Sequence Variation Informatics

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Sequence Variation Informatics
BI420 Introduction to Bioinformatics
Gabor T. Marth
Department of Biology, Boston College marth_at_bc.edu
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Sequence variations

• Human Genome Project produced a reference genome
  sequence that is 99.9 common to each human being

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Why do we care about variations?
phenotypic differences
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Where do variations come from?

• sequence variations are the result of mutation
  events

TAAAAAT
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SNP discovery

• comparative analysis of multiple sequences from
  the same region of the genome (redundant sequence
  coverage)

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Steps of SNP discovery
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Computational SNP mining PolyBayes
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Computational SNP mining PolyBayes
sequence clustering simplifies to database search
with genome reference
multiple alignment by anchoring fragments to
genome reference
paralog filtering by counting mismatches weighed
by quality values
SNP detection by differentiating true
polymorphism from sequencing error using quality
values
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SNP discovery with PolyBayes
genome reference sequence
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Sequence clustering

• Clustering simplifies to search against sequence
  database to recruit relevant sequences

• Clusters groups of overlapping sequence
  fragments matching the genome reference

genome reference
fragments
cluster 1
cluster 2
cluster 3
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(Anchored) multiple alignment

• The genomic reference sequence serves as an
  anchor
• fragments pair-wise aligned to genomic sequence
• insertions are propagated sequence padding

• Advantages
• efficient -- only involves pair-wise comparisons
• accurate -- correctly aligns alternatively
  spliced ESTs

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Paralog filtering -- idea

• The paralog problem
• unrecognized paralogs give rise to spurious SNP
  predictions
• SNPs in duplicated regions may be useless for
  genotyping

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Paralog filtering -- probabilities

• Pair-wise comparison between EST and genomic
  sequence

• Model of expected discrepancies
• Native sequencing error polymorphisms
• Paralog sequencing error paralogous sequence
  difference

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Paralog filtering -- paralogs
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Paralog filtering -- selectivity
375 paralogous ESTs
1,579 native ESTs
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SNP detection

• Goal to discern true variation from sequencing
  error

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Bayesian-statistical SNP detection
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The SNP score
polymorphism
specific variation
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SNP priors

• Polymorphism rate in population -- e.g. 1 / 300
  bp

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Selectivity of detection
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Validation by pooled sequencing
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Validation by re-sequencing
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Rare alleles are hard to detect

• frequent alleles are easier to detect
• high-quality alleles are easier to detect

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The PolyBayes software
http//genome.wustl.edu/gsc/polybayes

• First statistically rigorous SNP discovery tool

• Correctly analyzes alternative cDNA splice forms

• Available for use (70 licenses)

Marth et al., Nature Genetics, 1999
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INDEL discovery
Sequencing chemistry context-dependent
There is no base quality value for deleted
nucleotide(s)
No reliable prior expectation for INDEL rates of
various classes
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INDEL discovery
Deletion Flank
Deletion Flank
Deletion
Insertion
Insertion Flank
Insertion Flank
Q(deletion) average of Q(deletion flank)
Q(insertion flank) gt 35
Q(deletion flank) gt 35
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INDEL discovery

• 123,035 candidate INDELs ( 25 of substitutions)

• Majority 1-4 bp insertion length (1 bp 68 ,
  2bp 13)

• Validation rate steeply increases with insertion
  length

lt
lt
61.7
60.8
14.3
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SNP discovery in diploid traces
usually, PCR products are sequenced from multiple
individuals
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SNP discovery in diploid traces
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SNP mining genome BAC overlaps
inter- intra-chromosomal duplications known
human repeats fragmentary nature of draft data
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BAC overlap mining results
30,000 clones
gtCloneX ACGTTGCAACGT GTCAATGCTGCA
gtCloneY ACGTTGCAACGT GTCAATGCTGCA
25,901 clones (7,122 finished, 18,779 draft with
basequality values)
21,020 clone overlaps (124,356 fragment overlaps)
ACCTAGGAGACTGAACTTACTG
ACCTAGGAGACCGAACTTACTG
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SNP mining projects
1. Short deletions/insertions (DIPs) in the BAC
overlaps
Weber et al., AJHG 2002
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The current variation resource

• The current public resource (dbSNP) contains
  over 2 million SNPs as a dense genome map of
  polymorphic markers

1. How are these SNPs structured within the
genome? 2. What can we learn about the processes
that shape human variability?
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New sequencers for SNP discovery