iSeqsSearch: Incremental Protein Search for iBlast/iMMSeqs2/iDiamond

Abstract

BackgroundThe advancement of sequencing technology has led to a rapid increase in the amount of DNA and protein sequence data; consequently, the size of genomic and proteomic databases is constantly growing. As a result, database searches need to be continually updated to account for the new data being added. Continually re-searching the entire existing dataset, however, wastes resources. Incremental database search can address this problem. MethodsOne recently introduced incremental search method is iBlast, which wraps the BLAST sequence search method with an algorithm to reuse previously processed data and thereby increase search efficiency. The iBlast wrapper, however, must be generalized to support more performant DNA/protein sequence search methods that have been developed, namely MMseqs2 and Diamond. Moreover, the previously published iBlast wrapper has to be revised to be more robust and usable by the general community. ResultsiMMseqs2 and iDiamond, which apply the incremental approach, obtain results nearly identical to those achieved using only MMseqs2 and Diamond. Notably, when comparing ranking comparison methods such as the Pearson correlation, we observe a high concordance of over 0.9, indicating similar results. Moreover, in some cases, our incremental approach applying iBlast merge function and using m8 formats including the new m8e format provides more hits compared to the conventional MMseqs2 and Diamond. ConclusionThe incremental approach using iMMseqs2 and iDiamond demonstrates efficiency in terms of reusing previously processed data while maintaining high accuracy and concordance in search results. This method can reduce resource waste in continually growing genomic and proteomic database searches. The sample codes are made available at GitHub: https://github.com/EESI/Incremental-Protein-Search.