Next-Generation Sequencing Dataset Downloader and In Silico Sequence Mining: Graphical-User-Interface-Based Tools for...

Area of Science:

• Bioinformatics
• Genomics
• Clinical Diagnostics

Background:

• Next-generation sequencing (NGS) clinical applications are growing.
• Current pipelines often delay critical decisions until after full data alignment and taxonomic analysis.
• This delay can impact resource allocation and timely clinical insights.

Purpose of the Study:

• To introduce In Silico Sequence Mining (ISSM), a user-friendly, graphical tool for rapid pre-alignment screening of raw sequencing data.
• To enable probe-based target identification directly from FASTQ/FASTA files.
• To enhance the efficiency of clinical bioinformatics workflows.

Main Methods:

• ISSM employs probe-based matching on raw sequencing files (FASTQ/FASTA).
• The workflow supports configurable match thresholds and data subsampling.
• Performance was evaluated using diverse datasets including coronavirus, feline coronavirus (FCoV), HIV, and colorectal cancer samples.

Main Results:

• ISSM accurately identified coronavirus targets using public panels, with a 95% threshold enhancing sensitivity.
• The tool detected FCoV and HIV in relevant samples with minimal spurious matches.
• ISSM facilitated preliminary KRAS mutation screening in cancer datasets and HIV subtyping.

Conclusions:

• ISSM provides rapid post-sequencing triage and prioritizes confirmatory analyses.
• It optimizes resource allocation for bioinformatics and downstream testing (e.g., qPCR, targeted sequencing).
• ISSM offers an accessible solution for researchers and clinicians with limited bioinformatics expertise.