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Tools and best practices for allelic expression analysis

Stephane E. Castel, Ami Levy Moonshine, Pejman Mohammadi, Eric Banks, Tuuli Lappalainen
doi: https://doi.org/10.1101/016097
Stephane E. Castel
1New York Genome Center, New York, NY
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  • For correspondence: scastel@nygenome.org tlappalainen@nygenome.org
Ami Levy Moonshine
2Broad Institute, Cambridge, MA
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Pejman Mohammadi
1New York Genome Center, New York, NY
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Eric Banks
2Broad Institute, Cambridge, MA
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Tuuli Lappalainen
1New York Genome Center, New York, NY
3Department of Systems Biology, Columbia University, New York, NY
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  • For correspondence: scastel@nygenome.org tlappalainen@nygenome.org
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ABSTRACT

Allelic expression (AE) analysis has become an important tool for integrating genome and transcriptome data to characterize various biological phenomena such as cis-regulatory variation and nonsense-mediated decay. In this paper, we systematically analyze the properties of AE 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 and filtering for such errors, and show that the resulting AE data has extremely low technical noise. Finally, we introduce novel software for high-throughput production of AE data from RNA-sequencing data, implemented in the GATK framework. These improved tools and best practices for AE analysis yield higher quality AE data by reducing technical bias. This provides a practical framework for wider adoption of AE analysis by the genomics community.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted March 06, 2015.
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Tools and best practices for allelic expression analysis
Stephane E. Castel, Ami Levy Moonshine, Pejman Mohammadi, Eric Banks, Tuuli Lappalainen
bioRxiv 016097; doi: https://doi.org/10.1101/016097
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Tools and best practices for allelic expression analysis
Stephane E. Castel, Ami Levy Moonshine, Pejman Mohammadi, Eric Banks, Tuuli Lappalainen
bioRxiv 016097; doi: https://doi.org/10.1101/016097

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