Literature DB >> 11590101

Detecting the impact of sequencing errors on SAGE data.

J Colinge1, G Feger.   

Abstract

UNLABELLED: SAGE data are obtained by sequencing short DNA tags. Due to the mistakes in DNA sequencing, SAGE data contain errors. We propose a new approach to identify tags whose abundance is biased by sequencing errors. This approach is based on a concept of neighbourhood: abundant tags can contaminate tags whose sequence is very close. The application of our approach reveals that moderately abundant tags can be generated by sequencing errors uniquely. It also allows for detecting correct rare tags. AVAILABILITY: Software is available only to non-profit entities and for non-commercial purposes upon request.

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Year:  2001        PMID: 11590101     DOI: 10.1093/bioinformatics/17.9.840

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  11 in total

1.  Increasing the efficiency of SAGE adaptor ligation by directed ligation chemistry.

Authors:  Austin P So; Robin F B Turner; Charles A Haynes
Journal:  Nucleic Acids Res       Date:  2004-07-06       Impact factor: 16.971

2.  Large-scale production of SAGE libraries from microdissected tissues, flow-sorted cells, and cell lines.

Authors:  Jaswinder Khattra; Allen D Delaney; Yongjun Zhao; Asim Siddiqui; Jennifer Asano; Helen McDonald; Pawan Pandoh; Noreen Dhalla; Anna-Liisa Prabhu; Kevin Ma; Stephanie Lee; Adrian Ally; Angela Tam; Danne Sa; Sean Rogers; David Charest; Jeff Stott; Scott Zuyderduyn; Richard Varhol; Connie Eaves; Steven Jones; Robert Holt; Martin Hirst; Pamela A Hoodless; Marco A Marra
Journal:  Genome Res       Date:  2006-11-29       Impact factor: 9.043

3.  The transcriptome of Toxoplasma gondii.

Authors:  Jay R Radke; Michael S Behnke; Aaron J Mackey; Josh B Radke; David S Roos; Michael W White
Journal:  BMC Biol       Date:  2005-12-02       Impact factor: 7.431

4.  DiscoverySpace: an interactive data analysis application.

Authors:  Neil Robertson; Mehrdad Oveisi-Fordorei; Scott D Zuyderduyn; Richard J Varhol; Christopher Fjell; Marco Marra; Steven Jones; Asim Siddiqui
Journal:  Genome Biol       Date:  2007       Impact factor: 13.583

5.  Using reads to annotate the genome: influence of length, background distribution, and sequence errors on prediction capacity.

Authors:  Nicolas Philippe; Anthony Boureux; Laurent Bréhélin; Jorma Tarhio; Thérèse Commes; Eric Rivals
Journal:  Nucleic Acids Res       Date:  2009-06-16       Impact factor: 16.971

6.  A score system for quality evaluation of RNA sequence tags: an improvement for gene expression profiling.

Authors:  Daniel G Pinheiro; Pedro A F Galante; Sandro J de Souza; Marco A Zago; Wilson A Silva
Journal:  BMC Bioinformatics       Date:  2009-06-06       Impact factor: 3.169

7.  Modeling SAGE tag formation and its effects on data interpretation within a Bayesian framework.

Authors:  Michael A Gilchrist; Hong Qin; Russell Zaretzki
Journal:  BMC Bioinformatics       Date:  2007-10-18       Impact factor: 3.169

8.  Unexpected observations after mapping LongSAGE tags to the human genome.

Authors:  Céline Keime; Marie Sémon; Dominique Mouchiroud; Laurent Duret; Olivier Gandrillon
Journal:  BMC Bioinformatics       Date:  2007-05-15       Impact factor: 3.169

9.  Deep analysis of cellular transcriptomes - LongSAGE versus classic MPSS.

Authors:  Lawrence Hene; Vattipally B Sreenu; Mai T Vuong; S Hussain I Abidi; Julian K Sutton; Sarah L Rowland-Jones; Simon J Davis; Edward J Evans
Journal:  BMC Genomics       Date:  2007-09-24       Impact factor: 3.969

10.  Modeling transcriptome based on transcript-sampling data.

Authors:  Jiang Zhu; Fuhong He; Jing Wang; Jun Yu
Journal:  PLoS One       Date:  2008-02-20       Impact factor: 3.240

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