DeepPep, is a protein identification software which uses deep-convolutional neural network to predict the protein set from a proteomics mixture, given the sequence universe of possible proteins and a target peptide profile.
git clone https://github.com/ameenetemady/MyCommon.git
git clone https://github.com/IBPA/DeepPep.git
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Step1: prepare a directory containing your input files (with exact names):
identification.tsv: tab-delimeted file: column1: peptide, column2: protein name, column3: identification probabilitydb.fasta: reference protein database in fasta format.
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Step2:
python run.py directoryName
Upon completion, pred.csv will contain the predicted protein identification probabilities.
There are 7 example datasets (used for benchmarking in the paper). Each dataset is generated from MS/MS raw files using TPP pipeline. For example, to run the 18Mix benchmark dataset, simply run the following:
python run.py data/18Mix
If you have any questions about DeepPep, please contact Minseung Kim (msgkim@ucdavis.edu) or Ameen Eetemadi (eetemadi@ucdavis.edu).
M. Kim, A. Eetemadi, and I. Tagkopoulos, “DeepPep: deep proteome inference from peptide profiling”, PLoS Computational Biology (2017) [link]
See the LICENSE file for license rights and limitations (Apache2.0).
This work was supported by a grant from Mars, Inc. and NSF award 1516695.