title: DIAMUND: direct comparison of genomes to detect mutations.
abstract: DNA sequencing has become a powerful method to discover the genetic basis of disease. Standard, widely used protocols for analysis usually begin by comparing each individual to the human reference genome. When applied to a set of related individuals, this approach reveals millions of differences, most of which are shared among the individuals and unrelated to the disease being investigated. We have developed a novel algorithm for variant detection, one that compares DNA sequences directly to one another, without aligning them to the reference genome. When used to find de novo mutations in exome sequences from family trios, or to compare normal and diseased samples from the same individual, the new method, direct alignment for mutation discovery (DIAMUND), produces a dramatically smaller list of candidate mutations than previous methods, without losing sensitivity to detect the true cause of a genetic disease. We demonstrate our results on several example cases, including two family trios in which it correctly found the disease-causing variant while excluding thousands of harmless variants that standard methods had identified.
authors.affiliation: Center for Computational Biology, Johns Hopkins School of Medicine, Baltimore, Maryland, 21205; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, 21205.
authors.forename: Steven L
authors.initials: SL
authors.lastname: Salzberg
authors.forename: Mihaela
authors.initials: M
authors.lastname: Pertea
authors.forename: Jill A
authors.initials: JA
authors.lastname: Fahrner
authors.forename: Nara
authors.initials: N
authors.lastname: Sobreira
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D059472:Exome; D056426:Genetic Loci; D014644:Genetic Variation; D015894:Genome, Human; D006801:Humans; D009154:Mutation; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
keywords: bioinformatics; computational biology; exome sequencing; sequence alignment; variant detection