Computational methods for discovering structural variation with next-generation sequencing

• Published in: Nature methods Vol. 6; no. Suppl 11; pp. S13 - S20
• Main Authors: Medvedev, Paul, Stanciu, Monica, Brudno, Michael
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.11.2009; Nature Publishing Group
• Subjects: Algorithms; Analysis; Base Sequence; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Computational biology; Computational Biology - methods; DNA sequencing; Genetic Variation; Genome; Genomes; Genomics; Genomics - methods; Humans; Hybridization; Innovations; Life Sciences; Methods; Molecular structure; Nucleotide sequencing; Properties; Proteomics; Research methodology; review-article; Sequence Analysis, DNA - methods; Technological change; Canada
• ISSN: 1548-7091; 1548-7105; 1548-7105
• DOI: 10.1038/nmeth.1374

In the last several years, a number of studies have described large-scale structural variation in several genomes. Traditionally, such methods have used whole-genome array comparative genome hybridization or single-nucleotide polymorphism arrays to detect large regions subject to copy-number variation. Later techniques have been based on paired-end mapping of Sanger sequencing data, providing better resolution and accuracy. With the advent of next-generation sequencing, a new generation of methods is being developed to tackle the challenges of short reads, while taking advantage of the high coverage the new sequencing technologies provide. In this survey, we describe these methods, including their strengths and their limitations, and future research directions.