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


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.

SUBMITTER: Castel SE 

PROVIDER: S-EPMC4574606 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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

Castel Stephane E SE   Levy-Moonshine Ami A   Mohammadi Pejman P   Banks Eric E   Lappalainen Tuuli T  

Genome biology 20150917


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 pro  ...[more]

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