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Presentation : Kevin Charles Paruchuri Padmavathi Department of Computer Science UTSA

Presentation : Kevin Charles Paruchuri Padmavathi Department of Computer Science UTSA. Introduction. GASSST: global alignment short sequence search tool A Gibbs sampling strategy applied to the mapping of ambiguous short-sequence tags. GASSST: global alignment short sequence search tool.

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Presentation : Kevin Charles Paruchuri Padmavathi Department of Computer Science UTSA

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  1. Presentation : Kevin Charles Paruchuri Padmavathi Department of Computer Science UTSA 11/1/2010

  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-fold—achieving 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 (σ), it reaches relative entropy between Ps and Pn.

  10. 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%.

  11. Thank you for listening. Questions

  12. 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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