Back

EndoGenius: Enabling comprehensive identification and quantitation of endogenous peptides

Fields, L.; Dang, T. C.; Gray, M. D.; Protya, S. S.; Li, l.

2025-06-13 bioinformatics
10.1101/2025.06.12.659347 bioRxiv
Show abstract

Structured abstractO_ST_ABSSummaryC_ST_ABSThe investigation of endogenous peptides, specifically with respect to neuropeptides, from mass spectrometry data is rife with bioinformatics bottlenecks, stemming from the low in vivo abundance of these analytes, increased susceptibility to degradation, and an immense search space of possible peptides. To address this, we present EndoGenius in its expanded form, strategically designed to optimize the searching for these endogenous peptides complemented with a pipeline designed for tasks including quantitation, spectral library building, motif extraction, and usage with data-independent acquisition workflows. Availability and ImplementationEndoGenius is released as an open-source software package under an MIT License. The EndoGenius package with a user interface can be installed from https://www.lilabs.org/resources. The source code for EndoGenius can be accessed at https://github.com/lingjunli-research/EndoGenius-v2.0. ContactLingjun.Li@wisc.edu

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.