SWPepNovo: An Efficient De Novo Peptide Sequencing Tool for Large-scale MS/MS Spectra Analysis

Abstract

Tandem mass spectrometry (MS/MS)-based de novo peptide sequencing is a powerful method for high-throughput protein analysis. However, the explosively increasing size of MS/MS spectra dataset inevitably and exponentially raises the computational demand of existing de novo peptide sequencing methods, which is an issue urgently to be solved in computational biology. This paper introduces an efficient tool based on SW26010 many-core processor, namely SWPepNovo, to process the large-scale peptide MS/MS spectra using a parallel peptide spectrum matches (PSMs) algorithm. Our design employs a two-level parallelization mechanism: (1) the task-level parallelism between MPEs using MPI based on a data transformation method and a dynamic feedback task scheduling algorithm, (2) the thread-level parallelism across CPEs using asynchronous task transfer and multithreading. Moreover, three optimization strategies, including vectorization, double buffering and memory access optimizations, have been employed to overcome both the compute-bound and the memory-bound bottlenecks in the parallel PSMs algorithm. The results of experiments conducted on multiple spectra datasets demonstrate the performance of SWPepNovo against three state-of-the-art tools for peptide sequencing, including PepNovo+, PEAKS and DeepNovo-DIA. The SWPepNovo also shows high scalability in experiments on extremely large datasets sized up to 11.22 GB. The software and the parameter settings are available at https://github.com/ChuangLi99/SWPepNovo.

Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis

“…This new approach calibrates the matching results from a target-decoy database with sample-specific controls, and can significantly improve the accuracy of isoform identification in non-canonical proteomes. Parallel PSM processing algorithms for large scale proteomics dataset have also been implemented [33].…”

Communication Lower-Bounds for Distributed-Memory Computations for Mass Spectrometry based Omics Data

“…To confirm our lower-bounds that we have proved for the existing methods, and lowerbounds on communication that might be possible we did a thorough evaluation of the existing methods. These existing methods [18,19,20,7,13,11,21,16,12,22,15,14] included MPI-based memory-distributed implementations, Map-Reduce/Hadoop implementations, and GPU-based methods. Since we are assuming a memory-distributed architecture for our bounds; we have concentrated on those studies.…”

“…For evaluation, we downloaded all the results [18,19,20,7,13,11,21,16,12,22,15,14] that have been reported till date. This information included, the database size, the number of spectra, serial and parallel times, and the speedups.…”

Omics-Driven Biotechnology for Industrial Applications

“…Proteomics data analysis tools are generally used for protein identification (via bioinformatics) and quantification, and bioinformatics techniques tools used to process the proteomics data. A few examples of data analysis tools that are used for the identification of peptides and proteins include Mascot (Eng et al, 1994 ), Swiss-Prot (Bairoch and Boeckmann, 1994 ), Sequest (Perkins et al, 1999 ), Tandem (Craig and Beavis, 2004 ), Skyline (MacLean et al, 2010 ), Uni-Prot, 1 UniNovo (Jeong et al, 2013 ), and SWPepNovo (Li et al, 2019 ). Such algorithm-based software were developed to match the MS collected data from peptide/protein analysis to their base peptides/proteins and with in silico predicted intact masses and fragmentation patterns (Urgen Cox and Mann, 2011 ).…”