Sequence Analysis Methods for comparative Genomics

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Sequence Analysis Methods for comparative Genomics
Philipp Bucher

• Comparative Genomics
• SIB PhD program
• Lausanne Feb 12-16 2007

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Topics covered

• Alignment methods for genomic DNA sequences
• Comparative gene prediction
• Comparative motif finding

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Genome sequence comparison - Challenges

• Very large sequences up to several 100 million
  bp per chromosome
• Only a small percentage of the genomes may be
  alignable.
• Chromosomal rearrangements
• Insertions
• Translocations
• Duplications
• Repetitive elements
• simple repeats
• lineage specific transposons
• ancient transposons
• Processed pseudogenes

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Nothing in biology makes sense except in the
light of evolutionTh. Dobzhansky

• In fact, many contemporary biologists dont care
  about evolution but are more interested in
  molecular function or drug discovery.
• To those, comparative genomics is an extremely
  powerful genome annotation technique.
• Genome sequence comparison may helpful in
• the assembly of a new genomes
• predicting gene structures
• the identification of non-coding regulatory
  elements
• In the recognition of orthologs
• to discarding sequence regions not of interest in
  a particular context
• for structural and functional inferences from
  substitution patterns

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Sequence comparison tools overview

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BLAST heuristics for protein sequence similarity
searches
1. Query compilation For each position of the
query sequence, compile a list of 3-letter words
which match the query with score threshold T
(score depends on substitution matrix). 2. Word
search Search for two non-overlapping word
matches on the same diagonal of the path matrix
within a distance A. 3. Un-gapped match
extension Extend the un-gapped alignment along
the diagonal in both directions unless the score
drops more than X below the maximal score yet
attained. 4. Gapped alignment extension For
un-gapped alignments (segment pairs) exceeding a
threshold score Sg , trigger a regional gapped
alignment until the score drops more than Xg
below the maximal score yet attained. 5.
Statistical evaluation of gapped alignments
Blast uses hard-wired extreme value distribution
parameters ? and K for specific scoring systems
(substitution matrix gap penalties)
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Examples of word match scores with Blosum62 matrix
WWW -gt 33 WWW YYY -gt 21 YYY GGG -gt
18 GGG QQQ -gt 15 QQQ SSS -gt 13 SSS HSW -gt
20 HAW LFY -gt 12 IYY EWD -gt 15 DWE KFI
-gt 8 RYV EWC -gt -11 FDE
Matrix made by matblas from blosum62.iij
column uses minimum score BLOSUM Clustered
Scoring Matrix in 1/2 Bit Units Blocks
Database /data/blocks_5.0/blocks.dat Cluster
Percentage gt 62 Entropy 0.6979, Expected
-0.5209 A R N D C Q E G H I L K
M F P S T W Y V B Z X A 4 -1 -2 -2
0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 -2
-1 0 -4 R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1
-3 -2 -1 -1 -3 -2 -3 -1 0 -1 -4 N -2 0 6 1 -3
0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 3
0 -1 -4 D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3
-3 -1 0 -1 -4 -3 -3 4 1 -1 -4 C 0 -3 -3 -3 9
-3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 -3
-3 -2 -4 Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0
-3 -1 0 -1 -2 -1 -2 0 3 -1 -4 E -1 0 0 2 -4
2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 1
4 -1 -4 G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3
-3 -2 0 -2 -2 -3 -3 -1 -2 -1 -4 H -2 0 1 -1 -3
0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 0
0 -1 -4 I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1
0 -3 -2 -1 -3 -1 3 -3 -3 -1 -4 L -1 -2 -3 -4 -1
-2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 -4
-3 -1 -4 K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1
-3 -1 0 -1 -3 -2 -2 0 1 -1 -4 M -1 -1 -2 -3 -1
0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1 -3
-1 -1 -4 F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0
6 -4 -2 -2 1 3 -1 -3 -3 -1 -4 P -1 -2 -2 -1 -3
-1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 -2
-1 -2 -4 S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1
-2 -1 4 1 -3 -2 -2 0 0 0 -4 T 0 -1 0 -1 -1
-1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 -1
-1 0 -4 W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1
1 -4 -3 -2 11 2 -3 -4 -3 -2 -4 Y -2 -2 -2 -3 -2
-1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 -3
-2 -1 -4 V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1
-1 -2 -2 0 -3 -1 4 -3 -2 -1 -4 B -2 -1 3 4 -3
0 1 -1 0 -3 -4 0 -3 -3 -2 0 -1 -4 -3 -3 4
1 -1 -4 Z -1 0 0 1 -3 3 4 -2 0 -3 -3 1 -1
-3 -1 0 -1 -3 -2 -2 1 4 -1 -4 X 0 -1 -1 -1 -2
-1 -1 -1 -1 -1 -1 -1 -1 -1 -2 0 0 -2 -1 -1 -1
-1 -1 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4
-4 -4 -4 -4 -4 -4 -4 -4 -4 -4 1
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Blast Principle of two-hit heuristic
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Blast Gap extension.
a Area considered by gap extension algorithm.
Computation starts at the center of the highest
scoring segment pair obtained after word-hit
extension. b Path of optimal local alignment
found. c Optimal local alingment.
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Why is blastn less sensitive than blastp ?

