Welcome to xiRT’s documentation!

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xiRT is a versatile python package for multi-dimensional retention time prediction for linear and crosslinked peptides.

xiRT requires identified peptide sequences with an assigned confidence (FDR) to learn the retention behavior from multiple dimensions. The high confidence identifications are necessary to reduce the noise in the data which allows more accurate retention time prediction. However, typically we want to supply higher FDR (>1%) data to also predict the retention times for peptide spectrum matches where the search score was not sufficient for passing the FDR cutoff. Post-search validation algorithms such as percolator can then be used to rescore the given set of PSMs with the predicted retention times.

Approach.

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xiRT uses a deep neural network architecture to realize the simultaneous learning for multiple retention times. In brief, xiRT builds a multi-layer network that can be divided into a Siamese part and individual task subnetworks. The Siamese part takes the peptide sequences as input and applies an embedding and recurrent function to the input. For linear peptides the output of the recurrent layer is directly forwarded to the task subnetworks. For crosslinked peptides, each peptide has its own input and after the recurrent layer the two outputs are first combined and then passed towards the individual task networks. In contrast, to typical regression models the input data (peptide) sequences are not transformed into features but rather the entire peptide sequence including modifications is used as input.

Supported Prediction Tasks

xiRT is versatile in the input and experimental design. An arbitrary number of prefractionation methods are supported as well as a standard reversed phase RT prediction. In addition, similar tasks such has collision-cross section prediction can be learned.

Contents:

Indices and tables