xiRT - Introduction

xiRT is a deep learning tool to predict the retention times(s) of linear and crosslinked peptides from multiple fractionation dimensions including RP (typically coupled to the mass spectrometer). xiRT was developed with a combination of SCX / hSAX / RP chromatography. However, xiRT supports all available chromatography methods.

xiRT requires the columns shown in the table below. Importantly, the xiRT framework requires that CSM are sorted such that in the Peptide1 - Peptide2, Peptide1 is the longer or lexicographically larger one for crosslinked RT predictions.

Description

xiRT is meant to be used to generate additional information about CSMs for machine learning-based rescoring frameworks (similar to percolator). However, xiRT also delivers RT prediction for various scenarios. Therefore xiRT offers several training / prediction modes that need to be configured depending on the use case. At the moment training, prediction, crossvalidation are the supported modes. - training: trains xiRT on the input CSMs (using 10% for validation) and stores a trained model - prediction: use a pretrained model and predict RTs for the input CSMs - crossvalidation: load/train a model and predict RTs for all data points without using them in the training process. Requires the training of several models during CV

Note: all modes can be supplemented by using a pretrained model (“transfer learning”).