecular Genetics, 3rd Edition]  you are here, Opossum, Lizard, Stickleback too far sweet spot too close Which species to compare to? Too close and purifying selection will be largely indistinguishable from the neutral rate. Too far and many functional orthologs will diverge beyond our ability to accurately align them.

37 Searching Near And Far http://cs173.stanford.edu [BejeranoWinter12/13] 37 Search too near (eg human to chimp or orang above) and you cannot distinguish neutral sequence from sequence under purifying selection. Search further still (eg mouse) and the two distributions pry apart. But now you’ve lost younger functional sequences born after the split. Ie, conservation implies function, but lack of conservation does NOT imply lack of function!

38 http://cs173.stanford.edu [BejeranoWinter12/13] 38, Opossum, Lizard, Stickleback Phylogenetic Shadowing  you are here too close “too close” can actually be a boon if you have enough closely related genomes

39 PhastCons Conserved Elements http://cs173.stanford.edu [BejeranoWinter12/13]

40 Distant homologies http://cs173.stanford.edu [BejeranoWinter12/13] 40 When species diverge too much (e.g. chicken and beyond above), confident alignments can no longer be detected at the DNA level. E.g.: all SPI1 and SLC39A13 exons are there in chicken & fish.

41 Distant homologies search strategies http://cs173.stanford.edu [BejeranoWinter12/13] 41 Here it is much better to search a gene model from species A (e.g human) against the genome of species B (e.g. chicken) This is a search of amino acids in all their possible codons into a gene structure with unknown exon – intron structure. (eg TBLASTN, translated BLAT)

42 Distant homologies http://cs173.stanford.edu [BejeranoWinter12/13] 42 Find the most distantly related genes using gene models in both species: 1 search amino acids sequences against each other. (eg using BLASTP). 2 Map your hits back to the two respective genomes, anchored on the amino acid alignment (respecting any exon-intron gene body structure change). 3 Examine co-linear homology of flanking genes to try and call orthologs from paralogs.

43 RNA homology searches http://cs173.stanford.edu [BejeranoWinter12/13] 43 1 Define a mathematical construct that describe potential homologs. 2 Go search for them (efficiently!). 3 Examine genomic context.

44 Enhancer remote homologs http://cs173.stanford.edu [BejeranoWinter12/13] 44 Enhancer = Gene regulatory sequences in general are the most challenging to search for: Individual binding sites are very flexible. Gaps between binding sites may evolve (semi) neutrally, making DNA alignment seeding particularly frail. Binding site gain/loss and shuffling may or may not be allowed – we need a better understanding of underlying logic.

45 Exceptionally Old Enhancers Exist http://cs173.stanford.edu [BejeranoWinter12/13] 45 But how many of these really exist?

46 http://cs173.stanford.edu [BejeranoWinter12/13] 46 Ultraconservation: No known function requires this much conservation CDSncRNATFBS * * * * * seq. ?

47 “Gene” Finding III: Comparative Genomics http://cs173.stanford.edu [BejeranoWinter12/13] 47

48 The challenge: map code to output http://cs173.stanford.edu [BejeranoWinter12/13] 48 genome person Ultimately we sequence genomes, and study their function in detail to understand genome to phenotype relationships: Minus side: Genomic contribution to disease Plus side: Adaptation and speciation 3*10 9 letters 10 13 cells To be continued…


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