Kevin Charles

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• Presentation
• Kevin Charles
• Paruchuri Padmavathi
• Department of Computer Science
• UTSA

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Introduction

• GASSST global alignment short sequence search
  tool
• A Gibbs sampling strategy applied to the mapping
  of ambiguous short-sequence tags.

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GASSST global alignment short sequence search
tool
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Current Sequence Aligners

• Next-generation sequencing machines are able to
  produce huge amounts data
• Common techniques often restrict indels in the
  alignment to improve speed
• Flexible aligners are too slow for large-scale
  applications

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GASSST

• GASSST is thus 2-foldachieving high performance
  with no restrictions on the number of indels with
  a design that is still effective on long reads.
• This method compares with BLAST, with a new
  efficient filtering step that discards most
  alignments coming from the seed phase
• Carefully designed series of filters of
  increasing complexity and efficiency to quickly
  eliminate most candidate alignments
• Algorithm manipulates pre-computed small table of
  64KB which easily fits into the cache memory

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• Last step, extend, receives alignments that
  passed the filter step.
• It is computed using a traditional banded NW
  algorithm. Significant alignments are then
  printed with their full description.
• Provides a lower bound only

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Tiled Algorithm
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A Gibbs sampling strategy applied to the mapping
of ambiguous short-sequence tags.
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Gibbs Sampling for Ambiguous Seq

• Maps ambiguous tags to individual genomic sites.
• Mapping of ambiguous tags
• Calculating LR for each site
• For each map site the number of co-located tags
  are counted. This count is used for calculate
  likelihood ratio
• Higher likelihood ratio, higher confidence,
  increases non-linearly with tag counts
• LR is calculating conditional prob
• Two steps are circular, led to adopt Gibbs
  Sampling.
• For some set of ambiguous tags (s), it reaches
  relative entropy between Ps and Pn.

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Comparison

• Compared against MAQ s/w method, which randomly
  selects a site for each ambiguous tag.
• Comparison on the eight seq tag libraries (20 bp
  tags, 35 bp tags) shows that Gibbs Sampling
  correctly maps from 49 to 71, MAQ method 8 to
  23.

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Thank you for listening.
Questions
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Results
We found that GASSST achieves high sensitivity in
a wide range of configurations and faster
overall execution time than other
state-of-the-art aligners.