Multiple Sequence Alignment

http://cs273a.stanford.edu [Bejerano Fall16/17] 1

CS273A

Lecture 17: Cross Species Comparisons


Announcements• Your project should be coming along nicely!

TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATACATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTCAGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTCCGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACTAGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATGATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAAAAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAATTGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAATTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGGATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGATTTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAATCTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATGAACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATCATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAAAAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCAGCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAACTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACATTTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAAGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACAACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATCAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGTTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCTTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATTAATGCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATAAAG

3http://cs273a.stanford.edu [Bejerano Fall16/17]


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


Chains join together related local alignments

Protease Regulatory Subunit 3

likely ortholog

likely paralogsshared domain?


Before and After Chaining


Netting AlignmentsCommonly multiple mouse alignments can be found for a particular human region, eg including for most 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.


Nets attempt to computationally capture orthologs

(they also hide everything else)


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

Drawbacks

14

• Inversions not handled optimally

> > > > chr1 > > >

> > > > chr1 > > >

< < < < chr1 < < < <

< < < < chr5 < < < <

Chains

Nets > > > > chr1 > > >

> > > > chr1 > > >

< < < < chr5 < < < <

http://cs273a.stanford.edu [Bejerano Fall16/17]


What nets can’t show, but chains will


Same Region…

same in allthe other fish

Drawbacks

• High copy number genes can break orthology

17

Gene Families

18


Self Chain reveals (some) paralogs

(self net ismeaningless)


The Biggest Challenge in Genomics…… is computational:

How does this encode this

Program Output

21

Xkcd Take – It’s Actually Not That Bad



Why compare to Chimp?

2323

Humans and Chimpanzees PossessMany Vastly Different Phenotypes

A: Chimp B: Human

A B

[Varki, A. and Altheide, T., Genome Res., 2005]

A B


Disease Susceptibility Differences


What human-chimp changes do we find?

Small

Large

Medium


Large differences

Fusion (HSA 2) 18 pericentromeric inversions


Medium Sized Differences

Gene families expandand contract

Mobile element insertionand mediated deletion


Small Differences

1% difference at the base level

PhenotypeGenotype

Genetic basis of human phenotypes?N

umbe

r of r

earr

ange

men

ts


Most mutationsare near/neutral.How do we know?4D sites, ARs.

The Genotype - Phenotype divide


Can we find evolutionary patterns that are distinct enough to be phenotypically revealing?

Species A

Species B

Problem #1:

Too many nucleotide changes between any pair of related species (or individuals).

The vast majority of these are near/neutral.


Is it in our protein coding genes?

70-80% of all human-chimp orthologous proteins differ.On average they differ by 1-2 amino acids.• Which amino acid changes matter?• One can also compare non-synonymous amino acid

substitutions with synonymous changes, and look for proteins unusually enriched from the former.Those may be evolving under positive selection.


Positive and negative gene selection in the human genome


Candidate genes for human specific evolution

...

34

What if we did an unbiased search?Human-specific substitutions in conserved sequences

34

[Pollard, K. et al., Nature, 2006] [Beniaminov, A. et al., RNA, 2008]

Human

Chimp

Humanrapid change

HAR1:• Novel ncRNA• 18 unique human substitutions

conserved

Chimp

Different Unbiased Search: Loss vs Gain

Chimp

Humanrapid change • 4-18 unique human substitutions

• Pollard, K. et al., Nature, 2006• Prabhakar, S. et al., Science, 2008

conserved

Human Accelerated Regions

deleted!

Chimp

Human

conserved

Human Conserved Sequence Deletions

(hCONDELs)• Complete human loss of sequence• Likely to confer human-specific

phenotypes


[McLean, Reno, Pollen et al., Nature, 2011]

35

Identifying hCONDELs


deleted!

