DeepFLR is a deep learning-based framework that accurately predicts phosphopeptides spectra and effectively controls false localization rates in phosphoproteomics.
Model construction was performed using python (3.8.3, Anaconda distribution version 5.3.1, https://www.anaconda.com/) with the following packages: FastNLP (0.6.0), pytorch (1.8.1), transformers (4.12.5), bidict (0.22.0) and pyteomics (4.5.5).
Data analysis for FLR estimation was performed using python (3.8.3) with the following packages: pandas (1.0.5) and numpy (1.18.5).
Users can install packages using the command provided in the User Guide, or use the “pip install -r requirements.txt” command to install all the required packages.
Tutorials are avaliable in User guide.docx.
Users can either use the model parameters used in DeepFLR or finetune the model or retrain a model following the User guide.docx. The Model parameters "best__2deepchargeModelms2_bert_mediancos_2021-09-20-01-17-50-729399" (329 MB) can be downloaded from Model and put it in phosT folder.
Waiting for publication...(paper)
DeepFLR is distributed under a BSD license. See the LICENSE file for details.
Please report any problems directly to the github issue tracker. Also, you can send feedback to liang_qiao@fudan.edu.cn.