Computational Analyses of Eukaryotic Gene Evolution

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Computational Analyses ofEukaryotic Gene
Evolution

• Sourav Chatterji
• souravc_at_cs.berkeley.edu
• May 18, 2006

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• White Blood Cells From Cancer-resistant
• Mice Cure Cancers In Ordinary Mice
• (Science Daily News. May 9, 2006)

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• The cancer-resistant mice all stem from a single
  mouse discovered in 1999.
• The original studies showed that the resistance
  is inherited.
• About half of the progeny inherit the resistance.
• Cui and his colleagues believe that the
  resistance results from a mutation in a single
  gene and are attempting to find it, but that has
  proved frustrating.
• Mouse genome published in 2002.

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Understanding the biology of genes

• Draft Human Genome (2001).
• 30,000-40,000 genes
• Finished Human Genome (2004).
• 20,000-25,000 genes
• A Third Approach to Gene Prediction Suggests
  Thousands of Additional Human Transcribed
  Regions (Glusman et al. 2006).

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Understanding the biology of genes

• Why is it a hard problem?
• Pseudogenes.
• Long Introns.
• Conserved Non Coding Sequences (CNSes).
• Determination of Gene Boundaries.
• Alternative Splicing.

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Understanding the biology of genes

• what's needed is knowledge about why genes have
  the characteristics they do Pennisi, 2003
• Studying gene evolution should help.

Illustration Terry E Smith
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State of Sequencing Projects

• 25 Mammalian Genomes.
• Sequencing around H.sapiens.
• 12 Fly Genomes.
• Sequencing around D. melanogaster.
• 5 Worm Genomes.
• Sequencing around C. elegans.

Source NHGRI
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Outline

1. Reference Based Annotation.
2. The GeneMapper Algorithm.
3. Annotation of Fruitfly Genomes.
4. Evolution of Gene Structure.

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Outline

1. Reference Based Annotation.
2. The GeneMapper Algorithm.
3. Annotation of Fruitfly Genomes.
4. Evolution of Gene Structure.

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Source http//rana.lbl.gov/drosophila/
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Species ESTs mRNAs RefSeq
D. melanogaster 383407 19931 19967
D. simulans 5013 80 None
D. yakuba 11015 808 None
D. erecta None 6 None
D. ananassae None 11 None
D. pseudoobscura 35042 40 None
D. mojavensis 361 2 None
D. virilis 663 41 None
D. grimshawi None None None
Source UCSC browser
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Reference Based Annotation

• How to accurately annotate the newly sequenced
  genomes?
• Transfer annotations from a well-studied
  reference genome.
• Implicitly creates a data set for studying the
  evolution of genes.

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Protein Alignment Approach
Reference Protein
Genomic Sequence

• Procrustes Gelfand at al. 1996
• GeneWise Birney at al. 2004
• Integral part of the ENSEMBL gene annotation
  pipeline.
• Not aware of exon/intron boundaries.
• Accuracy decreases when sequence identity is low.

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Similarity Based Approach
Reference Gene
Target Sequence

• Projector Meyer and Durbin 2004
• Predicts the global gene structure using a pair
  HMM.
• Uses heuristics to decrease the search space.
• GeneMapper
• Uses a bottom up algorithm for predicting the
  gene structure.
• Not suitable if the exon/intron structure of the
  gene has diverged a lot.

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Outline

1. Reference Based Annotation.
2. The GeneMapper Algorithm.
3. Annotation of Fruitfly Genomes.
4. Evolution of Gene Structure.

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The GeneMapper Algorithm

• Bottom Up Algorithm
• Predict the ortholog of each reference exon in
  the target sequence.
• Join exon predictions together to predict gene
  structure.
• Multiple Species GeneMapper
• Uses all available information if the gene has to
  be mapped into multiple target species.
• Uses a profile based approach to get more
  accurate annotations in evolutionary distant
  species.

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Mapping Exons Accurately

• Predicting the ortholog of a reference exon in
  the target sequence
• Accurately model the evolution of exons.
• Use StrataSplice to model splice sites.

