SeqForge: a scalable platform for alignment-based searches, motif detection, and sequence curation across...

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Increasing volumes of microbial and metagenomic data necessitate efficient analysis tools.
• Existing methods like BLAST+ require custom scripting for large-scale population studies, posing a barrier for researchers.
• There is a growing demand for streamlined approaches to comparative sequence searches and functional annotation.

Purpose of the Study:

• To develop a scalable, modular command-line toolkit for efficient large-scale genomic data analysis.
• To automate database creation, querying, and motif mining for microbial and metagenomic datasets.
• To reduce the computational and technical barriers for researchers conducting population-level genomic studies.

Main Methods:

• Developed SeqForge, a command-line toolkit for alignment-based searches and motif mining.
• Automated BLAST+ database creation and querying, integrated amino acid motif discovery, and enabled sequence extraction.
• Implemented support for diverse input formats, parallelized execution, and built-in visualization tools.

Main Results:

• SeqForge streamlines BLAST+ searches and motif discovery across large genomic datasets.
• The toolkit automates result curation into structured, easily parsed formats.
• Benchmarking showed near-linear runtime scaling for computationally intensive modules with modest memory usage.

Conclusions:

• SeqForge lowers the computational barrier for large-scale meta/genomic exploration.
• Enables population-scale BLAST searches, motif detection, and sequence curation without custom scripting.
• The toolkit is freely available, platform-independent, and suitable for various computing environments.