MegaPX: fast and space-efficient peptide assignment method using IBF-based multi-indexing

doi:10.1093/bioinformatics/btag134

MegaPX: fast and space-efficient peptide assignment method using IBF-based multi-indexing

2026-05-01

0

PRE

AI

DOI：10.1093/bioinformatics/btag134

Original

Original request for help

Save

Abstract

En 中文

Motivation A central problem for metaproteomic analysis is the often-unknown taxonomic composition of the analyzed microbiomes. Using a database search, the standard approach requires prior knowledge of which proteins and taxa to include in the protein reference database or to use tailored metagenome-derived databases, which are expensive and error-prone in their generation. A possible strategy to circumvent this database search issue is de novo sequencing, where peptide sequences are directly identified from mass spectra. However, these sequences must still be mapped back to potentially extensive databases. Here, alignment-based approaches enable robust and precise results, with the potential drawback of high memory usage and long run times.Results We present MegaPX, a software for rapidly classifying de novo peptide sequences against large protein databases. MegaPX implemented as a C++-based tool, uses an alignment-free, k-mer approach as a taxonomic classification method with the possibility of generating mutated reference databases for error-tolerant searching. It uses various algorithms, including interleaved Bloom filters, to efficiently compute approximate membership queries, ensuring fast processing times while querying and indexing large databases in a multi-indexing fashion. We demonstrate the potential of MegaPX by analyzing different samples, including metaproteomics, against extensive reference databases, highlighting its use as a fast screening tool.

Keywords:

IDENTIFICATION

SEQUENCE

MegaPX: fast and space-efficient peptide assignment method using IBF-based multi-indexing

Abstract

Journal

Organization