iSeqSearch: incremental protein search for iBlast/iMMSeqs2/iDiamond

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics
• Proteomics

Background:

• Sequencing technology advancements rapidly increase DNA and protein data, leading to continuously growing genomic and proteomic databases.
• Frequent database updates are necessary, but re-searching entire datasets is resource-intensive.
• Incremental database search offers a solution to this challenge by reusing previously processed information.

Purpose of the Study:

• To generalize the iBlast incremental search method to support advanced sequence search tools like MMseqs2 and Diamond.
• To develop a more robust and broadly applicable incremental search framework named iSeqsSearch.
• To enhance the usability of incremental search for the scientific community.

Main Methods:

• Proposed iSeqsSearch, an extension of the iBlast framework.
• Integrated support for MMseqs2 via iMMseqs2 and Diamond via iDiamond.
• Revised the existing iBlast wrapper for improved robustness and community usability.

Main Results:

• iMMseqs2 and iDiamond demonstrated performance nearly identical to the standalone MMseqs2 and Diamond tools.
• High concordance (Pearson correlation > 0.9) was observed between incremental and conventional search methods.
• The iSeqsSearch framework sometimes yielded more hits than conventional MMseqs2 and Diamond searches.

Conclusions:

• The incremental approach using iMMseqs2 and iDiamond is efficient, reusing data while preserving accuracy and result concordance.
• This method effectively reduces resource waste in searching large, growing genomic and proteomic databases.
• Sample code and data are publicly available for broader adoption and research.