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David HausslerDavid HausslerHoward Hughes Medical Institute Howard Hughes Medical Institute
University of California, Santa CruzUniversity of California, Santa Cruz
Assembly, Comparison, and Annotation Assembly, Comparison, and Annotation of Mammalian Genomesof Mammalian Genomes
Bioinformatics of mammalian genomesBioinformatics of mammalian genomes
•Sequence Assembly
•Genome Browsers: new computational microscopes
•Computing Evolution’s Path: key to understanding function
Assembling the human genomeAssembling the human genome• GigAssembler (Kent)GigAssembler (Kent)
– Built first draft of the human genome from lower-level contigs Built first draft of the human genome from lower-level contigs produced by Phrap( P. Green)produced by Phrap( P. Green)
• Celera Assembler (Myers/Sutton)Celera Assembler (Myers/Sutton)
– First mammalian whole genome shotgun assemblerFirst mammalian whole genome shotgun assembler
Outgoing UCSC internet traffic (green) for year 2000. Main peak is
activity on July 7, 2000 when human sequence was first posted on the web
Assembling other Assembling other mammalian genomesmammalian genomes
• Arachne (Jaffe/Batzolou, Lander group at MIT)Arachne (Jaffe/Batzolou, Lander group at MIT)
– Built first draft of mouse genome, February 2002Built first draft of mouse genome, February 2002– Mouse also assembled by Phusion assembler (Mullikin, Mouse also assembled by Phusion assembler (Mullikin,
Sanger Centre)Sanger Centre)
• Atlas (Havlak/Chen/Durbin, Gibbs group at Baylor)Atlas (Havlak/Chen/Durbin, Gibbs group at Baylor)
– Built first draft of rat genome, November 2002Built first draft of rat genome, November 2002
Browsers as web-based Browsers as web-based genome microscopesgenome microscopes
• Ensembl Browser (Birney Ensembl Browser (Birney et alet al.).)
• MapViewer (NCBI Mapviewer team)MapViewer (NCBI Mapviewer team)
• UCSC Genome Browser (Kent UCSC Genome Browser (Kent et alet al.) .) http://genome.ucsc.edu, currently getting , currently getting more than 140,000 page requests per daymore than 140,000 page requests per day
Browsers take you from early Browsers take you from early maps of the genome . . .maps of the genome . . .
. . . and at the single base level. . . and at the single base level
caggcggactcagtggatctggccagctgtgacttgacaag caggcggactcagtggatctagccagctgtgacttgacaag
linking to functional informationlinking to functional information
In situ image from I. Dragatsis et al. 1998
Goal: the browser as a continuously-tuned Goal: the browser as a continuously-tuned engine for discoveryengine for discovery
• Multiple streams of high-throughput Multiple streams of high-throughput genomics data generated asynchronously genomics data generated asynchronously
• Data fed into nightly updates of browser Data fed into nightly updates of browser database, analysis and displaydatabase, analysis and display
• Browser becomes a new kind of Browser becomes a new kind of microscope scanning the genome at ever microscope scanning the genome at ever greater detail, dimension, and depthgreater detail, dimension, and depth
Using evolution to find genes Using evolution to find genes and other functional elementsand other functional elements
Mouse conservation pattern in the Mouse conservation pattern in the IGFALS gene on human chr. 16 and a IGFALS gene on human chr. 16 and a known transcription factor binding siteknown transcription factor binding site
R. Weber, L. Elnitski et. al.
At least half of the human genome consistsAt least half of the human genome consistsof relics of retrotransposonsof relics of retrotransposons
DNA of genome Retrotransposon New copy of retrotransposon
RNA
Reverse transcriptase
1. Transcription
2. Translation
4. Synthesis of secondDNA strand
3. Reverse transcriptionof RNA to DNA
5. Insertionof retro-transposonDNA
Ancestral retrotransposonsAncestral retrotransposons
• Retrotransposon relics from our common ancestor Retrotransposon relics from our common ancestor with mouse and other placental mammalswith mouse and other placental mammals
• They cover 22% of the human genomeThey cover 22% of the human genome
• ““AR” sites can be used to study neutral evolution: AR” sites can be used to study neutral evolution: mutation without selectionmutation without selection
• ““AR” sites are similar to “4D” sites in genes AR” sites are similar to “4D” sites in genes (four-fold degenerate sites in codons)(four-fold degenerate sites in codons)
Estimated rate of neutral substitution from AR Estimated rate of neutral substitution from AR
and 4D sites co-varies along the chromosomesand 4D sites co-varies along the chromosomes
R. Hardison, K. Roskin, S, Yang, A. Smit, et al.
By comparison to local neutral substitution By comparison to local neutral substitution rates, it appears that about 5% of the human rates, it appears that about 5% of the human genome may be under purifying selection.genome may be under purifying selection.
