COMPARATIVE GENOMICS: GENOMEWIDE ANALYSIS IN METAZOAN EUKARYOTES

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COMPARATIVE GENOMICS GENOME-WIDE ANALYSIS IN
METAZOAN EUKARYOTES

• Mao-Feng Ger
• 02/08/2006

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• Completely sequenced genome could be used for
  large-scale comparative analysis
• Effective methods for enormous data are
  objectives
• Main areas in comparative genomics
• Whole-genome alignment
• Gene prediction
• Regulatory-region prediction

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• Introduction
• Whole-genome alignments
• Gene prediction
• Finding regulatory regions
• conclusion

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Sequenced genomes

• In NCBI Genomic Project Database, up to 02/05,
  the of genome project
• Archaea 51 (complete , draft assembly, in
  progress)
• Bactria 821 (complete , draft assembly, in
  progress)
• Eukaryota 391 (complete, draft assembly, in
  progress, organelles)
• In eukaryota, 174 are metazoans.

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Comparative genomics

• Presumption two genomes are from a common
  ancestor, so every bp is the combination of the
  original genome and the action of evolution
• Evolution mutation selection
• Can be represented by a rate matrix
• Selection
• Negative selection
• Neutral selection
• Positive selection

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BLOSUM and PAM rate matrices

• PAM (Percent Accepted Mutation)
• From a set of proteins which are at least 85
  identical
• Numeric suffix means the number of self
  multiplication
• BLOSUM (BLOcks SUbstitution Matrix)
• More empirical and from a large dataset
• Contructed by extracting ungapped segments
  (blocks) from a set of aligned protein families
• Numeric suffix means at least x identity to the
  blocks

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Difficulties in aligning genomes

• Knowing so little about evolution processes that
  wed better focus on functional sequences
• Because genome size differences and genome
  readiness, doing whole-genome alignments is
  pretty difficult
• Recently, there are more and more programs
  dealing with large-scale comparisons. Biologist
  need to know these approaches.

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• Introduction
• Whole-genome alignments
• Gene prediction
• Finding regulatory regions
• conclusion

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Precomputed alignments

• Several groups have made large cross-species
  comparisons
• UC Santa Cruz/PennState (translated BLAT or
  BLASTZ)
• Berkeley Genome Pipeline (BLAT/AVID)
• Ensembl (Phusion/Blastn)

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Whole-genome alignment
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Which genome to align

• Sufficient similarity between genomes enable the
  easy identification of homologous regions
• Example DNA alignment between human and mouse
  resulted in finding new genes and gene regulatory
  regions
• Alignment between human and puffer fish, though
  less easy, is still feasible

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Comparing genomes at protein level

• Not closely related genomes might have problems
  to align genomes at nucleotide level
• At protein level, might lost info which can help
  finding new genes and regulatory sequences
• It is better to start from closely related genomes

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Alignment strategy

• Dynamic programming makes alignment tractable as
  long as you follow a few rules
• Needleman/Wunch align sequences globally

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• Smith/waterman align sequences locally
• No negative score, at least 0
• Tracing to 0
• However, limitations
• Cannot handle rearrangement such as inversion,
  duplication, translocation
• For long sequence (gt10,000 bp), very expensive in
  time and memory usage

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Seeding strategy

• Because correct alignment comes from stretches of
  ungapped matches
• So, first finding a set of ungapped matches
  (seeds)
• Then, extending gapped alignment from where seeds
  happens.
• Loss in sensitivity but reward in time and memory
  usage
• Consecutive model and Two weighted-spaced model

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• Simply put,
• Seeding
• Seeds used as nucleation point for extension
• Dynamic programming to produce gapped alignments
• In this review, we focus on 4 whole-genome
  alignment methods
• BLASTZ
• BLAT/AVID
• BLAT/LAGAN
• WABA

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BLASTZ

• Local aligner, like BLASTZ and BLAT, are highly
  sensitive but less specific
• BLASTZ applies several methods to increase
  sensitivity and specificity
• Seeding instead of 11 consecutive model, new
  BLASTZ used two weighted-spaced model(12 of 19
  and tolerate a transition among 12)
• Extend the seeds without gaps
• Extend gapped alignment down-weight
  low-complexity matches first

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• In mouse-human alignment case, using a specific
  scoring matrix from known mouse-human homology
  region
• A post-processing step is needed to sort out the
  most significant orthologues in multiple matches
• Overall, BLASTZ covered 98 coding region in
  mouse and human genome, indicating it is highly
  sensitive for identifying well-conserved regions

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BLAT

• A local aligner
• Untranslated designed to align cDNA to genomic
  sequences and less effective at lt 90 identity
• Translated mode more effective in genome
  comparison. With mask for repeats and
  low-complexity, the output is faster and cleaner
• Produce a set of ungapped alignments, good in
  speed at the expense of overall sensitivity
• Used in human-pufferfish genome comparison

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Global alignment

• 3 steps
• 1 finding the maximal repeated region
• 2 clean matches first, then repeat matched
• Recursively step 1 and 2
• 3 lt4kb, use NW algorithm gt 4kb, no significant
  alignment

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AVID

• Assumption strictly homologous and no gene
  duplication, inversion, translocation
• When apply to a whole-genome, it needs a
  pre-processing step to identify syntenic regions

