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BMC Med Genomics. 2015 Mar 1;8:9. doi: 10.1186/s12920-015-0084-2.

ASEQ: fast allele-specific studies from next-generation sequencing data.

Author information

1
Centre for Integrative Biology (CIBIO), University of Trento, Trento, Italy. romanel@science.unitn.it.
2
Centre for Integrative Biology (CIBIO), University of Trento, Trento, Italy. sara.lago@studenti.unitn.it.
3
Centre for Integrative Biology (CIBIO), University of Trento, Trento, Italy. davide.prandi@unitn.it.
4
Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, USA. ans2077@med.cornell.edu.
5
Institute for Computational Biomedicine, Weill Cornell Medical College, New York, USA. ans2077@med.cornell.edu.
6
Institute for Precision Medicine, Weill Cornell Medical College & New York Presbyterian Hospital, New York, USA. ans2077@med.cornell.edu.
7
Centre for Integrative Biology (CIBIO), University of Trento, Trento, Italy. demichelis@science.unitn.it.
8
Institute for Computational Biomedicine, Weill Cornell Medical College, New York, USA. demichelis@science.unitn.it.
9
Institute for Precision Medicine, Weill Cornell Medical College & New York Presbyterian Hospital, New York, USA. demichelis@science.unitn.it.

Abstract

BACKGROUND:

Single base level information from next-generation sequencing (NGS) allows for the quantitative assessment of biological phenomena such as mosaicism or allele-specific features in healthy and diseased cells. Such studies often present with computationally challenging burdens that hinder genome-wide investigations across large datasets that are now becoming available through the 1,000 Genomes Project and The Cancer Genome Atlas (TCGA) initiatives.

RESULTS:

We present ASEQ, a tool to perform gene-level allele-specific expression (ASE) analysis from paired genomic and transcriptomic NGS data without requiring paternal and maternal genome data. ASEQ offers an easy-to-use set of modes that transparently to the user takes full advantage of a built-in fast computational engine. We report its performances on a set of 20 individuals from the 1,000 Genomes Project and show its detection power on imprinted genes. Next we demonstrate high level of ASE calls concordance when comparing it to AlleleSeq and MBASED tools. Finally, using a prostate cancer dataset we report on a higher fraction of ASE genes with respect to healthy individuals and show allele-specific events nominated by ASEQ in genes that are implicated in the disease.

CONCLUSIONS:

ASEQ can be used to rapidly and reliably screen large NGS datasets for the identification of allele specific features. It can be integrated in any NGS pipeline and runs on computer systems with multiple CPUs, CPUs with multiple cores or across clusters of machines.

PMID:
25889339
PMCID:
PMC4363342
DOI:
10.1186/s12920-015-0084-2
[Indexed for MEDLINE]
Free PMC Article

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