SequenceVariantAnalyzer

SequenceVariantAnalyzer (SVA) is a computer program for annotating and analyzing genetic variants called (identified) from a whole genome or exome sequencing study (Shotgun sequencing).

Introduction

Background

DNA sequence information underpins genetic research, enabling discoveries of important biological or medical benefit.^[1] Compared with previous discovery strategies, a whole-genome sequencing study is no longer constrained by differing patterns of linkage disequilibrium,^[2] thus, in theory, is more possible to directly identify the genetic variants contributing to biological traits or medical outcomes.

The rapidly evolving high-throughput DNA sequencing technologies have now allowed the rapid generation of large amounts of sequence data for the purpose of performing such whole-genome sequencing studies, at a reasonable cost. SequenceVariantAnalyzer, or SVA, is software that analyzes genetic variants identified in such studies.

Functions

SVA is designed for two specific aims:

(1) To annotate the biological functions of the identified genetic variants and group them, conveniently;

(2) To find the genetic variants that are associated with or responsible for the biological traits or medical outcomes of interest.

Language

SVA is developed on the Java platform.

Authors

SVA is developed and maintained by Dr. Dongliang Ge and Dr. David B. Goldstein at Duke University, Center for Human Genome Variation.

References

^ Bentley DR, Balasubramanian S, Swerdlow HP, et al. (2008). "Accurate whole human genome sequencing using reversible terminator chemistry". Nature. 456 (7218): 53–59. doi:10.1038/nature07517. PMC 2581791. PMID 18987734.
^ Need AC, Goldstein DB (2009). "Next generation disparities in human genomics: concerns and remedies". Trends in Genetics. 25: 489–494. doi:10.1016/j.tig.2009.09.012. PMID 19836853.

External links

SVA project website

[1] Bentley DR, Balasubramanian S, Swerdlow HP, et al. (2008). "Accurate whole human genome sequencing using reversible terminator chemistry". Nature. 456 (7218): 53–59. doi:10.1038/nature07517. PMC 2581791. PMID 18987734.

[2] Need AC, Goldstein DB (2009). "Next generation disparities in human genomics: concerns and remedies". Trends in Genetics. 25: 489–494. doi:10.1016/j.tig.2009.09.012. PMID 19836853.

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