Literature DB >> 30216669

MaSS-Simulator: A Highly Configurable Simulator for Generating MS/MS Datasets for Benchmarking of Proteomics Algorithms.

Muaaz Gul Awan1, Fahad Saeed2.   

Abstract

Mass Spectrometry (MS)-based proteomics has become an essential tool in the study of proteins. With the advent of modern MS machines huge amounts of data is being generated, which can only be processed by novel algorithmic tools. However, in the absence of data benchmarks and ground truth datasets algorithmic integrity testing and reproducibility is a challenging problem. To this end, MaSS-Simulator has been presented, which is an easy to use simulator and can be configured to simulate MS/MS datasets for a wide variety of conditions with known ground truths. MaSS-Simulator offers many configuration options to allow the user a great degree of control over the test datasets, which can enable rigorous and large- scale testing of any proteomics algorithm. MaSS-Simulator is assessed by comparing its performance against experimentally generated spectra and spectra obtained from NIST collections of spectral library. The results show that MaSS-Simulator generated spectra match closely with real-spectra and have a relative-error distribution centered around 25%. In contrast, the theoretical spectra for same peptides have relative-error distribution centered around 150%. MaSS-Simulator will enable developers to specifically highlight the capabilities of their algorithms and provide a strong proof of any pitfalls they might face. Source code, executables, and a user manual for MaSS-Simulator can be downloaded from https://github.com/pcdslab/MaSS-Simulator.
© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  benchmarking; big data; mass spectrometry; simulator

Mesh:

Substances:

Year:  2018        PMID: 30216669      PMCID: PMC6400488          DOI: 10.1002/pmic.201800206

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  19 in total

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  2 in total

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2.  SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions.

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  2 in total

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