xiRT 1.0.51

xiRT: Multi-dimensional Retention Time Prediction for Linear and Crosslinked Peptides.

A python package for multi-dimensional retention time prediction for linear and crosslinked peptides using a (siamese) deep neural network architecture.

• Overview
• Description
• Installation

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overview

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").

This readme only gives a brief overview about xiRTs functions and parameters. Please refere to the documentation for more details and examples.

Installation and Usage

xiRT is a python package that comes with a executable python file. To run xiRT follow the steps below.

Installation

To install xiRT simply run the command below. We recommend to use an isolated python environment, for example by using pipenv or conda. Using pipenv:

pipenv shell

pip install xirt

To enable CUDA support, the easiest thing is to create a conda environment. Conda will take care of the CUDA libraries and other dependencies.

conda create --name xirt_env python=3.7

conda activate xirt_env

pip install xirt

conda install tensorflow-gpu

Hint: pydot and graphviz sometimes make trouble when they are installed via pip. If on linux, simply use sudo apt-get install graphviz, on windows download latest graphviz package from here, unzip the content of the file and the bin directory path to the windows PATH variable.

Usage

The command line interface (CLI) requires three inputs:

1. input PSM/CSM file
2. a YAML file to configure the neural network architecture
3. another YAML file to configure the general training / prediction behaviour, called setup-config

To use xiRT these options are put together as shown below:

xirt(.exe) -i peptides.csv -o out_dir -x xirt_params -l learning_params

To adapt the xiRT parameters a yaml config file needs to be prepared. The configuration file is used to determine network parameters (number of neurons, layers, regularization) but also for the definition of the prediction task (classification, regression, ordered regression). Depending on the decoding of the target variable the output layers need to be adapted. For standard RP prediction, regression is essentially the only viable option. For SCX/hSAX (general classification from fractionation experiments) the prediction task can be formulated as classification, regression or ordered regression. For the usage of regression for fractionation it is recommended that the estimated salt concentrations are used as target variable for the prediction (raw fraction numbers are possible too).

input format

short name	explicit column name	description	Example
peptide sequence 1	Peptide1	First peptide sequence for crosslinks	PEPRTIDER
peptide sequence 2	Peptide2	Second peptide sequence for crosslinks, or empty	ELRVIS
link site 1	LinkPos1	Crosslink position in the first peptide (0-based)	3
link site 2	LinkPos2	Crosslink position in the second peptide (0-based	2
precursor charge	Charge	Precursor charge of the crosslinked peptide	3
score	Score	Single score from the search engine	17.12
unique id	CSMID	A unique index for each entry in the result table	0
decoy	isTT	Binary column which is True for any TT identification and False for TD, DD ids	TT
fdr	fdr	Estimated false discovery rate	0.01
fdr level	fdrGroup	String identifier for heteromeric and self links (splitted FDR)	heteromeric

The first four columns should be self explanatory, if not check the sample input. The fifth column ("CSMID") is a unique(!) integer that can be used as to retrieve CSMs. In addition, depending on the number retention time domains that should to be learned/predicted the RT columns need to be present. The column names need to match the configuration in the network parameter yaml.

xiRT config

This file determines the network architecture and training behaviour used in xiRT.

Setup config

This file determines the input data to be used and gives some training procedure options.

Contributors

• Sven Giese

Citation

If you consider xiRT helpful for your work please cite our manuscript. Currently, in preparation.

RappsilberLab

The Rappsilber applies and developes crosslinking chemistry methods, workflows and software. Visit the lab page to learn more about the developed software.

xiSUITE

1. xiVIEW: Graham, M. J.; Combe, C.; Kolbowski, L.; Rappsilber, J. bioRxiv 2019.
2. xiNET: Combe, C. W.; Fischer, L.; Rappsilber, J. Mol. Cell. Proteomics 2015.
3. xiSPEC: Kolbowski, L.; Combe, C.; Rappsilber, J. Nucleic Acids Res. 2018, 46 (W1), W473–W478.
4. xiSEARCH: Mendes, M. L.; Fischer, L.; Chen, Z. A.; Barbon, M.; O’Reilly, F. J.; Giese, S. H.; Bohlke‐Schneider, M.; Belsom, A.; Dau, T.; Combe, C. W.; Graham, M.; Eisele, M. R.; Baumeister, W.; Speck, C.; Rappsilber, J. Mol. Syst. Biol. 2019, 15 (9), e8994.