mw 11:00-12:15 in beckman b302 prof: gill bejerano tas: jim notwell & harendra guturu

http://cs173.stanford.edu [BejeranoWinter12/13] 1

MW 11:00-12:15 in Beckman B302Prof: Gill BejeranoTAs: Jim Notwell & Harendra Guturu

CS173

Lecture 12: Chains & Nets, Conservation & Function


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)


Inferring Genomic MutationsFrom Alignments of Genomes


TerminologyOrthologs : 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

SpeciationDuplicationLoss

singleancestralgene

• 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?



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...

functionaljunk

TT CAT

“anythinggoes”

many changesare not tolerated

chicken

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)

AGGCTATCACCTGACCTCCAGGCCGATGCCCTAGCTATCACGACCGCGGTCGATTTGCCCGAC

What to expect from genome comparisons?


human

lizar

d

Objective: find local alignment blocks, that are likely homologous (share common origin)

O(mn) examine the full matrix using DPO(m+n) heuristics based on seeding + extension trades sensitivity for speed

9

“Raw” (B)lastz track (no longer displayed)

Protease Regulatory Subunit 3

Alignment = homologous regions

Chaining co-linear alignment blocks


human

lizar

d

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.

Reference genome perspective,The Use of an Outgroup

A B CD E

Outgroup Sequence

A B CD E

Human SequenceA B CD E

Mouse Sequence

B’

In Human BrowserImplicitHumansequence

Mousechains B’

…

…

D E

D E

In Mouse BrowserImplicitMousesequence

Humanchains

…

… D E

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Gap Types: Single vs Double sidedA B C

D E

Ancestral Sequence

A B CD E

Human SequenceA B CD E

Mouse Sequence

B’

In Human BrowserImplicitHumansequence

Mousechains B’

…

…

D E

D E

In Mouse BrowserImplicitMousesequence

Humanchains

…

… D E

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Conservation Track Documentation


Chains join together related local alignments

Protease Regulatory Subunit 3

likely ortholog

likely paralogsshared domain?

Note: repeats are a nuisance


humanm

ouse

If, for example, human and mouse have each 10,000 copiesof the same repeat:We will obtain and need to output 108 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.


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]


Before and After Chaining


Chaining AlgorithmInput - blocks of gapless alignments from blastzDynamic 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

See [Kent et al, 2003] “Evolution's cauldron: Duplication, deletion, and rearrangement in the mouse and human genomes”


Netting AlignmentsCommonly 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.


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.


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.


Nets attempt to capture the ortholog

(they also hide everything else)


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).


Useful in finding pseudogenes

Ensembl and Fgenesh++ automatic gene predictions confounded by numerous processed pseudogenes. Domain structure of resulting predicted protein must be interesting!

genepred.


Nets/chains can reveal retrogenes (and when they jumped in!)


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]


Before and After Netting


Convert / LiftOver"LiftOver chains" are actually chains extracted from nets, or chains filtered by the netting process.

LiftOver – batch utility


What nets can’t show, but chains will


Same Region…

same in allthe other fish

Drawbacks

• Inversions not handled optimally

> > > > chr1 > > > > > > > chr1 > > >

< < < < chr1 < < < <

< < < < chr5 < < < <

Chains

Nets > > > > chr1 > > > > > > > chr1 > > >

< < < < chr5 < < < <

31

Drawbacks

• High copy number genes can break orthology

32


Self Chain reveals paralogs

(self net ismeaningless)


Conservation and Function


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...

functionaljunk

TT CAT

“anythinggoes”

many changesare not tolerated

chicken

Conservation implies function!(But what function?)


Vertebrates: what to sequence?

[Human Molecular Genetics, 3rd Edition]

you are here

, Opossum

, Lizard

, Stickleback

too farsweet spottoo 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.

Searching Near And Far


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, butlack of conservation does NOT imply lack of function!


, Opossum

, Lizard

, Stickleback

Phylogenetic Shadowing

you are here

too close

“too close” can actually be a boon if you have enough closely related genomes

PhastCons Conserved Elements

http://cs173.stanford.edu [BejeranoWinter12/13]

Distant homologies


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.

Distant homologies search strategies


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)

Distant homologies


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.

RNA homology searches


1 Define a mathematical construct that describe potential homologs.2 Go search for them (efficiently!). 3 Examine genomic context.

Enhancer remote homologs


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.

Exceptionally Old Enhancers Exist


But how many of these really exist?


Ultraconservation: No known function requires this much conservation

CDS ncRNA TFBS

*****

seq.

?

“Gene” Finding III: Comparative Genomics


The challenge: map code to output


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*109 letters1013 cells

To be continued…

mw 11:00-12:15 in beckman b302 prof: gill bejerano tas: jim notwell & harendra guturu

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