Bart Mesuere

Abstract Pattern matching is a fundamental challenge in bioinformatics, especially in the fields of genomics, transcriptomics and proteomics. Efficient indexing structures, such as suffix arrays, are critical for searching large datasets. While sparse suffix arrays offer significant memory savings compared to full suffix arrays, they typically still require the construction of a full suffix array prior to a sampling step, resulting in substantial memory overhead during the construction phase.

Abstract Background: Metaproteomics, the study of the collective proteome within microbial ecosystems, has gained increasing interest over the past decade. However, peptide identification rates in metaproteomics remain low compared to single-species proteomics. A key challenge is the limited sequence resolution of current identification algorithms, which were primarily designed for single-species analyses.

Abstract Metaproteomics, the large-scale study of proteins from microbial communities, presents complex challenges in taxonomic inference due to sequence homologies between proteins within and across taxa. Commonly, taxonomic inference relies on heuristics, and few more advanced methods are available. We introduce the Peptonizer2000, a novel graphical model-based workflow designed to provide high-resolution taxonomic identifications of metaproteomic samples with associated confidence scores.