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Benchmarking mass spectrometry based proteomics algorithms using a simulated database.

ABSTRACT: Protein sequencing algorithms process data from a variety of instruments that has been generated under diverse experimental conditions. Currently there is no way to predict the accuracy of an algorithm for a given data set. Most of the published algorithms and associated software has been evaluated on limited number of experimental data sets. However, these performance evaluations do not cover the complete search space the algorithmand the software might encounter in real-world. To this end, we present a database of simulated spectra that can be used to benchmark any spectra to peptide search engine. We demonstrate the usability of this database by bench marking two popular peptide sequencing engines. We show wide variation in the accuracy of peptide deductions and a complete quality profile of a given algorithm can be useful for practitioners and algorithm developers. All benchmarking data is available at https://users.cs.fiu.edu/~fsaeed/Benchmark.html.

SUBMITTER: Awan MG

PROVIDER: S-EPMC8130870 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Benchmarking mass spectrometry based proteomics algorithms using a simulated database.

Awan Muaaz Gul MG Awan Abdullah Gul AG Saeed Fahad F

Network modeling and analysis in health informatics and bioinformatics 20210326

Protein sequencing algorithms process data from a variety of instruments that has been generated under diverse experimental conditions. Currently there is no way to predict the accuracy of an algorithm for a given data set. Most of the published algorithms and associated software has been evaluated on limited number of experimental data sets. However, these performance evaluations do not cover the complete search space the algorithmand the software might encounter in real-world. To this end, we ...[more]

PMID: 34012763

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