PEMer: A computational framework with simulation-based error models for inferring genomic structural variants from massive paired-end sequencing data

Jan O. Korbel, Alexej Abyzov, Xinmeng Jasmine Mu, Nicholas Carriero, Philip Cayting, Zhengdong Zhang, Michael Snyder, Mark B. Gerstein

Research output: Contribution to journalArticle

175 Citations (Scopus)

Abstract

Personal-genomics endeavors, such as the 1000 Genomes project, are generating maps of genomic structural variants by analyzing ends of massively sequenced genome fragments. To process these we developed Paired-End Mapper (PEMer; http://sv.gersteinlab.org/pemer). This comprises an analysis pipeline, compatible with several next-generation sequencing platforms; simulation-based error models, yielding confidence-values for each structural variant; and a back-end database. The simulations demonstrated high structural variant reconstruction efficiency for PEMer's coverage-adjusted multi-cutoff scoring-strategy and showed its relative insensitivity to base-calling errors.

Original languageEnglish (US)
Article numberR23
JournalGenome Biology
Volume10
Issue number2
DOIs
StatePublished - Feb 23 2009
Externally publishedYes

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Genomic Structural Variation
genomics
genome
Genome
Genomics
simulation
Databases

ASJC Scopus subject areas

  • Genetics
  • Cell Biology
  • Ecology, Evolution, Behavior and Systematics

Cite this

PEMer : A computational framework with simulation-based error models for inferring genomic structural variants from massive paired-end sequencing data. / Korbel, Jan O.; Abyzov, Alexej; Mu, Xinmeng Jasmine; Carriero, Nicholas; Cayting, Philip; Zhang, Zhengdong; Snyder, Michael; Gerstein, Mark B.

In: Genome Biology, Vol. 10, No. 2, R23, 23.02.2009.

Research output: Contribution to journalArticle

Korbel, Jan O. ; Abyzov, Alexej ; Mu, Xinmeng Jasmine ; Carriero, Nicholas ; Cayting, Philip ; Zhang, Zhengdong ; Snyder, Michael ; Gerstein, Mark B. / PEMer : A computational framework with simulation-based error models for inferring genomic structural variants from massive paired-end sequencing data. In: Genome Biology. 2009 ; Vol. 10, No. 2.
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