Spectral prediction features as a solution for the search space size problem in proteogenomics

Verbruggen et al. (2021) address a core challenge in proteogenomics: as Ribo-seq and Nanopore RNA-seq expand the protein sequence search space, distinguishing true from false peptide-to-spectrum matches becomes increasingly difficult. The authors demonstrate that spectral intensity prediction features, extracted from the MS2PIP and Prosit predictors and integrated with canonical MaxQuant scores via the Percolator post-processing tool, substantially improve peptide identification confidence in large, Ribo-seq-derived databases. Applied to search spaces built from combined ribosome profiling and Nanopore long-read sequencing data, this approach increases sensitivity without compromising specificity, offering a practical solution for high-confidence novel proteoform discovery.

Search all posts

Popular news & events

Knowledge-for-Growth-2026_OHMX.bio

Tags

Do you have questions about the Knowledge for Growth (KfG)?

Fill out the form below and our experts will get back to you as soon as possible!

Search all posts

Popular news & events

Tags