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Blastz overview

• Searches for multiple gapped local alignments
• ordering constraints may apply
• Same 3-step procedure as blast
• word hit, ungapped extension, gapped extension
• Word hits
• exact (default 8) or patterned, e.g.
  1110100110010101111 (1 indicates relevant
  positions).
• optionally transition-tolerant (AG, CT count as
  matches)
• two hits strategy like blastp
• Species-pair specific base substitution matrix
• Alignment scores dynamically adjusted for local
  complexity
• Between adjacent alignments search repeated with
  more sensitive parameters
• Output
• Coordinates of local alignments and scores
• For each alignment, coordinates and scores of
  HSPs (high-scoring segment pairs)

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Comparative gene prediction

• Categories of gene prediction programs
• Single-genome ab initio (classical gene
  prediction problem)
• Dual-genome ab initio (comparative gene
  prediction)
• EST-, mRNA, and protein-based methods (same or
  related species)
• Anything goes (e.g. ENSEMBL gene annotation
  pipeline)
• Why should comparative gene prediction be more
  effective
• Consensus of two predictions more reliable than
  one
• Coding regions more conserved than non-coding
  regions (??)
• Coding regions have different substitution and
  indel patterns (frame conservation)
• Splice junctions need to be conserved as well
• Some limitations
• Genomes need to be reasonably diverse
• Gene structures may not be conserved

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Gene prediction statement of the problem

• Given a genome sequence
• predict the structure of all transcripts
• Predict the structure of the coding part of all
  transcripts.
• Performance criteria
• of correctly predicted/missed genes
• of correctly predicted/missed exons
• of correctly predicted/missed coding
  nucleotides
• Further complications
• It is not known in advance how many genes a
  sequence contains
• The sequence may start or end in the middle of a
  gene
• Alternative splicing

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GENSCAN, and example of an ab initio gene finding
algorithm

• Principle GENSCAN finds the optimal parse of a
  sequence
• A parse is a succession of
• intergenic regions
• 5UTR (untranslated regions)
• Exons
• Introns
• 3UTRs
• Evaluation of alternative parses with the aid of
• Weight matrices or similar models for sites
  promoters, translation starts, splice donors and
  acceptors, translation stops, polyadenylation
  sites.
• Interpolated Markov chains (3-periodic HMMs), and
  length distributions for exons, introns, 5 and
  3 UTRs, and intergenic regions.

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GENESCAN model (variant HMM)
E0 exon plus starting in phase 0 I0 exon
on starting in phase 1
All named elements except promoters are scored by
Markov chains or interpolated Markov
chains. Transitions and promoters are scored by
weight matrices or similar descriptors
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Gene Prediction The Intron Phase Problem
ATGGGTCCAgttggtgcccttagGTGTTCGAgtgagccacagCACCTGGA
AG MetGlyPro..............ValTyrAs...........pThrT
rpLys
EInit
I0
E0
I2
E2
Introns can be inserted at three different codon
positions Introns inserted between codons have
phase 0 Introns inserted after the first codon
base have phase 1 Introns inserted after the
second codon position have phase 2 Exons starting
between codons have phase 0 Exons starting after
the first codon base have phase 1 Exons starting
after the second codon base have phase
2 Important rule An intron of a given phase
must be followed by an exon of the same phase.
Otherwise, the protein will not be translated in
the correct frame.
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Scoring target Scoring method F 5UTR simpl
e Markov chain F ? Einit Kozak
consensus weight matrix Einit First
exon interpolated Markov chain Einit ? I1 Splice
donor weigh matrix I1 Intron phase
1 interpolated Markov chain I1 ? E1 Splice
acceptor weigh matrix - like
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Example of a dual gene prediction program SLAM

