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Nucleic Acids Res. 2014 Apr;42(6):e44. doi: 10.1093/nar/gkt1381. Epub 2014 Jan 11.

An integrated framework for discovery and genotyping of genomic variants from high-throughput sequencing experiments.

Author information

1
Agrobiodiversity research area, International Center for Tropical Agriculture (CIAT), Km 17 Recta Cali- Palmira, A.A. 6713 Cali, Colombia, Laboratory of Molecular Cell Biology, Department of Biology, Institute of Botany and Microbiology, KU Leuven, Kasteelpark Arenberg 31, B-3001 Leuven-Heverlee, Flanders, Belgium, Department of Molecular Microbiology, VIB, Kasteelpark Arenberg 31, B-3001 Leuven-Heverlee, Flanders, Belgium, VIB Laboratory of Systems Biology, KU Leuven, Gaston Geenslaan 1, B-3001 Leuven-Heverlee, Flanders, Belgium and Laboratory for Genetics and Genomics, Centre of Microbial and Plant Genetics, KU Leuven, Gaston Geenslaan 1, B-3001 Leuven-Heverlee, Flanders, Belgium.

Abstract

Recent advances in high-throughput sequencing (HTS) technologies and computing capacity have produced unprecedented amounts of genomic data that have unraveled the genetics of phenotypic variability in several species. However, operating and integrating current software tools for data analysis still require important investments in highly skilled personnel. Developing accurate, efficient and user-friendly software packages for HTS data analysis will lead to a more rapid discovery of genomic elements relevant to medical, agricultural and industrial applications. We therefore developed Next-Generation Sequencing Eclipse Plug-in (NGSEP), a new software tool for integrated, efficient and user-friendly detection of single nucleotide variants (SNVs), indels and copy number variants (CNVs). NGSEP includes modules for read alignment, sorting, merging, functional annotation of variants, filtering and quality statistics. Analysis of sequencing experiments in yeast, rice and human samples shows that NGSEP has superior accuracy and efficiency, compared with currently available packages for variants detection. We also show that only a comprehensive and accurate identification of repeat regions and CNVs allows researchers to properly separate SNVs from differences between copies of repeat elements. We expect that NGSEP will become a strong support tool to empower the analysis of sequencing data in a wide range of research projects on different species.

PMID:
24413664
PMCID:
PMC3973327
DOI:
10.1093/nar/gkt1381
[Indexed for MEDLINE]
Free PMC Article

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