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

Abstract

Abstract. Precision medicine can be empowered by a personalized approach to patient care based on the patient's unique genomic sequence. T o be used in precision medicine, genomic findings must be robust, reproducible, and experimental data capture should adhere to FAIR Data Guiding Principles. Moreover, precision medicine requires standardization that extends beyond wet lab procedures to computational methods.Rapidly developing standardization technologies improves communication of genomic sequencing by introducing concepts such as error domain, usability domain, validation kit, and provenance information. T hese advancements allow data provenance to be standardized and ensure interoperability. T hus, a resulting bioinformatics computation instance that includes these advancements can be easily communicated, repeated and compared by scientists, regulators, clinicians and others, allowing a greater range of practical applications.Advancing clinical trials, precision medicine, and regulatory submissions requires an umbrella of standards that not only fuses these elements, but also ensures efficient communication and documentation of genomic analyses. T hrough standardized bundling of HT S studies under an umbrella, regulatory agencies (FDA), academic researchers, and clinicians can expand collaboration to drive innovation in precision medicine with the potential for decreasing the time and cost associated with NGS workflow exchange, including FDA regulatory review submissions.

Whole genome sequencing of Mycobacterium tuberculosis: current standards and open issues

“…MTBseq 34 record all thresholds, steps and implementation arguments for a given pipeline is utilised 59 . Comparisons of BCOs from different pipelines can then be used to set acceptable lower limits for the assessed parameters, refining technical validation criteria across pipelines 60 .…”

Sharing interoperable workflow provenance: A review of best practices and their practical application in CWLProv

“…This level of provenance, resource aggregation, and sharing can provide a researcher-centric view of data and enable users to re-enact a set of steps or full workflow by providing a filtered and annotated view of the execution. This can be non-trivial to achieve with mainstream methods of workflow definition and sharing because it requires guided user annotations with controlled vocabularies, but this can be simplified by reusing related tooling from existing efforts such as BioCompute Objects [41] and DataCrate [78].…”

“…The solution was tightly coupled with the Taverna WMS and hence, if shared, would not be reproducible outside of the Taverna environment. Other notable efforts to use ROs for workflow preservation and method aggregation have been undertaken in systems biology [39], in clinical settings [40], and in precision medicine [41].…”

Data Lakes, Clouds, and Commons: A Review of Platforms for Analyzing and Sharing Genomic Data

“…Workflow languages have been developed so that domain specialists knowledgeable about the workflows can describe the workflows in a manner that is independent of the specific underlying physical architecture of the system executing the workflows. Despite many years of effort though, there is still no standard language for expressing workflows in general and bioinformatics workflows in particular [ 47 , 48 ]. Within the cancer genomics community, the Common Workflow Language (CWL) [ 49 ] is gaining in popularity.…”