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Bell, A.

Publications and source records attributed to Bell, A..

2 recordsLinked to original sources

Enabling Precision Medicine via standard communication of NGS provenance, analysis, and results

A personalized approach based on a patients or pathogens unique genomic sequence is the foundation of precision medicine. Genomic findings must be robust and reproducible, and experimental data capture should adhere to FAIR guiding principles. Moreover, effective precision medicine requires standardized reporting that extends beyond wet lab procedures to computational methods. The BioCompute framework (https://osf.io/zm97b/) 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 (BCO) 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\". By communication of high-throughput sequencing studies using a BCO, regulatory agencies (e.g., 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.

scientific communication and education

Curated compendium of human transcriptional biomarker data

Genome-wide transcriptional profiles provide broad insights into cellular activity. One important use of such data isto identify relationships between transcription levels and patient outcomes. These translational insights can guide the development of biomarkers for predicting outcomes in clinical settings. Over the past decades, data from many translational-biomarker studies have been deposited in public repositories, enabling other scientists to reuse the data in follow-up studies. However, data-reuse efforts require considerable time and expertise because transcriptional data are generated using heterogeneous profiling technologies, preprocessed using diverse normalization procedures, and annotated in non-standard ways. To address this problem, we curated a compendium of 45 translational-biomarker datasets from the public domain. To increase the datas utility, we reprocessed the raw expression data using a standard computational pipeline and standardized the clinical annotations in a fully reproducible manner (see osf.io/ssk3t). We believe these data will be particularly useful to researchers seeking to validate gene-level findings or to perform benchmarking studies--for example, to compare and optimize machine-learning algorithms ability to predict biomedical outcomes.

cancer biology