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Mapping Exons Accurately

• Use version of Smith Waterman algorithm.
• Exact Optimization feasible.
• Green edges to model the evolution of codons.
• Uses 64 64 COD distance matrices.
• Red edges to allow for frameshifts.

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Multiple Species GeneMapper

• Generates a gene profile of orthologous genes.
• A more complete characterization than a single
  gene.
• Each column contains an alignment of
  orthologous codons.
• Special columns of width 1 are allowed to
  account for frameshifts and sequencing errors.

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Multiple Species GeneMapper

• Iteratively builds a gene profile to capture the
  characteristics of the gene.
• The profile helps us map exons more accurately to
  evolutionary distant species.
• ExonAligner is modified to align the profile with
  the target sequence.
• Uses different models for conserved and
  non-conserved codons.

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Exploiting Phylogeny Species Hopping
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Exploiting Phylogeny Species Hopping
Map gene into closest species
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Exploiting Phylogeny Species Hopping
Map gene into closest species
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Exploiting Phylogeny Species Hopping
Add the prediction to the profile
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Exploiting Phylogeny Species Hopping
Use profile to map gene into the next closest
species
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GeneMapper Performance
GeneWise Projector GeneMapper
Gene Sn. 61.3 59.9 81.7
Gene Sp. 60.8 59.5 81.7
Exon Sn. 92.8 94.2 97.2
Exon Sp. 93.4 90.5 97.8
Nucl Sn. 99.86 99.78 99.88
Nucl Sp. 99.91 99.70 99.94
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GeneMapper Performance
GeneWise Projector GeneMapper
Gene Sn. 61.3 59.9 81.7
Gene Sp. 60.8 59.5 81.7
Exon Sn. 92.8 94.2 97.2
Exon Sp. 93.4 90.5 97.8
Nucl Sn. 99.86 99.78 99.88
Nucl Sp. 99.91 99.70 99.94
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Sources of Errors

• Highly Divergent Exons
• Exon Splitting
• Improved handling in latest version.
• Assembly and Sequencing Errors

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Outline

1. Reference Based Annotation.
2. The GeneMapper Algorithm.
3. Annotation of Fruitfly Genomes.
4. Evolution of Gene Structure.

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Annotating the Fruitfly Genomes
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The Fruitfly Genomes
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The Annotation pipeline

• Construct whole genome homology maps of nine
  fruitfly genomes by using Mercator Dewey and
  Pachter, unpublished
• Extend the map using extrapolation.
• Use the map as a guide to transfer D.
  melanogaster RefSeq annotations by using
  GeneMapper.

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Generating Homology Maps
Waterston et al. , 2002
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Issues With Homology Maps

• Gene duplications
• Distinguishing between paralogs and orthologs.
• Incomplete coverage.
• Low sequence identity.
• Insufficient Anchor Coverage
• Micro-rearrangements.

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Fruitfly Annotations
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Fruitfly Annotations

• Annotations of the 11 fruitfly genomes
• http//bio.math.berkeley.edu/genemapper/CAF1_genes
  _v0.2
• Browsable on the UCSC browser
• http//bio.math.berkeley.edu/genemapper/fruitfly.h
  tml
• Gene Alignments
• http//bio.math.berkeley.edu/genemapper/CAF1_aln/

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Annotation Statistics
Species Transcripts Unique Complete
D. melanogaster 19697 13488 N/A
D. simulans 18274 12353 17074
D. yakuba 18551 12594 17614
D. erecta 18700 12682 18203
D. ananassae 17398 11561 15858
D. pseudoobscura 16651 10867 14595
D. mojavensis 15908 10214 13192
D. virilis 16032 10305 13451
D. grimshawi 15700 10063 13107
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Outline

1. Reference Based Annotation.
2. The GeneMapper Algorithm.
3. Annotation of Fruitfly Genomes.
4. Evolution of Gene Structure.

