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PLoS Biol. 2018 Dec 31;16(12):e3000099. doi: 10.1371/journal.pbio.3000099. eCollection 2018 Dec.

Enabling precision medicine via standard communication of HTS provenance, analysis, and results.

Author information

1
Harvard/MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, United States of America.
2
Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, United States of America.
3
Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Boston, Massachusetts, United States of America.
4
Seven Bridges, Cambridge, Massachusetts, United States of America.
5
School of Computer Science, The University of Manchester, Manchester, United Kingdom.
6
Common Workflow Language Project, Vilnius, Lithuania.
7
The Department of Biochemistry & Molecular Medicine, The George Washington University Medical Center, Washington, DC, United States of America.
8
Foundation for Innovative New Diagnostics (FIND), Geneva, Switzerland.
9
OpenBox Bio, Vienna, Virgnia, United States of America.
10
The McCormick Genomic and Proteomic Center, The George Washington University, Washington, DC, United States of America.
11
Internet 2, Washington, DC, United States of America.
12
US Food and Drug Administration, Silver Spring, Maryland, United States of America.
13
Center for Genetic Medicine, Children's National Medical Center, Washington, DC, United States of America.
14
The Department of Genomics and Precision Medicine, The George Washington University, School of Medicine and Health Sciences, Washington, DC, United States of America.
15
Center for Biomedical Informatics and Information Technology, National Cancer Institute, National Institutes of Health, Gaithersburg, Maryland, United States of America.
16
Attain, McClean, Virginia, United States of America.
17
Computational Biology Program, Oregon Health & Science University, Portland Oregon, United States of America.
18
MRL IT, Merck & Co., Boston, Massachusetts, United States of America.
19
Stony Brook University, School of Medicine and College of Engineering and Applied Sciences, Stony Brook, New York, United States of America.
20
Department of Biological Sciences, Hunter College of The City University of New York, New York, New York, United States of America.
21
Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York, United States of America.
22
Wellesley College, Wellesley, Massachusetts, United States of America.
23
Critical Path Institute, Tucson, Arizona, United States of America.
24
DDL Diagnostic Laboratory, Rijswijk, Netherlands.
25
NSilico Life Science, Nova Center, Belfield Innovation Park, University College Dublin, Dublin Ireland.
26
OTSUKA Pharmaceutical Development & Commercialization, Princeton, New Jersey, United States of America.
27
New York Genome Center, New York, New York, United States of America.

Abstract

A personalized approach based on a patient's or pathogen's unique genomic sequence is the foundation of precision medicine. Genomic findings must be robust and reproducible, and experimental data capture should adhere to findable, accessible, interoperable, and reusable (FAIR) guiding principles. Moreover, effective precision medicine requires standardized reporting that extends beyond wet-lab procedures to computational methods. The BioCompute framework (https://w3id.org/biocompute/1.3.0) enables standardized reporting of genomic sequence data provenance, including provenance domain, usability domain, execution domain, verification kit, and error domain. This framework facilitates communication and promotes interoperability. Bioinformatics computation instances that employ the BioCompute framework are easily relayed, repeated if needed, and compared by scientists, regulators, test developers, and clinicians. Easing the burden of performing the aforementioned tasks greatly extends the range of practical application. Large clinical trials, precision medicine, and regulatory submissions require a set of agreed upon standards that ensures efficient communication and documentation of genomic analyses. The BioCompute paradigm and the resulting BioCompute Objects (BCOs) offer that standard and are freely accessible as a GitHub organization (https://github.com/biocompute-objects) following the "Open-Stand.org principles for collaborative open standards development." With high-throughput sequencing (HTS) studies communicated using a BCO, regulatory agencies (e.g., Food and Drug Administration [FDA]), diagnostic test developers, researchers, and clinicians can expand collaboration to drive innovation in precision medicine, potentially decreasing the time and cost associated with next-generation sequencing workflow exchange, reporting, and regulatory reviews.

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