Distributed computing and data storage in proteomics: Many hands make light work, and a stronger memory

Verheggen, Kenneth; Barsnes, Harald; Martens, Lennart

doi:10.1002/pmic.201300288

Cited by 22 publications

(19 citation statements)

References 86 publications

(86 reference statements)

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“…This is particularly problematic in the case of proteogenomics studies and metaproteomics . Distributed computing can help overcoming some of these limitations, through the use of grid or cloud computing . This way, extensive processing power can be made available to the community at large, notably through the establishment of dedicated environments, like the Galaxy project .…”

Section: Conclusion and Overviewmentioning

confidence: 99%

“…126 Distributed computing can help overcoming some of these limitations, through the use of grid or cloud computing. 127 This way, extensive processing power can be made available to the community at large, notably through the establishment of dedicated environments, like the Galaxy project. 128,129 It is also possible to distribute tasks on a local cluster of computers, making it possible for most labs to carry out demanding searches even with limited informatics resources.…”

Section: Conclusion and Overviewmentioning

confidence: 99%

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Anatomy and evolution of database search engines—a central component of mass spectrometry based proteomic workflows

Verheggen

Ræder

Berven

et al. 2017

Mass Spectrometry Reviews

Self Cite

111

108

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Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.

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Section: Conclusion and Overviewmentioning

confidence: 99%

Section: Conclusion and Overviewmentioning

confidence: 99%

Anatomy and evolution of database search engines—a central component of mass spectrometry based proteomic workflows

Verheggen

Ræder

Berven

et al. 2017

Mass Spectrometry Reviews

Self Cite

111

108

View full text Add to dashboard Cite

show abstract

“…In the 2010 new emphasis was given to data processing, introducing new data mining methodologies based on multivariate approaches, that is PCA, which are still used nowadays (for a review see ). In 2010 new multivariate algorithms for network building and comprehensive processing of the different omics levels were available, and PTM study allowed an unprecedented power to study biological systems, which are now mainly limited by the need of highly demanding computer systems .…”

Section: Methodological Evolution In Plant Proteomicsmentioning

confidence: 99%

Fourteen years of plant proteomics reflected in Proteomics: Moving from model species and 2DE‐based approaches to orphan species and gel‐free platforms

et al. 2015

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In this article, the topic of plant proteomics is reviewed based on related papers published in the journal Proteomics since publication of the first issue in 2001. In total, around 300 original papers and 41 reviews published in Proteomics between 2000 and 2014 have been surveyed. Our main objective for this review is to help bridge the gap between plant biologists and proteomics technologists, two often very separate groups. Over the past years a number of reviews on plant proteomics have been published . To avoid repetition we have focused on more recent literature published after 2010, and have chosen to rather make continuous reference to older publications. The use of the latest proteomics techniques and their integration with other approaches in the "systems biology" direction are discussed more in detail. Finally we comment on the recent history, state of the art, and future directions of plant proteomics, using publications in Proteomics to illustrate the progress in the field. The review is organized into two major blocks, the first devoted to provide an overview of experimental systems (plants, plant organs, biological processes) and the second one to the methodology.

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“…novel variants and post-translational modifications (6)). However, in order to process these large amounts of (public) data, it is increasingly necessary to use elastic compute resources such as Linux-based cluster environments and cloud infrastructures (7).…”

Section: Introductionmentioning

confidence: 99%

ThermoRawFileParser: modular, scalable and cross-platform RAW file conversion

Hulstaert

Sachsenberg

Walzer

et al. 2019

Preprint

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The field of computational proteomics is approaching the big data age, driven both by a continuous growth in the number of samples analysed per experiment, as well as by the growing amount of data obtained in each analytical run. In order to process these large amounts of data, it is increasingly necessary to use elastic compute resources such as Linux-based cluster environments and cloud infrastructures.Unfortunately, the vast majority of cross-platform proteomics tools are not able to operate directly on the proprietary formats generated by the diverse mass spectrometers. Here, we presented ThermoRawFileParser, an open-source, crossplatform tool that converts Thermo RAW files into open file formats such as MGF and the HUPO-PSI standard file format mzML. To ensure the broadest possible availability, and to increase integration capabilities with popular workflow systems such as Galaxy or Nextflow, we have also built Conda and BioContainers containers around ThermoRawFileParser. In addition, we implemented a user-friendly interface (ThermoRawFileParserGUI) for those users not familiar with command-line tools.

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Distributed computing and data storage in proteomics: Many hands make light work, and a stronger memory

Cited by 22 publications

References 86 publications

Anatomy and evolution of database search engines—a central component of mass spectrometry based proteomic workflows

Anatomy and evolution of database search engines—a central component of mass spectrometry based proteomic workflows

Fourteen years of plant proteomics reflected in Proteomics: Moving from model species and 2DE‐based approaches to orphan species and gel‐free platforms

ThermoRawFileParser: modular, scalable and cross-platform RAW file conversion

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