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Bioinformatics. 2015 Jul 15;31(14):2377-9. doi: 10.1093/bioinformatics/btv135. Epub 2015 Mar 8.

Reducing the search space for causal genetic variants with VASP.

Author information

1
Department of Immunology, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia.
2
Australian Phenomics Facility, Australian National University, Canberra, ACT 2601, Australia.
3
Department of Immunology, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia, Department of Immunology, The Canberra Hospital, Canberra, ACT 2605, Australia.
4
Department of Immunology, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia, Rammaciotti Immunisation Genomics Laboratory, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia and.
5
Department of Immunology, John Curtin School of Medical Research, Australian National University, Canberra City, ACT 2601, Australia, Immunogenomics Group, Immunology Research Program, Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia.

Abstract

MOTIVATION:

Increasingly, cost-effective high-throughput DNA sequencing technologies are being utilized to sequence human pedigrees to elucidate the genetic cause of a wide variety of human diseases. While numerous tools exist for variant prioritization within a single genome, the ability to concurrently analyze variants within pedigrees remains a challenge, especially should there be no prior indication of the underlying genetic cause of the disease. Here, we present a tool, variant analysis of sequenced pedigrees (VASP), a flexible data integration environment capable of producing a summary of pedigree variation, providing relevant information such as compound heterozygosity, genome phasing and disease inheritance patterns. Designed to aggregate data across a sequenced pedigree, VASP allows both powerful filtering and custom prioritization of both single nucleotide variants (SNVs) and small indels. Hence, clinical and research users with prior knowledge of a disease are able to dramatically reduce the variant search space based on a wide variety of custom prioritization criteria.

AVAILABILITY AND IMPLEMENTATION:

Source code available for academic non-commercial research purposes at https://github.com/mattmattmattmatt/VASP.

PMID:
25755272
PMCID:
PMC4495293
DOI:
10.1093/bioinformatics/btv135
[Indexed for MEDLINE]
Free PMC Article

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