• Input
• two sequences
• Approximate alignments generated with Avid or
  Mummer
• Coupled gene prediction and alignment
  optimization
• Based on a generalized pair HMM (GPHMM)
• gene model analogous to GENSCAN
• arbitrary length distributions (two sets of
  durations instead of one)
• exon model based on codon pairs in different
  alignment configurations (new!)
• Splice junctions modeled by VLMMs (non-stationary
  variable-length Markov models)
• Models individually trained for different species
• Other examples of dual gene prediction programs
• SGP2
• TWINSCAN

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Evaluation of gene predictions(from Guigo et al.
2006, Genome Biol. 7S2)
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Evaluation at gene level
Gene transcript evaluation. Computing sensitivity
and specificity at transcript level (a) complete
transcript annotation (b) incomplete transcript
annotation. Transcripts marked with an asterisk
are considered 'consistent with the annotation'
and will be scored as correct.
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Performance Evaluation of gene prediction
programs based on experimentally characterized
EGASP regions
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Computational Approaches to Gene Regulatory
elements
Commonly addressed problems and corresponding
bottlenecks

• Finding a common sequence motif in a set of
  sequences known to contain a binding site to the
  same transcription factor or to confer the same
  regulatory property to an adjacent gene.
  Bottleneck appropriate data sets, computer
  algorithms. New perspective ChIP-chip data.
• Identification of sequence motifs that are
  over-represented at a particular distance from
  transcription initiation sites. Bottleneck large
  sets of experimentally mapped promoters. Hope
  comes from mass genome annotation (MGA) data
  (CAGE, etc.).
• Identification of transcription factor binding
  sites and other sequence elements in DNA
  regulatory sequences. Bottleneck accurate and
  reliable motif descriptions.
• Development of promoter prediction algorithms.
  Current bottleneck large sets of experimentally
  mapped promoters. Perspective MGA data.
• Identification and interpretation of conserved
  non-coding sequence regions between orthologous
  genes of related organisms. Current bottleneck
  concepts and models of gene regulatory regions.

Comparative genomics may help solving these
problems
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Transcription Factor Binding Sites Features and
Facts

• Degenerate sequence motifs
• Typical length 6-20 bp
• Low information content 8-12 bits (1 site per
  250-4000 bp)
• Quantitative recognition mechanism measurable
  affinity of different sites may vary over three
  orders of magnitude
• Regulatory function often depends on cooperative
  interactions with neighboring sites

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Limitations of Motif Discovery Algorithms

• A recent paper show that computational motif
  discovery is disappointingly ineffective

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(No Transcript)
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Bad Performance of Motif Discovery algorithms on
Eukaryotic Benchmark Data Sets (Results from
Tompa et. al. 2005)
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Similar results are obtained with prokaryotic
benchmark datasets
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Comparative Motif Discovery Problem statement
and strategies
Problem 1 Motif discovery Input several sets of
aligned or unaligned sequences. Output a set of
over-, under-, or class-associated motifs Motifs
may be represented as consensus sequences or
weight matrices Case study Xie et al. (2005)
mammalian promoters and 3UTRs Program example
PhyloGibbs (Siddhartan et al. 2005)
http//www.phylogibbs.unibas.ch/cgi-bin/phylogibb
s.pl Problem 2 Motif search Input one set of
aligned or unaligned sequences, a library of
motif definitions (e.g. TRANSAC matrices) Output
conserved binding site locations Program example
ConSite (Sandelin et al. 2004), CisOrtho (Bigelow
et al. 2004)
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New human promoter motifs discovered by
comparative approach
Data from Xie et al. 2005, Nature 434, 338-345