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SUMMARY:Model-based base-calling and de novo error correction algorithms f
 or short-read sequencing - Song\, Y (UC Berkeley)
DTSTART:20100713T160000Z
DTEND:20100713T163000Z
UID:TALK25492@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:An important computational challenge associated with recent ad
 vances in sequencing technology is to develop efficient methods that can e
 xtract accurate sequence information from raw instrument data. In this tal
 k\, I will describe a couple of algorithms which significantly improve the
  accuracy of short-read sequence data\, particularly in the later cycles o
 f a sequencing run. First\, I will describe a novel model-based base-calli
 ng algorithm for the Illumina sequencing platform. Being founded on the to
 ols of statistical learning\, our approach is flexible enough to incorpora
 te various features of the sequencing process. In particular\, it can easi
 ly incorporate cycle-dependent parameters and model residual effects. I wi
 ll then describe an efficient algorithm for correcting base-call errors. O
 ur algorithm does not require a reference genome and it significantly outp
 erforms previous error correction algorithms under various realistic setti
 ngs. Finally\, I will demonstrate how improved data quality resulting from
  our algorithms may facilitate de novo assembly and SNP calling.
LOCATION:Seminar Room 1\, Newton Institute
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