K. Roskin, R. Weber, F. Chiaromonte
More species increases power to More species increases power to detect conserved elementsdetect conserved elements
BROWSER SNAPSHOT
Human Chimp Baboon Cat Dog Pig Cow Rat Mouse Chicken Zebrafish Fugu Tetraodon
Data from Eric Green at NGHRI, alignments by Webb Miller
About 4% of CFTR region is under purifying selection
Models of molecular evolutionModels of molecular evolution
Branch length equalsaverage number ofsubstitutions per site
Models of molecular evolutionModels of molecular evolution
Branch length equalsaverage number ofsubstitutions per site
A
Models of molecular evolutionModels of molecular evolution
Branch length equalsaverage number ofsubstitutions per site
A
A
G
Models of molecular evolutionModels of molecular evolution
Branch length equalsaverage number ofsubstitutions per site
A
A
G
A
T
G
G
Models of molecular evolutionModels of molecular evolution
Branch length equalsaverage number ofsubstitutions per site
A
A
G
A
T
G
G
T
T
Models of molecular evolutionModels of molecular evolution
Branch length equalsaverage number ofsubstitutions per site
A
A
G
A
T
G
G
T
T
Continuous-time Markov models ofContinuous-time Markov models ofmolecular evolution can be used tomolecular evolution can be used tocalculate p-values for conservationcalculate p-values for conservation
Conditional probability Conditional probability distribution on each distribution on each branch has the form branch has the form P = eP = eQtQt where t is the where t is the time and Q is a 4 by 4 time and Q is a 4 by 4 rate matrix.rate matrix.
Parameterizations of Q: JC, …, HKY, REV, UNRParameterizations of Q: JC, …, HKY, REV, UNR
Calculation of p-valuesCalculation of p-values
• p-value is probability of getting a given parsimony score or better, using a cont. time Markov model of evolution• p-values are calculated recursively for the two subtrees, for all possible values of parsimony score and ancestral bases for each subtree• data for subtrees is combines to produce p-value at root
Method developed by Mathieu Blanchette and Martin Tompa
Calculation of p-valuesCalculation of p-values
• p-value is probability of getting a given parsimony score or better, using a cont. time Markov model of evolution• p-values are calculated recursively for the two subtrees, for all possible values of parsimony score and ancestral bases for each subtree• data for subtrees is combines to produce p-value at root
Method developed by Mathieu Blanchette and Martin Tompa
Calculation of p-valuesCalculation of p-values
• p-value is probability of getting a given parsimony score or better, using a cont. time Markov model of evolution• p-values are calculated recursively for the two subtrees, for all possible values of parsimony score and ancestral bases for each subtree• data for subtrees is combines to produce p-value at root
Method developed by Mathieu Blanchette and Martin Tompa
Examples of conserved regionsExamples of conserved regions
Analysis of CFTR region by Mathieu Blanchette
Intronic RNA structural elementIntronic RNA structural element
73kb to ST7 173kb to ST7 1stst exon 73kb to ST7 2 exon 73kb to ST7 2ndnd exon exon
~90 bp conserved stem~90 bp conserved stem
Mathieu Blanchette
Modeling different modes of Modeling different modes of substitutionsubstitution
We want to pay attention to how elements are conserved, not just
that they are conserved
Context mattersContext matters
substitution rate matrix for non-coding dinucleotidessubstitution rate matrix for non-coding dinucleotides