Chimp

Human

conserved

hCONDEL genomic distribution

• Median size: 2.8kb• Not enriched in highly variable genomic regions• Most do not disrupt proteins: only 1 validated exonic deletion



Deletions of functional non-coding DNAGene Gene Gene

GeneGeneGene

Gene Gene

GeneGene

( ) ( ) ( )

( )

( ) ( ) ( ) ( )

( )( )

Gene Gene

Gene with functione.g. “neuronal gene” Gene without function

( )hCONDEL Conserved element

[McLean et al., Nat. Biotechnol., 2010]

http://great.stanford.edu

38

http://great.stanford.edu/

Functional enrichments of hCONDELs

Ontology Term p-valueGene Ontology Steroid hormone receptor activity 3.73 x 10-4

InterPro Fibronectin, type III 1.01 x 10-4

Zinc finger, nuclear hormone receptor type 1.80 x 10-4

CD80-like, immunoglobulin C2 set 1.37 x 10-3

Entrez Gene Neuronal genes 1.11 x 10-4

Monoallelically-Expressed Genes Monoallelic expression 8.62 x 10-3

These enrichmentsare unique to hCONDELs

http://great.stanford.eduhttp://cs273a.stanford.edu [Bejerano Fall16/17] 39

http://great.stanford.edu/

hCONDEL near Androgen Receptor

The deletion appears fixed in humansand appears deleted in Neandertal.


Androgen Receptor chimpanzee enhancer assay

[Phil Reno, David Kingsley]

Androgen Receptor

Human

Chimp

Genomic fragment Hsp68 promoter LacZ reporter gene


The human deletion near AR acts as an enhancer within known AR expression domains

E16.5

Sensory whiskers

E16.5

Genital tubercle

E16.5

E16.5

Penile spines

8 weeksE16.5

Chi

mp

enha

ncer

Mou

se e

nhan

cer

http://cs273a.stanford.edu [Bejerano Fall16/17] [Phil Reno, David Kingsley] 42


Androgen Receptor

Cell

AndrogenReceptor

Nucleus

Testosterone

AR+Tdimer

Androgen Receptor

Human

Chimp


Androgen responsiveness in domains of expressionSensory whiskers Penile spines

Galago

Sen

sory

whi

sker

leng

th (m

m)

[Dixson, 1976]

Mice with Ar coding region mutations lack penile spines

[Murakami, 1987]

Sensory Penilewhiskers spines

44

[Ibrahim & Wright 1983]

Could sequence loss lead to tissue gain?

• hCONDELs enriched for suppressors of cell proliferation or cell migration expressed in cortex (P=1.3 x 10-3)

Non-human mammals Humans

( )

Suppressproliferation

Do notsuppressproliferation


The Genotype - Phenotype divide


Can we find evolutionary patterns that are distinct enough to be phenotypically revealing?

Species A

Species B

Problem #1:

Too many nucleotide changes between any pair of related species (or individuals).

The vast majority of these are near/neutral.

Genotype -> Phenotype screens


deleted!

Chimp

Human

conserved

Define a “dramatic” (non-neutral) genomic scenario:

hCONDEL

[McLean, Pollen, Reno et al, 2011]

Problem #2:

What is the phenotype?

Testing is Exciting… and Humbling


These are “wild rides”: Often not what we expected, Often not what we can understand.Are we looking at the right place?Did we test at the right time?

[McLean, Pollen, Reno et al, 2011]

We are creating the humanized mice KOs

What about a tree of related species?


What if we could find evolutionary patterns that were distinct enough to be phenotypically revealing?

ancestor

Species A

Species H

Genomes:Inherited and Modified.

Traits:Come and Go.

Species B...

ancestral trait information

Trait information is no longer under selection

Erodes away over evolutionary time

ancestor

What happens when an ancestral trait “goes”?

Phenotype Genome





ancestor

Phenotype Genome

A lot of DNA and many traitsvary between any two species.





ancestor

Phenotype Genome


A lot of DNA and many traitsvary between any two species.

What about independent trait loss?

vitamin C synthesis, tail, body hair,dentition features, etc. etc.




ancestor

Phenotype Genome



matches trait presence/absence pattern

The PG screen

[Hiller et al., 2012a] 54

The PG screen


Capture the independent genomic switch from purifying selection neutral evolution

in all and only the trait loss species.

