Trecode: a FAIR eco-system for the analysis and archiving of omics data in a combined diagnostic and research setting

AbstractMotivationThe increase in speed, reliability and cost-effectiveness of high-throughput sequencing has led to the widespread clinical application of genome (WGS), exome (WXS) and transcriptome analysis. WXS and RNA sequencing is now being implemented as standard of care for patients and for patients included in clinical studies. To keep track of sample relationships and analyses, a platform is needed that can unify metadata for diverse sequencing strategies with sample metadata whilst supporting automated and reproducible analyses. In essence ensuring that analysis is conducted consistently, and data is Findable, Accessible, Interoperable and Reusable (FAIR).ResultsWe present “Trecode”, a framework that records both clinical and research sample (meta) data and manages computational genome analysis workflows executed for both settings. Thereby achieving tight integration between analyses results and sample metadata. With complete, consistent and FAIR (meta) data management in a single platform, stacked bioinformatic analyses are performed automatically and tracked by the database ensuring data provenance, reproducibility and reusability which is key in worldwide collaborative translational research.Availability and implementationThe Trecode data model, codebooks, NGS workflows and client programs are currently being cleared from local compute infrastructure dependencies and will become publicly available in spring [email protected]

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PCN230 A Systematic Literature Review on Cost-Effectiveness of Next-Generation Sequencing (NGS): NGS Compared to Other High-Throughput Sequencing Methods in the Context of Personalized Cancer Therapy

Value in Health ◽

10.1016/j.jval.2020.08.367 ◽

2020 ◽

Vol 23 ◽

pp. S464

Author(s):

A. Filipov ◽

R. Mitova ◽

G. Slavchev ◽

S. Djambazov ◽

T. Vekov

Keyword(s):

Cost Effectiveness ◽

Next Generation Sequencing ◽

Cancer Therapy ◽

Literature Review ◽

High Throughput ◽

High Throughput Sequencing ◽

Personalized Cancer Therapy ◽

Personalized Cancer ◽

Next Generation Sequencing Ngs ◽

Generation Sequencing

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Review of applications of high-throughput sequencing in personalized medicine: barriers and facilitators of future progress in research and clinical application

Briefings in Bioinformatics ◽

10.1093/bib/bby051 ◽

2019 ◽

Vol 20 (5) ◽

pp. 1795-1811 ◽

Cited By ~ 19

Author(s):

Gaye Lightbody ◽

Valeriia Haberland ◽

Fiona Browne ◽

Laura Taggart ◽

Huiru Zheng ◽

...

Keyword(s):

Personalized Medicine ◽

High Throughput ◽

High Performance ◽

High Throughput Sequencing ◽

Technology Selection ◽

Patient Treatment ◽

Full Potential ◽

Omics Data ◽

Full Genome Sequencing ◽

Data Governance

Abstract There has been an exponential growth in the performance and output of sequencing technologies (omics data) with full genome sequencing now producing gigabases of reads on a daily basis. These data may hold the promise of personalized medicine, leading to routinely available sequencing tests that can guide patient treatment decisions. In the era of high-throughput sequencing (HTS), computational considerations, data governance and clinical translation are the greatest rate-limiting steps. To ensure that the analysis, management and interpretation of such extensive omics data is exploited to its full potential, key factors, including sample sourcing, technology selection and computational expertise and resources, need to be considered, leading to an integrated set of high-performance tools and systems. This article provides an up-to-date overview of the evolution of HTS and the accompanying tools, infrastructure and data management approaches that are emerging in this space, which, if used within in a multidisciplinary context, may ultimately facilitate the development of personalized medicine.

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