General methods of SNP discovery: PolyBayes

Gabor T. Marth

Department of BiologyBoston CollegeChestnut Hill, MA 02467

General methods of SNP mining – PolyBayes

2. Use sequence quality information (base quality values) to distinguish true mismatches from sequencing errors sequencing error true polymorphism

1. Utilize the genome reference sequence as a template to organize other sequence fragments from arbitrary sources

Two innovative ideas:

Computational SNP mining – PolyBayes

sequence clustering simplifies to database search with genome reference

paralog filtering by counting mismatches weighed by quality values

multiple alignment by anchoring fragments to genome reference

SNP detection by differentiating true polymorphism from sequencing error using quality values

Sequence clustering

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

cluster 1 cluster 2 cluster 3

genome reference

fragments

• Clusters = groups of overlapping sequence fragments matching the genome reference

(Anchored) multiple alignment

• Advantages

• efficient -- only involves pair-wise comparisons

• accurate -- correctly aligns alternatively spliced ESTs

• The genomic reference sequence serves as an anchor

• fragments pair-wise aligned to genomic sequence

• insertions are propagated – “sequence padding”

Paralog filtering

• The “paralog problem”

• unrecognized paralogs give rise to spurious SNP predictions

• SNPs in duplicated regions may be useless for genotyping

Paralogous difference

Sequencing errors

• Challenge

• to differentiate between sequencing errors and paralogous difference

SNP detection

• Goal: to discern true variation from sequencing error

sequencing error polymorphism

genome reference sequence

1. Fragment recruitment (database search)

2. Anchored alignment

3. Paralog identification

4. SNP detection

SNP discovery with PolyBayes

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probability of polymorphism base call,

base quality

a priori polymorphism rate

base composition

depth of coverage

1. The algorithm

Bayesian-statistical SNP detection