Experimental design-based functional mining and characterization of high-throughput sequencing data in the sequence...

Area of Science:

• Genomics and Bioinformatics
• Biotechnology
• Data Science

Background:

• High-throughput sequencing, or next-generation sequencing (NGS), generates vast amounts of genomic, transcriptomic, and epigenetics data.
• The Sequence Read Archive (SRA) is a public repository for this sequencing data, experiencing rapid growth.
• Challenges exist in searching and accessing high-quality data from SRA due to complex structure and inconsistent data quality.

Purpose of the Study:

• To improve the accessibility and quality assessment of data within the Sequence Read Archive (SRA).
• To establish curated lists of SRA entries linked to publications and disease information.
• To facilitate research utilizing -omics data for disease mechanism elucidation.

Main Methods:

• Focused on SRA entries cited in journal articles as a proxy for data quality.
• Extracted SRA IDs and PubMed IDs (PMIDs) to create SRA-PMID pairs and a publication list.
• Characterized SRA entries by disease keywords (MeSH terms) from associated articles, creating SRA-MeSH disease term pairs and a disease list.

Main Results:

• Successfully retrieved 2748 SRA ID-PMID pairs and 989 SRA ID-MeSH disease term pairs.
• Constructed a publication list and a disease list referencing SRA data.
• Developed hyperlinks between SRA diseases and existing disease feature profiles in the Gendoo system.

Conclusions:

• The developed DBCLS SRA web service enhances the discoverability of high-quality, cited sequencing data.
• This resource aids researchers in navigating SRA for -omics analyses, particularly for understanding disease mechanisms.
• Improved data accessibility from SRA supports advancements in genomic research and personalized medicine.