Adam Siepel
Dinucleotide and trinucleotide Dinucleotide and trinucleotide models fit substitution data from models fit substitution data from
neutral regions much betterneutral regions much better
Improvement in log likelihood on AR sites for higher order models of base substitution
Adam Siepel
Method also produces improved Method also produces improved models of codon evolutionmodels of codon evolution
Adam Siepel
Phylogenetic HMMsPhylogenetic HMMs
TAATGGTA…CCAGTTA…GCAGAGT…
CCATGGTT…CCCGTAG…CCAGAGT…
TAATGGTA…CCGGTTA…ACAGAGT…
TTATGGTA…CCTGTTA…ACAGAGT…
CGATGGTG…CCGGTCG…ACAGAGC…CTATGGTC…CCTGTTA…TCAGAGC…GTATGGTC…CCTGTCG…TCAGAGC…CCATGGTT…CCCGTAG…CCAGAGT…
human
baboon
mouse
dog
cat
cow
pig
chicken
Adam Siepel
Human splice variants of ZNF278 conserved in mouseHuman splice variants of ZNF278 conserved in mouseChuck Sugnet
Comparative cDNA analysis finds Comparative cDNA analysis finds alternatively spliced genesalternatively spliced genes
Molecular evolution is moreMolecular evolution is morethan base substitutionsthan base substitutions
• InsertionsInsertions
• DeletionsDeletions
• DuplicationsDuplications
• InversionsInversions
• RearrangementsRearrangements
Genome-wide human-mouse Genome-wide human-mouse alignments reveal a host of multibase alignments reveal a host of multibase
evolutionary eventsevolutionary events
A 15,000 base inversion on human A 15,000 base inversion on human chromosome 7 containing two geneschromosome 7 containing two genes
J. Kent, W. Miller, R. Baertsch
Hot spots for rearrangements?Hot spots for rearrangements?
At finer resolution, many thousands of syntenic blocks between human and mouse are found, and short blocks are clustered in clumps
J. Kent, W. Miller, R. Baertsch
Grand challenge of humanGrand challenge of humanmolecular evolutionmolecular evolution
Reconstruct the evolutionary historyof each base in the human genome
CreditsCredits Thanks to Jim Kent, Terry Furey, Mathieu Blanchette, Adam Thanks to Jim Kent, Terry Furey, Mathieu Blanchette, Adam
Siepel, Chuck Sugnet, Ryan Weber, Krishna Roskin, Mark Siepel, Chuck Sugnet, Ryan Weber, Krishna Roskin, Mark Diekhans, Robert Baertsch, Matt Schwartz, Angie Hinrichs, Diekhans, Robert Baertsch, Matt Schwartz, Angie Hinrichs, Donna Karolchik, Heather Trumbower, Yontao Lu, Fan Hsu, Donna Karolchik, Heather Trumbower, Yontao Lu, Fan Hsu, Daryl Thomas, Jorge Garcia, Patrick Gavin and Paul Tatarsky Daryl Thomas, Jorge Garcia, Patrick Gavin and Paul Tatarsky at UCSCat UCSC
Francis Collins, Bob Waterston, Eric Lander, Richard Gibbs, Francis Collins, Bob Waterston, Eric Lander, Richard Gibbs, Eric Green, Elliot Margulies, David Kulp, Alan Williams, Eric Green, Elliot Margulies, David Kulp, Alan Williams, Ray Wheeler, Webb Miller, Ross Hardison, Scott Schwartz, Ray Wheeler, Webb Miller, Ross Hardison, Scott Schwartz, Francesca Chiaromonte, Thomas Pringle, Greg Schuler, Francesca Chiaromonte, Thomas Pringle, Greg Schuler, Deanna Church, Steve Sherry, Ewan Birney, Michelle Clamp, Deanna Church, Steve Sherry, Ewan Birney, Michelle Clamp, David Jaffe, Asif Chinwalla, Jim Mullikin,Tim Hubbard, David Jaffe, Asif Chinwalla, Jim Mullikin,Tim Hubbard, Arian Smit, Nick Goldman, Barbara Trask, Ian Dunham, Sean Arian Smit, Nick Goldman, Barbara Trask, Ian Dunham, Sean Eddy, Evan Eichler, David Cox, Carol Bult, and many other Eddy, Evan Eichler, David Cox, Carol Bult, and many other outside collaboratorsoutside collaborators