Robust to: Different trait disabling times.Different trait disabling mutations.

Forward Genetics:Search for mutations that segregate with a trait of interest

Forward Genomics:Search for regions that are lost only in species lacking the trait

phenotype genotype


Branding ;-)

But does it work?

Vitamin C Synthesis

synthesize vitamin C cannot synthesize vitamin C

rats & mice human


vitamin C synthesis was lost3-4 times independently in mammalian evolution


The Vitamin C synthesis “phenotree”

Fwd Genomics asks:Do one or moregenomic locilook like THAT?

We quantify divergence by comparing sequences to the reconstructed ancestral sequence

reconstruct ancestral sequence

ancestor

59

species 1

outgroup

species 2

ACCCTATCGATT-CA

ACCCTATCGATTGCA

TCCGTATCG-TT-CA

species 1

species 2

14 identical bases

11 identical bases

Mutation in species 1 or 2?

species 1species 2

93%79%

percent of identical bases: more diverged

Insertion in species 1 or deletion in species 2 ?

ACCCTATCGATTGCA

TCCGTATCG-TT-CA

ACTCT-TCGATT-AA

Sequencing errors mimic divergence

60

high sequencing error rate

treat species 2 as missing data

sequence quality scores

ancestor ACCCTATCGATT-CAATGG

ACCCTATCGATTGCAAGGGspecies 1

species 2

89% identical bases

61% identical basesTCCGTAACG--T-CTATCG

Assembly gaps mimic divergence

61

?????????species 1

Sanger reads

assembly gap

conserved region

treat species 1 as missing data

species 2species 3species 4species 5

...

Reconstruct the evolutionary history of all conserved regions, coding and non-coding

85%

70%

93%

matrix: 33 species x 544,549 regions

544,549 conserved regions

• Reconstruct ancestral sequence• Measure extant species divergence• Avoid

• Low quality sequence• Assembly gaps

• Seek perfect phenotree match


reconstructancestrallocus

We quantify the match to the vitamin C pattern by counting the number of species that violate the pattern

Percent identity0 100

Percent identity0 100

1 violation

2 violations63http://cs273a.stanford.edu [Bejerano Fall16/17]

8

Regions matching the vitamin C trait are clustered

these conserved regions are all exons of a single gene

544,549 conserved regions

no. o

f vio

latin

g sp

ecie

s

012345

7

910

6

no match

perfect match


This gene is more diverged in all non-vitamin C synthesizing species


What is the function of this gene ?


encodes the enzyme responsible for vitamin C biosynthesis

Vitamin C pattern

Gulo - gulonolactone (L-) oxidase

33 genomes X 544,549 regions

Note: 1. No likely shared

disabling mutation.2. We learned about

both evolution and function.

The Power of Forward Genomics


Vitamin C pattern

Gulo - gulonolactone (L-) oxidase

33 genomes X 544,549 regions

Forward genomics works.Can it work for continuous traits?With only two independent losses?And many unknown values?

BileBile is a fluid produced by the liver that aids the digestion of lipids in the small intestine.


Bile Phospholipids


Different mammals have remarkably different levels of biliary phospholipids:

ABCB4 is a phospholipid transporter


Find “Cure” Models for Human Disease


Human ABCB4 mutations lower patient biliary phospholipid levels to guinea pig levels but are detrimental. Our discovery: Guinea pig and horse have inactivated the Abcb4 gene in their natural state. How can they do it?

create KO gene

try to fix/treat

Natural KO

find nature’s cure!

We have now collected • Million genomic loci by Fifty mammals• Thousands of scored mammalian traits

And we are playing MATCH and TEST.

Reverse Genetics:Pick interesting loci, mutate and try to figure out phenotype/s

Reverse Genomics:Compute independent loss for ALL genomic loci, match to traits

phenotype genotype


Reverse Genomics

Reverse Genomics of Enhancers


Back of an Envelope Wish


Poster Child Example



Date post:	25-Feb-2016
Category:	Documents
Upload:	oral
View:	45 times
Download:	0 times

Multiple Sequence Alignment

Documents