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Exon Intron Gene Structure

• Phenomenon due to exon intron structure
• Alternative Splicing.
• Regulation through UTRs.
• Nonsense Mediated Decay.
• Diversity in Gene Structure
• Prokaryotic genes are intronless.
• Tremendous Diversity within Eukaryotes.
• Might have been responsible for the formation of
  nucleus Martin and Koonin, 2006.

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Evolution of Gene Structure

• Introns early Theory
• Introns lost in prokaryotic evolution.
• Introns late Theory
• Spliceosomal introns invented during eukaryotic
  evolution.
• Reality probably in middle.
• Most introns are fairly new.
• Eukaryotic ancestor had spliceosomal mechanisms.

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Evolution of Gene Structure

• Conservation of intron positions among various
  eukaryotic cladesRogozin et al. 2003
• Both loss and gain of introns in various
  eukaryotic lineages Rogozin et al. 2003
• Intron preferentially lost near 3 ends of genes
  Roy and Gilbert, 2005
• Excess of 5 introns in eukaryotic genomes Lin
  and Zhang, 2005

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Mechanisms of Intron Gain

• Duplication Theory Tarrio et al. 1998.
• New Introns are formed by duplication of existing
  introns.
• Transposons Theory Crick 1979
• Novel Introns arise by insertion of transposons.

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Mechanisms of Intron Loss

• Recombination Theory Bernstein et al., 1983
• Recombination of reverse transcribed mRNA
  transcript with the genome results in the loss of
  introns.
• Predicts intron loss at 3 end.
• Deletion Theory Kent and Zahler, 2000
• Intron loss by genomic deletion.
• Predicts inexact intron loss.

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Finding Intron Gain/Loss

• Two criteria for detecting credible intron gain
  Logsdin et al. 1998
• Strong Phylogenetic Support.
• Source of Intron DNA should be identifiable.
• Hard to satisfy this criteria as there are few
  constraints on intron evolution.

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Finding Intron Gain/Loss
The Melanogaster Group
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Finding Intron Gain/Loss
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Finding Intron Gain/Loss
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Finding Intron Gain/Loss
Present
Absent
Present
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Finding Intron Gain/Loss
Absent
Absent
Present
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Finding Intron Gain/Loss

• Determination of Intron Gain
• GeneMapper allows for a single inserted intron in
  a exon.
• Determination of Intron Loss
• Minimum intron length for maintaining splicing
  reaction.

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Finding Intron Gain/Loss

• Used GeneMapper to search for lost/inserted
  introns in all FlyBase genes.
• Found 231 inserted introns.
• Found 105 deleted introns.

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Lengths of Inserted Introns
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Phases of Inserted Introns
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Mechanisms of Intron Loss

• Recombination Hypothesis
• Predicts that intron loss should occur at the 3
  end.
• Testing the hypothesis
• 53 of the 105 lost introns are either the last or
  penultimate intron at the 3 end.
• 82 of 105 lost introns are in the 3 third.
• 94 of 105 lost introns are in the 3 half.

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Mechanisms of Intron Loss

• Deletion Hypothesis
• Predicts inexact intron loss.
• Testing the hypothesis
• Look at gene alignments around fusion events.
• There doesnt appear to be many intron losses
  through inexact deletions.

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Mechanisms of Intron Gain

• Duplication Hypothesis
• Introns formed by duplication of existing
  introns.
• Testing the hypothesis
• Use BLAST to search for matches with existing
  Melanogaster introns.
• No duplications found.

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Mechanisms of Intron Gain

• Transposon Hypothesis
• Testing the hypothesis
• Searched for TEs using RepeatMasker.
• 3 of the 231 new introns were found to be
  transposons.
• It seems that even though some introns are formed
  by insertion of TEs into genes, this mechanism is
  used very rarely.

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Conclusions

• Large scale sequencing projects provide an
  unprecedented opportunity to study genome
  evolution.
• We have developed computational tools to study
  genome evolution.
• These tools can be used to prove or disprove
  theories about gene evolution.