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

Next-generation sequencing (NGS) is expanding in clinical practice, yet many pipelines defer actionable decisions until full alignment and taxonomic calling are complete. The primary contribution of In Silico Sequence Mining (ISSM) lies in providing an accessible graphical-user-interface-based workflow for rapid probe-based pre-alignment screening of raw FASTQ/FASTA datasets. ISSM performs probe-based matching of predefined targets directly on raw FASTQ/FASTA files and supports configurable match thresholds and subsampling. We assessed ISSM using 4 datasets, including 34,899 coronavirus genomes, NGS data from CRFK cells infected with feline coronavirus (FCoV), plasma-derived HIV datasets from patients with sepsis, and colorectal cancer sequencing datasets. Public primer/probe panels applied to coronavirus genomes reliably identified targets at a 100% match threshold, while a 95% threshold increased sensitivity with limited false positives. In FCoV samples, strong positives were observed with both FCoV-specific and pan-coronavirus probes, and spurious matches were negligible relative to true-positive read counts. In HIV data, specificity was preserved in controls; under the most permissive screening criterion, HIV probe matches were detected in 170/263 datasets. Subtyping using subtype-specific probes was feasible in 80 datasets, with subtype D most frequent. In colorectal cancer datasets, ISSM supported preliminary KRAS-mutation-associated probe screening. Operationally, ISSM supports rapid post-sequencing triage, prioritization of confirmatory alignments, and rational reflex testing to quantitative polymerase chain reaction/targeted sequencing, improving allocation of bioinformatics resources. Together with the NGS Dataset Downloader, the ISSM provides a user-friendly graphical-user-interface-based program for researchers and clinical users with limited bioinformatics expertise. Software and a reproducible workflow are available at https://github.com/khk1329/NDD-and-ISSM.git.