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Adapting METAGENOTE to greatly facilitate all genomic sequencing data submissions to the NCBI Sequence Read Archive (SRA) prior to publication

Friday, September 14, 2018 — Poster Session V

12:00 p.m. – 1:30 p.m.
FAES Terrace
NIAID
COMPBIO-9

Authors

  • M Quiñones
  • D Liou
  • C Shyu
  • L Kim
  • D Hurt

Abstract

​Science involves the action of collecting, analyzing, publishing, reproducing, critiquing and reusing data. While the concept of "Open science" began in the 17th century with the academic journal, modern era researchers have encountered numerous challenges for sharing data associated with their research. Dissemination of data is crucial for accelerating research by facilitating integration and enhancing reproducibility. In order to promote robust data sharing, on 2015, the NIH established the Genomic Data Sharing (GDS) policy, which requires responsible data sharing prior to publication of a manuscript. NIH researchers are encouraged to upload their sequencing files to the NCBI SRA repository, which in turn stores and shares the files with the greater scientific community. The public datafiles are of no value unless accompanied by proper annotations such as tissue source, host or sequencing method. Unfortunately, most researchers still find the conventional annotation and metadata publishing processes very cumbersome. To overcome these challenges, our team adapted METAGENOTE, a tool originally designed for microbiome samples, to also include tables that allow annotation of any type of genomic samples with associated sequencing files. The user will be able to add custom annotations or use vocabulary from the integrated ontologies recommended by NCBI templates. After annotating samples, the user will upload by "drag and drop" all sequencing files to METAGENOTE and at the end the creation of BioProject, BioSamples and SRA Experiments will be automated. METAGENOTE is a robust system that allows sharing of data with detailed metadata needed for discovery through comparative analyses. Visit https://metagenote.niaid.nih.gov

Category: Computational Biology