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LAGAN

• The advantage of LAGAN over AVID is that it can
  align larger sequences
• Lower memory requirement
• Different matching algorithm in step 1 (not
  necessary to find exact matches)
• In conjunction with BLAT, it has been applied to
  rat-human and rat-mouse comparisons

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MLAGAN

• An extension of LAGAN
• Can do multiple alignment
• Align closely related genomes first, then
  incorporate others in order of phylogenetic
  distance

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WABA

• Take genetic code degeneracy into consideration
• Seeding step based on nucleotides and use two
  weighted-spaced rule 6of8, which allow the third
  position to mismatch
• No extension step, but group proper seeds to
  define homologous regions

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Biological correctness

• There is no best way to do alignments
• Know evolution inadequately to indicate which one
  is superior
• Different algorithms are tuned to different
  genome comparisons (ex. BLASTZ in human-mouse
  case and WABA in C. eleganC. briggsae case)
• Purposes are different
• Align as much as possible, regardless of
  selection (ex. AVID, LAGAN, BLASTZ)
• Identify conserved regions which are under
  selection (ex. BLAT, WABA)

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• Most programs concern maximizing the homologous
  bps, while biologist are interested in conserved
  regions for a function.
• For example, in the mouse-human alignment, 40
  are alignable, but only 6 are under selection
• To make things worse, substitution rate varies
  across genome.

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• Introduction
• Whole-genome alignments
• Gene prediction
• Finding regulatory regions
• conclusion

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Defining gene structure

• Still a challenge because of poor signal-to-noise
  level
• Comparison between closely related genomes could
  provide additional info (dual genome gene
  predictors)
• Different programs are with different
  presumptions, so users need to know the strengths
  and limitations

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Dual genome gene predictor

• To model a specific type of negative selection
• Assumption in alignments, most differences are
  neutral and regions without many mutations are
  conserved.
• Combine other info, such splicing, wobble effect
  to get a better model

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• Can be subdivided into 3 classes
• Pair-HMM take math approach to determine joint
  gene structure and alignment
• Informant approaches fix on alignment to provide
  a better gene prediction
• Exon-finding approaches try to demark the exons
  without splicing them together

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Pair-HMM

• HMM can be used to predict gene structure in a
  single genome
• Paire-HMM can find the most likely path to have
  generate these sequences and provide the
  alignment as well as gene prediction
• Contain two set of orthologous genes

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• Two pair HMM approaches
• SLAM
• DoubleScan
• Both need to optimize parameters for a specific
  species and better efficiency
• SLAM uses AVID method to do rough alignment,
  while DoubleScan uses BLAST

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Informant appraoches

• Use only one sequence to predict gene structure,
  and other one sequence is just for additional
  info by its alighment
• Can predict not only genome sequence but also
  different inputs, like unassembled reads
• 3 methods are available TwinScan, SGP-2,
  GenomeScan
• Need precise parameters, so have their own
  alignment methods (often BLAST)

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Exon prediction

• A carefully parameterized TBLASTX method designed
  to provide specific exon prediction from
  Tetraodon
• Sacrifice a certain amount sensitivity for high
  specificity

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Which method to use

• A particularly successful way to do this work
  used informant methods combined with some simple
  criteria
• Produce a strong prediction in mammalian genomes

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• Introduction
• Whole-genome alignments
• Gene prediction
• Finding regulatory regions
• conclusion

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Finding regulatory regions

• Called phylogentic footprinting (analogous with
  DNAase footprinting)
• Functionally important regions are mutated less
• These cis-regulatory motifs can be dertermined
  by
• Finding common motifs in orthologous sequences
• Aligingn orthologous sequences first, then
  indentifying common regions
• Previously known motifs might help

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Which region to use

• 5 and 3 flanking regions as well as intronic
  sequences
• Difficulties in finding regulatory regions
• 5 end is often the least well-defined, so we
  need experimental evidence of promoters
• Enhancers could be several kilobases away
• In addition to experimental evidence, guessing
  and systematic comparison is needed to potential
  cis-regulatory regions

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Evolutionary issues

• Two orthologous genes might have very different
  regulatory cis-elements, such as paralogous genes
• How evolution affects cis-regulatory motifs is
  still poorly understood
• Intra-mammal comparison show a large amount of
  non-functional conservation, while in
  intravertebrate, it is hard to detect

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• Neutral drift effect could destroy or create
  cis-regulatory sites at a certain rate
• However, expression pattern could remain
  little/no changed
• Raise the possibility of compensate mutation
• Recently, some researchers try to distinguish
  regulatory regions from neutrally evolving DNA by
  genome sequence alignments

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Motif overrepresentation

• Motif finding programs do not consider the
  phylogenetic relationship between homologous
  sequences
• Can be overcome by identifying DNA motifs
  evolving at a slower rate than the surrounding
  sequences
• All motif-finding techniques work better with
  increasing amounts of sequences

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Alignment for finding regulatory region

• Aligning regions of homology in the non-coding
  regions near the orthologous genes
• More and more researches show that cis-regulatory
  elements are in non-conserved regions

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• Introduction
• Whole-genome alignments
• Gene prediction
• Finding regulatory regions
• conclusion

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conclusion

• With more genomes to be sequenced, we can
  investigate the evolution effects on specific
  regions over the entire genomes
• With precomputed data, users can focus at the
  biological level
• 3 advances needed to be made
• Need more genomes to improve the power
• Power can be improved by knowing how negative
  selection works for different functional
  contraints
• Knowing more about positive selection