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Genome Biol. 2015 Sep 17;16:195. doi: 10.1186/s13059-015-0762-6.

Tools and best practices for data processing in allelic expression analysis.

Author information

1
New York Genome Center, New York, NY, USA. scastel@nygenome.org.
2
Department of Systems Biology, Columbia University, New York, NY, USA. scastel@nygenome.org.
3
Broad Institute, Cambridge, MA, USA.
4
New York Genome Center, New York, NY, USA.
5
Department of Systems Biology, Columbia University, New York, NY, USA.
6
New York Genome Center, New York, NY, USA. tlappalainen@nygenome.org.
7
Department of Systems Biology, Columbia University, New York, NY, USA. tlappalainen@nygenome.org.

Abstract

Allelic expression analysis has become important for integrating genome and transcriptome data to characterize various biological phenomena such as cis-regulatory variation and nonsense-mediated decay. We analyze the properties of allelic expression read count data and technical sources of error, such as low-quality or double-counted RNA-seq reads, genotyping errors, allelic mapping bias, and technical covariates due to sample preparation and sequencing, and variation in total read depth. We provide guidelines for correcting such errors, show that our quality control measures improve the detection of relevant allelic expression, and introduce tools for the high-throughput production of allelic expression data from RNA-sequencing data.

PMID:
26381377
PMCID:
PMC4574606
DOI:
10.1186/s13059-015-0762-6
[Indexed for MEDLINE]
Free PMC Article

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