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Title

SNPAAMapper: An efficient genome-wide SNP variant analysis pipeline for next-generation sequencing data

 

Authors

Yongsheng Bai1* & James Cavalcoli2

 

Affiliation

1Morgridge Institute for Research, University of Wisconsin-Madison, 330 N Orchard St, Madison, WI 53715, U.S.A; 2Center for Computational Medicine and Bioinformatics, and Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Ave., Ann Arbor, Michigan 48109, U.S.A

 

Email

yongshengbai@hotmail.com; *Corresponding author

 

Article Type

Software

 

Date

Received September 27, 2013; Accepted September 30, 2013; Published October 16, 2013

 

Abstract

Many NGS analysis tools focusing on read alignment and variant calling functions for exome sequencing data have been developed in recent years. However, publicly available tools dealing with the downstream analysis of genome-wide variants -are fewer and have limited functionality. We developed SNPAAMapper, a novel variant analysis pipeline that can effectively classify variants by region (e.g. CDS, UTRs, intron, upstream, downstream), predict amino acid change type (e.g. synonymous, non-synonymous mutation), and prioritize mutation effects (e.g. CDS versus UTRs). Additional functionality afforded by our pipeline includes: checking variants at exon/intron junctions, customized homozygosity and allele frequency cutoff parameters, and annotation of known variants with dbSNP information, listing original and mutated amino acid sequences containing variants. The final result is reported in a spreadsheet format table containing all variant associated information and prioritized amino acids effects for investigators to examine.

 

Availability

http://www.ccmb.med.umich.edu/ccdu /SNPAAMapper

 

Keywords

SNP, Next Generation Sequencing, Downstream Analysis

 

Citation

Bai & Cavalcoli,   Bioinformation 9(17): 870-872 (2013)

 

Edited by

P Kangueane

 

ISSN

0973-2063

 

Publisher

Biomedical Informatics

 

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.