DeepSec: a deep learning framework for secreted protein discovery in human body fluids.

Motivation Human proteins that are secreted into different body fluids from various cells can be promising disease indicators. Modern proteomics research empowered by both qualitative and quantitative profiling techniques has made great progress in protein discovery in various human fluids. However, due to the large numbers of proteins and diverse modifications present in the fluids, as well as the existing technical limits of major proteomics platforms (e.g., mass spectrometry), large discrepancies are often generated from different experimental studies. As a result, a comprehensive proteomics landscape across major human fluids are not well determined. Results To facilitate this process, we have developed a deep learning framework, named DeepSec, to identify secreted proteins in twelve types of human body fluids. DeepSec adopts an end-to-end sequence-based approach, where a Convolutional Neural Network (CNN) is built to learn the abstract sequence features followed by a Bidirectional Gated Recurrent Unit (BGRU) with fully connected layer for protein classification. DeepSec has demonstrated promising performances with average AUCs of 0.85-0.94 on testing datasets in each type of fluids, which outperforms existing state-of-the-art methods available mostly on blood proteins. As an illustration of how to apply DeepSec in biomarker discovery research, we conducted a case study on kidney cancer by using genomics data from the cancer genome atlas (TCGA) and have identified 104 possible marker proteins. Availability DeepSec is available at https://bmbl.bmi.osumc.edu/deepsec/. Supplementary information Supplement ary data are available at Bioinformatics online.