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Kevin Charles

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Title: PowerPoint Presentation Author: zdk Last modified by: k Created Date: 11/12/2002 4:52:53 PM Document presentation format: On-screen Show (4:3) – PowerPoint PPT presentation

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Title: Kevin Charles


1
  • Presentation
  • Kevin Charles
  • Paruchuri Padmavathi
  • Department of Computer Science
  • UTSA

2
Introduction
  • GASSST global alignment short sequence search
    tool
  • A Gibbs sampling strategy applied to the mapping
    of ambiguous short-sequence tags.

3
GASSST global alignment short sequence search
tool
4
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

5
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

6
  • 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

7
Tiled Algorithm
8
A Gibbs sampling strategy applied to the mapping
of ambiguous short-sequence tags.
9
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.

10
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11
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.

12
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13
Thank you for listening.
Questions
14
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.
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