Primer Friday 10am Beckman B-302 Introduction to the UCSC Browser.

1. Primer Friday 10am Beckman B-302 Introduction to the UCSC Browser. http://cs273a.stanford.edu [Bejerano Fall11/12]

2. Lecture 6 Genome Evolution Chromosomal Mutations Paralogy & Orthology Chains & Nets http://cs273a.stanford.edu [Bejerano Fall11/12]

3. One Cell, One Genome, One Replication Every cell holds a copy of all its DNA = its genome. The human body is made of ~1013 cells. All originate from a single cell through repeated cell divisions. egg DNA strings = Chromosomes egg cell cell division genome = all DNA chicken egg chicken ≈ 1013 copies(DNA) of egg (DNA) http://cs273a.stanford.edu [Bejerano Fall11/12]

4. Mutation Rate per bp • 10-9 per base pair per cell division • This refers to mutations that are not repaired • Thus, there are at least six new mutations in each kid that were not present in either parent • Mutations range from the smallest possible (single base pair change) to the largest – whole genome duplication. • Selection does not tolerate all of these mutation, but it sure does tolerate some. chicken egg chicken

5. Example: Human-Chimp Genomic Differences 1% Number of events 3% Open question.. Fusion Indels < 10 Kb Pericentric inversions Nucleotide substitutions Deletions/Duplications Microinversions < 100 Kb Microinversions > 100 Kb

6. Chromosomal (ie Big) Mutations May Involve: Changing the structure of a chromosome The loss or gain of part of a chromosome

7. Chromosome Mutations Five types exist: Deletion Inversion Translocation Nondisjunction Duplication

8. Deletion Due to breakage A piece of a chromosome is lost

9. Inversion Chromosome segment breaks off Segment flips around backwards Segment reattaches

10. Duplication Occurs when a genomic region is repeated

11. Whole Genome Duplication at the Base of the Vertebrate Tree Xen.Laevis WGD http://cs273a.stanford.edu [Bejerano Fall11/12]

12. Translocation Involves two chromosomes that aren’t homologous Part of one chromosome is transferred to another chromosomes

13. Nondisjunction Failure of chromosomes to separate during meiosis Causes gamete to have too many or too few chromosomes Disorders: DownSyndrome – three 21st chromosomes Turner Syndrome – single X chromosome Klinefelter’s Syndrome – XXY chromosomes

14. Chromosome Mutation Animation

15. The Species Tree • How to infer a species tree? • Phenotype • Phenotypic characters • Inc. fossil evidence • Genotype • Molecular Evolution • Inc. Mobile Elements

16. The Species Tree S S Sampled Genomes S Speciation Time

17. The Species Tree S S Sampled Genomes S Speciation Time

18. Gene tree Speciation Duplication Loss A Gene tree evolves with respect to a Species tree Species tree

19. Gene tree Speciation Duplication Loss 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) single ancestral gene Species tree http://cs273a.stanford.edu [Bejerano Fall11/12]

20. Gene tree Speciation Duplication Loss Gene trees and even species trees are figments of our (scientific) imagination Species trees and gene trees can be wrong. All we really have are extant observations, and fossils. Observed Inferred single ancestral gene Species tree http://cs273a.stanford.edu [Bejerano Fall11/12]

21. Gene Families http://www.ncbi.nlm.nih.gov/Education/BLASTinfo/orthologs3.gif

22. Gu et al. Age distribution of human gene families shows significant roles of both large-scale and small-scale duplication in vertebrate evolution (2002) Nature Genetics31; 205-208

23. Chaining Alignments Chaining highlights homologous regions between genomes (it bridges the gulf between syntenic blocks and base-by-base alignments. Local alignments tend to break at transposon insertions, inversions, duplications, etc. Global alignments tend to force non-homologous bases to align. Chaining is a rigorous way of joining together local alignments into larger structures. http://cs273a.stanford.edu [Bejerano Fall11/12]

24. “Raw” Blastz track (no longer displayed) Alignment = homologous regions Protease Regulatory Subunit 3

25. Chains & Nets: How they’re built • 1: Blastz one genome to another • Local alignment algorithm • Finds short blocks of similarity Hg18: AAAAAACCCCCAAAAA Mm8: AAAAAAGGGGG Hg18.1-6 + AAAAAA Mm8.1-6 + AAAAAA Hg18.7-11 + CCCCC Mm8.1-5 - CCCCC Hg18.12-16 + AAAAA Mm8.1-5 + AAAAA

26. Chains & Nets: How they’re built • 2: “Chain” alignment blocks together • Links blocks that preserve order and orientation • Not single coverage in either species Hg18: AAAAAACCCCCAAAAA Mm8: AAAAAAGGGGGAAAAA • Hg18: AAAAAACCCCCAAAAA • Mm8 • chains Mm8.1-6 + Mm8.12-16 + Mm8.7-11 - Mm8.12-15 + Mm8.1-5 +

27. Another Chain Example Ancestral Sequence A B C D E Human Sequence Mouse Sequence A B C A B C D E B’ D E In Human Browser In Mouse Browser Implicit Human sequence Implicit Mouse sequence … … D E … … Mouse chains Human chains D E D E B’

28. Gap Types: Single vs Double sided Ancestral Sequence A B C D E Human Sequence Mouse Sequence A B C A B C D E B’ D E In Human Browser In Mouse Browser Implicit Human sequence Implicit Mouse sequence … … D E … … Mouse chains Human chains D E D E B’

29. The Use of an Outgroup OutgroupSequence A B C Human Sequence Mouse Sequence D E A B C A B C D E B’ D E In Human Browser In Mouse Browser Implicit Human sequence Implicit Mouse sequence … … D E … … Mouse chains Human chains D E D E B’

30. What if my topology is wrong? Mouse Sequence A B C Human Sequence “Outgroup” Sequence B’ D E A B C A B C D E D E In Human Browser In Mouse Browser Implicit Human sequence Implicit Mouse sequence … … D E … … Mouse chains Human chains D E D E B’

31. Chains join together related local alignments likely ortholog likely paralogs shared domain? Protease Regulatory Subunit 3 http://cs273a.stanford.edu [Bejerano Fall11/12]

32. 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] http://cs273a.stanford.edu [Bejerano Fall11/12]

33. Before and After Chaining http://cs273a.stanford.edu [Bejerano Fall11/12]

34. Chaining Algorithm Input - blocks of gapless alignments from blastz Dynamic program based on the recurrence relationship:score(Bi) = max(score(Bj) + match(Bi) - gap(Bi, Bj)) 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<i http://cs273a.stanford.edu [Bejerano Fall11/12]

35. 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. http://cs273a.stanford.edu [Bejerano Fall11/12]

36. Net Focuses on Ortholog http://cs273a.stanford.edu [Bejerano Fall11/12]

37. 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. [Angie Hinrichs, UCSC wiki] http://cs273a.stanford.edu [Bejerano Fall11/12]

38. Before and After Netting http://cs273a.stanford.edu [Bejerano Fall11/12]