@article {136,
	title = {The Matchmaker Exchange API: automating patient matching through the exchange of structured phenotypic and genotypic profiles.},
	journal = {Hum Mutat},
	volume = {36},
	year = {2015},
	month = {2015 Oct},
	pages = {922-7},
	abstract = {<p>Despite the increasing prevalence of clinical sequencing, the difficulty of identifying additional affected families is a key obstacle to solving many rare diseases. There may only be a handful of similar patients worldwide, and their data may be stored in diverse clinical and research databases. Computational methods are necessary to enable finding similar patients across the growing number of patient repositories and registries. We present the Matchmaker Exchange Application Programming Interface (MME API), a protocol and data format for exchanging phenotype and genotype profiles to enable matchmaking among patient databases, facilitate the identification of additional cohorts, and increase the rate with which rare diseases can be researched and diagnosed. We designed the API to be straightforward and flexible in order to simplify its adoption on a large number of data types and workflows. We also provide a public test data set, curated from the literature, to facilitate implementation of the API and development of new matching algorithms. The initial version of the API has been successfully implemented by three members of the Matchmaker Exchange and was immediately able to reproduce previously identified matches and generate several new leads currently being validated. The API is available at https://github.com/ga4gh/mme-apis. </p>},
	keywords = {Algorithms, Computational Biology, Databases, Genetic, Genetic Predisposition to Disease, Genotype, Humans, Information Dissemination, Phenotype, Rare Diseases, Web Browser},
	issn = {1098-1004},
	doi = {10.1002/humu.22850},
	author = {Buske, Orion J and Schiettecatte, Fran{\c c}ois and Hutton, Benjamin and Dumitriu, Sergiu and Misyura, Andriy and Huang, Lijia and Hartley, Taila and Girdea, Marta and Sobreira, Nara and Mungall, Chris and Brudno, Michael}
}
@article {277,
	title = {DIAMUND: direct comparison of genomes to detect mutations.},
	journal = {Hum Mutat},
	volume = {35},
	year = {2014},
	month = {2014 Mar},
	pages = {283-8},
	abstract = {<p>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. </p>},
	keywords = {Algorithms, Computational Biology, Exome, Genetic Loci, Genetic Variation, Genome, Human, Humans, Mutation, Sequence Alignment, Sequence Analysis, DNA, Software},
	issn = {1098-1004},
	doi = {10.1002/humu.22503},
	author = {Salzberg, Steven L and Pertea, Mihaela and Fahrner, Jill A and Sobreira, Nara}
}
