Intensification: A Resource for Amplifying Population-Genetic Signals with Protein Repeats. - Chen, Jieming; Wang, Bo; Regan, Lynne; Gerstein, Mark (J Mol Biol, 2017)
Large-scale genome sequencing holds great promise for the interpretation of protein structures through the discovery of many, rare functional variants in the human population. However, because protein-coding regions are under high selective constraints, these variants occur at low frequencies, such that there is often insufficient statistics for downstream calculations. To address this problem, we develop the Intensification approach, which uses the modular structure of repeat protein domains to amplify signals of selection from population genetics and traditional interspecies conservation. In particular, we are able to aggregate variants at the codon level to identify important positions in repeat domains that show strong conservation signals. This allows us to compare conservation over different evolutionary timescales. It also enables us to visualize population-genetic measures on protein structures. We make available the Intensification results as an online resource (http://intensification.gersteinlab.org) and illustrate the approach through a case study on the tetratricopeptide repeat.
Keywords: Amino Acid Sequence; Chromosome Mapping; Codon; Computational Biology; Databases, Genetic; Evolution, Molecular; Genetics, Population; Humans; Internet; Molecular Structure; Protein Conformation; Proteins; Signal Processing, Computer-Assisted; X-Rays

My46: a Web-based tool for self-guided management of genomic test results in research and clinical settings. - Tabor, Holly K; Jamal, Seema M; Yu, Joon-Ho; Crouch, Julia M; Shankar, Aditi G; Dent, Karin M; Anderson, Nick; Miller, Damon A; Futral, Brett T; Bamshad, Michael J (Genet Med, 2017)
A major challenge to implementing precision medicine is the need for an efficient and cost-effective strategy for returning individual genomic test results that is easily scalable and can be incorporated into multiple models of clinical practice. My46 is a Web-based tool for managing the return of genetic results that was designed and developed to support a wide range of approaches to disclosing results, ranging from traditional face-to-face disclosure to self-guided models. My46 has five key functions: set and modify results-return preferences, return results, educate, manage the return of results, and assess the return of results. These key functions are supported by six distinct modules and a suite of features that enhance the user experience, ease site navigation, facilitate knowledge sharing, and enable results-return tracking. My46 is a potentially effective solution for returning results and supports current trends toward shared decision making between patients and providers and patient-driven health management.Genet Med 19 4, 467-475.
Keywords: Biomedical Research; Computational Biology; Decision Making; Genomics; Humans; Internet; Medical Informatics; Patient Access to Records; Precision Medicine

New tools for Mendelian disease gene identification: PhenoDB variant analysis module; and GeneMatcher, a web-based tool for linking investigators with an interest in the same gene. - Sobreira, Nara; Schiettecatte, François; Boehm, Corinne; Valle, David; Hamosh, Ada (Hum Mutat, 2015)
Identifying the causative variant from among the thousands identified by whole-exome sequencing or whole-genome sequencing is a formidable challenge. To make this process as efficient and flexible as possible, we have developed a Variant Analysis Module coupled to our previously described Web-based phenotype intake tool, PhenoDB (http://researchphenodb.net and http://phenodb.org). When a small number of candidate-causative variants have been identified in a study of a particular patient or family, a second, more difficult challenge becomes proof of causality for any given variant. One approach to this problem is to find other cases with a similar phenotype and mutations in the same candidate gene. Alternatively, it may be possible to develop biological evidence for causality, an approach that is assisted by making connections to basic scientists studying the gene of interest, often in the setting of a model organism. Both of these strategies benefit from an open access, online site where individual clinicians and investigators could post genes of interest. To this end, we developed GeneMatcher (http://genematcher.org), a freely accessible Website that enables connections between clinicians and researchers across the world who share an interest in the same gene(s).
Keywords: Computational Biology; Databases, Genetic; Genetic Association Studies; Genetic Diseases, Inborn; Genetic Variation; Genomics; Internet; Phenotype; Software; Web Browser

Launching genomics into the cloud: deployment of Mercury, a next generation sequence analysis pipeline. - Reid, Jeffrey G; Carroll, Andrew; Veeraraghavan, Narayanan; Dahdouli, Mahmoud; Sundquist, Andreas; English, Adam; Bainbridge, Matthew; White, Simon; Salerno, William; Buhay, Christian; Yu, Fuli; Muzny, Donna; et al. (BMC Bioinformatics, 2014)
BACKGROUND: Massively parallel DNA sequencing generates staggering amounts of data. Decreasing cost, increasing throughput, and improved annotation have expanded the diversity of genomics applications in research and clinical practice. This expanding scale creates analytical challenges: accommodating peak compute demand, coordinating secure access for multiple analysts, and sharing validated tools and results. RESULTS: To address these challenges, we have developed the Mercury analysis pipeline and deployed it in local hardware and the Amazon Web Services cloud via the DNAnexus platform. Mercury is an automated, flexible, and extensible analysis workflow that provides accurate and reproducible genomic results at scales ranging from individuals to large cohorts. CONCLUSIONS: By taking advantage of cloud computing and with Mercury implemented on the DNAnexus platform, we have demonstrated a powerful combination of a robust and fully validated software pipeline and a scalable computational resource that, to date, we have applied to more than 10,000 whole genome and whole exome samples.
Keywords: Genome; Genomics; High-Throughput Nucleotide Sequencing; Humans; Internet; Software

PhenoDB: a new web-based tool for the collection, storage, and analysis of phenotypic features. - Hamosh, Ada; Sobreira, Nara; Hoover-Fong, Julie; Sutton, V Reid; Boehm, Corinne; Schiettecatte, François; Valle, David (Hum Mutat, 2013)
To interpret whole exome/genome sequence data for clinical and research purposes, comprehensive phenotypic information, knowledge of pedigree structure, and results of previous clinical testing are essential. With these requirements in mind and to meet the needs of the Centers for Mendelian Genomics project, we have developed PhenoDB (http://phenodb.net), a secure, Web-based portal for entry, storage, and analysis of phenotypic and other clinical information. The phenotypic features are organized hierarchically according to the major headings and subheadings of the Online Mendelian Inheritance in Man (OMIM®) clinical synopses, with further subdivisions according to structure and function. Every string allows for a free-text entry. All of the approximately 2,900 features use the preferred term from Elements of Morphology and are fully searchable and mapped to the Human Phenotype Ontology and Elements of Morphology. The PhenoDB allows for ascertainment of relevant information from a case in a family or cohort, which is then searchable by family, OMIM number, phenotypic feature, mode of inheritance, genes screened, and so on. The database can also be used to format phenotypic data for submission to dbGaP for appropriately consented individuals. PhenoDB was built using Django, an open source Web development tool, and is freely available through the Johns Hopkins McKusick-Nathans Institute of Genetic Medicine (http://phenodb.net).
Keywords: Databases, Factual; Genomics; Humans; Internet; Medical Informatics; Phenotype; Software