Literature DB >> 30474983

Protein Inference Using PIA Workflows and PSI Standard File Formats.

Julian Uszkoreit1, Yasset Perez-Riverol2, Britta Eggers1, Katrin Marcus1, Martin Eisenacher1.   

Abstract

Proteomics using LC-MS/MS has become one of the main methods to analyze the proteins in biological samples in high-throughput. But the existing mass-spectrometry instruments are still limited with respect to resolution and measurable mass ranges, which is one of the main reasons why shotgun proteomics is the major approach. Here proteins are digested, which leads to the identification and quantification of peptides instead. While often neglected, the important step of protein inference needs to be conducted to infer from the identified peptides to the actual proteins in the original sample. In this work, we highlight some of the previously published and newly added features of the tool PIA - Protein Inference Algorithms, which helps the user with the protein inference of measured samples. We also highlight the importance of the usage of PSI standard file formats, as PIA is the only current software supporting all available standards used for spectrum identification and protein inference. Additionally, we briefly describe the benefits of working with workflow environments for proteomics analyses and show the new features of the PIA nodes for the KNIME Analytics Platform. Finally, we benchmark PIA against a recently published data set for isoform detection. PIA is open source and available for download on GitHub ( https://github.com/mpc-bioinformatics/pia ) or directly via the community extensions inside the KNIME analytics platform.

Entities:  

Keywords:  computational proteomics; protein inference; protein isoforms; spectrum identification; standard formats; workflows

Mesh:

Substances:

Year:  2018        PMID: 30474983     DOI: 10.1021/acs.jproteome.8b00723

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  17 in total

1.  Establishing a Custom-Fit Data-Independent Acquisition Method for Label-Free Proteomics.

Authors:  Britta Eggers; Martin Eisenacher; Katrin Marcus; Julian Uszkoreit
Journal:  Methods Mol Biol       Date:  2021

2.  Quantitative Mass Spectrometry-Based Proteomics: An Overview.

Authors:  Svitlana Rozanova; Katalin Barkovits; Miroslav Nikolov; Carla Schmidt; Henning Urlaub; Katrin Marcus
Journal:  Methods Mol Biol       Date:  2021

3.  Dataset containing physiological amounts of spike-in proteins into murine C2C12 background as a ground truth quantitative LC-MS/MS reference.

Authors:  Julian Uszkoreit; Katalin Barkovits; Sandra Pacharra; Kathy Pfeiffer; Simone Steinbach; Katrin Marcus; Martin Eisenacher
Journal:  Data Brief       Date:  2022-07-04

4.  Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs.

Authors:  Karin Schork; Michael Turewicz; Julian Uszkoreit; Jörg Rahnenführer; Martin Eisenacher
Journal:  PLoS One       Date:  2022-10-21       Impact factor: 3.752

5.  Improved Protein Inference from Multiple Protease Bottom-Up Mass Spectrometry Data.

Authors:  Rachel M Miller; Robert J Millikin; Connor V Hoffmann; Stefan K Solntsev; Gloria M Sheynkman; Michael R Shortreed; Lloyd M Smith
Journal:  J Proteome Res       Date:  2019-08-23       Impact factor: 4.466

6.  EPIFANY: A Method for Efficient High-Confidence Protein Inference.

Authors:  Julianus Pfeuffer; Timo Sachsenberg; Tjeerd M H Dijkstra; Oliver Serang; Knut Reinert; Oliver Kohlbacher
Journal:  J Proteome Res       Date:  2020-02-13       Impact factor: 4.466

7.  Label-Free Method Development for Hydroxyproline PTM Mapping in Human Plasma Proteome.

Authors:  Shakilur Rahman; Gourab Bhattacharje; Debabrata Dutta; Swarnendu Bag; Bidhan Chandra Sing; Jyotirmoy Chatterjee; Amit Basak; Amit Kumar Das
Journal:  Protein J       Date:  2021-04-11       Impact factor: 2.371

Review 8.  Scalable Data Analysis in Proteomics and Metabolomics Using BioContainers and Workflows Engines.

Authors:  Yasset Perez-Riverol; Pablo Moreno
Journal:  Proteomics       Date:  2019-12-18       Impact factor: 5.393

9.  Transcriptome and Proteome Analysis in LUHMES Cells Overexpressing Alpha-Synuclein.

Authors:  Matthias Höllerhage; Markus Stepath; Michael Kohl; Kathy Pfeiffer; Oscar Wing Ho Chua; Linghan Duan; Franziska Hopfner; Martin Eisenacher; Katrin Marcus; Günter U Höglinger
Journal:  Front Neurol       Date:  2022-04-11       Impact factor: 4.003

10.  Let me infuse this for you - A way to solve the first YPIC challenge.

Authors:  Britta Eggers; Sandra Pacharra; Martin Eisenacher; Katrin Marcus; Julian Uszkoreit
Journal:  EuPA Open Proteom       Date:  2019-08-16
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