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+ often require custom scripting for large-scale comparative searches and functional annotation, posing challenges for researchers.

Purpose of the Study:

• To develop a scalable, modular toolkit (SeqForge) for streamlined alignment-based searches and motif mining in large genomic datasets.
• To automate database creation, querying, and result curation for enhanced meta/genomic exploration.

Main Methods:

• SeqForge is a command-line toolkit automating BLAST+ database operations and integrating amino acid motif discovery.
• It supports diverse input formats, parallelized execution, and provides sequence/contig extraction and result parsing.
• Built-in visualization tools and benchmarking for performance analysis are included.

Main Results:

• SeqForge automates complex tasks, including BLAST+ searches and motif detection, across large genomic datasets.
• The toolkit demonstrates near-linear runtime scaling for intensive modules with modest memory requirements.
• Results are curated into structured, easily parsable formats for simplified downstream analysis.

Conclusions:

• SeqForge reduces the computational hurdles for large-scale meta/genomic data analysis.
• It empowers researchers to conduct population-scale BLAST searches and motif detection without custom scripting.
• The freely available, platform-independent toolkit is suitable for various computing environments.