In search of true reads: A classification approach to next generation sequencing data selection

Wijaya, Edward; Pessiot, Jean-François; Frith, Martin C.; Fujibuchi, Wataru; Asai, Kiyoshi; Horton, Paul

doi:10.1109/bibmw.2010.5703862

Cited by 1 publication

(1 citation statement)

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“…Fortunately two correction tools have been designed for transcriptome analysis: FreClu ( 7 ) and recount ( 16 ). recount is designed to correct sequence count biases [including for those sequences which should have a zero count ( 17 )] resulting from sequencing error in Solexa/Illumina reads. It uses a probabilistic model to estimate the true expression reads based on their counts and quality scores, without using a reference genome ( Figure 1 ).…”

Section: Introductionmentioning

confidence: 99%

RecountDB: a database of mapped and count corrected transcribed sequences

Wijaya

Frith

Asai

et al. 2011

Nucleic Acids Research

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The field of gene expression analysis continues to benefit from next-generation sequencing generated data, which enables transcripts to be measured with unmatched accuracy and resolution. But the high-throughput reads from these technologies also contain many errors, which can compromise the ability to accurately detect and quantify rare transcripts. Fortunately, techniques exist to ameliorate the affects of sequencer error. We present RecountDB, a secondary database derived from primary data in NCBI's short read archive. RecountDB holds sequence counts from RNA-seq and 5′ capped transcription start site experiments, corrected and mapped to the relevant genome. Via a searchable and browseable interface users can obtain corrected data in formats useful for transcriptomic analysis. The database is currently populated with 2265 entries from 45 organisms and continuously growing. RecountDB is publicly available at: http://recountdb.cbrc.jp.

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Section: Introductionmentioning

confidence: 99%