PROTEINCHALLENGE:: Crowd sourcing in proteomics analysis and software development

Sarah F. Martin; Heiner Falkenberg; Thomas F. Dyrlund; Guennadi A. Khoudoli; Craig J. Mageean; Rune Linding

doi:10.1016/j.jprot.2012.11.014

PROTEINCHALLENGE: Crowd sourcing in proteomics analysis and software development

Sarah F. Martin, Heiner Falkenberg, Thomas F. Dyrlund, Guennadi A. Khoudoli, Craig J. Mageean, Rune Linding

Research output: Contribution to journal › Article › peer-review

19 Citations (Scopus)

Abstract

In large-scale proteomics studies there is a temptation, after months of experimental work, to plug resulting data into a convenient if poorly implemented set of tools, which may neither do the data justice nor help answer the scientific question. In this paper we have captured key concerns, including arguments for community-wide open source software development and "big data" compatible solutions for the future. For the meantime, we have laid out ten top tips for data processing. With these at hand, a first large-scale proteomics analysis hopefully becomes less daunting to navigate.

However there is clearly a real need for robust tools, standard operating procedures and general acceptance of best practises. Thus we submit to the proteomics community a call for a community-wide open set of proteomics analysis challenges-PROTEINCHALLENGE-that directly target and compare data analysis workflows, with the aim of setting a community-driven gold standard for data handling, reporting and sharing.

Original language	English
Pages (from-to)	41-46
Number of pages	6
Journal	Journal of Proteomics
Volume	88
DOIs	https://doi.org/10.1016/j.jprot.2012.11.014
Publication status	Published - 2 Aug 2013

Keywords

Software
ALGORITHMS
PHOSPHORYLATION NETWORKS
QUANTIFICATION
INFORMATICS
Benchmarking
Data analysis
Open source
Community challenge
RESOLUTION
MODEL
TOOL
Crowd sourcing
YEAST
MASS-SPECTROMETRY

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10.1016/j.jprot.2012.11.014

Cite this

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title = "PROTEINCHALLENGE:: Crowd sourcing in proteomics analysis and software development",

abstract = "In large-scale proteomics studies there is a temptation, after months of experimental work, to plug resulting data into a convenient if poorly implemented set of tools, which may neither do the data justice nor help answer the scientific question. In this paper we have captured key concerns, including arguments for community-wide open source software development and {"}big data{"} compatible solutions for the future. For the meantime, we have laid out ten top tips for data processing. With these at hand, a first large-scale proteomics analysis hopefully becomes less daunting to navigate.However there is clearly a real need for robust tools, standard operating procedures and general acceptance of best practises. Thus we submit to the proteomics community a call for a community-wide open set of proteomics analysis challenges-PROTEINCHALLENGE-that directly target and compare data analysis workflows, with the aim of setting a community-driven gold standard for data handling, reporting and sharing.This article is part of a Special Issue entitled: New Horizons and Applications for Proteomics [EuPA 2012]. (C) 2013 Elsevier B.V. All rights reserved.",

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author = "Martin, {Sarah F.} and Heiner Falkenberg and Dyrlund, {Thomas F.} and Khoudoli, {Guennadi A.} and Mageean, {Craig J.} and Rune Linding",

year = "2013",

month = aug,

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doi = "10.1016/j.jprot.2012.11.014",

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AU - Martin, Sarah F.

AU - Falkenberg, Heiner

AU - Dyrlund, Thomas F.

AU - Khoudoli, Guennadi A.

AU - Mageean, Craig J.

AU - Linding, Rune

PY - 2013/8/2

Y1 - 2013/8/2

N2 - In large-scale proteomics studies there is a temptation, after months of experimental work, to plug resulting data into a convenient if poorly implemented set of tools, which may neither do the data justice nor help answer the scientific question. In this paper we have captured key concerns, including arguments for community-wide open source software development and "big data" compatible solutions for the future. For the meantime, we have laid out ten top tips for data processing. With these at hand, a first large-scale proteomics analysis hopefully becomes less daunting to navigate.However there is clearly a real need for robust tools, standard operating procedures and general acceptance of best practises. Thus we submit to the proteomics community a call for a community-wide open set of proteomics analysis challenges-PROTEINCHALLENGE-that directly target and compare data analysis workflows, with the aim of setting a community-driven gold standard for data handling, reporting and sharing.This article is part of a Special Issue entitled: New Horizons and Applications for Proteomics [EuPA 2012]. (C) 2013 Elsevier B.V. All rights reserved.

AB - In large-scale proteomics studies there is a temptation, after months of experimental work, to plug resulting data into a convenient if poorly implemented set of tools, which may neither do the data justice nor help answer the scientific question. In this paper we have captured key concerns, including arguments for community-wide open source software development and "big data" compatible solutions for the future. For the meantime, we have laid out ten top tips for data processing. With these at hand, a first large-scale proteomics analysis hopefully becomes less daunting to navigate.However there is clearly a real need for robust tools, standard operating procedures and general acceptance of best practises. Thus we submit to the proteomics community a call for a community-wide open set of proteomics analysis challenges-PROTEINCHALLENGE-that directly target and compare data analysis workflows, with the aim of setting a community-driven gold standard for data handling, reporting and sharing.This article is part of a Special Issue entitled: New Horizons and Applications for Proteomics [EuPA 2012]. (C) 2013 Elsevier B.V. All rights reserved.

KW - Software

KW - ALGORITHMS

KW - PHOSPHORYLATION NETWORKS

KW - QUANTIFICATION

KW - INFORMATICS

KW - Benchmarking

KW - Data analysis

KW - Open source

KW - Community challenge

KW - RESOLUTION

KW - MODEL

KW - TOOL

KW - Crowd sourcing

KW - YEAST

KW - MASS-SPECTROMETRY

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SN - 1874-3919

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SP - 41

EP - 46

JO - Journal of Proteomics

JF - Journal of Proteomics

ER -

PROTEINCHALLENGE: Crowd sourcing in proteomics analysis and software development

Abstract

Keywords

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