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Proteomics

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

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

Kenneth Verheggen¹, Harald Barsnes, Lennart Martens

¹Department of Medical Protein Research, VIB, Ghent, Belgium; Department of Biochemistry, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.

|November 29, 2013

Summary

This summary is machine-generated.

Distributed computing offers solutions for handling large proteomics datasets that exceed desktop capabilities. This review explores current distributed computing techniques and their applications in proteomics, highlighting benefits and potential challenges.

Keywords:

Bioinformatics Cloud computing Crowdsourcing Distributed computing Parallelized computing

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Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Modern proteomics experiments produce vast and complex datasets.
Traditional desktop computing struggles to manage the storage and processing demands of this data.
The increasing scale of proteomic data necessitates advanced computational approaches.

Purpose of the Study:

To review current distributed computing techniques relevant to proteomics.
To provide examples of distributed computing applications in the field.
To discuss the advantages and potential drawbacks of using distributed computing in proteomics.

Main Methods:

Literature review of distributed computing methodologies.
Analysis of case studies in proteomics data processing.
Synthesis of information on benefits and challenges.

Main Results:

Distributed computing architectures are increasingly adopted in proteomics.
Various techniques exist for managing large-scale proteomic data.
Successful applications demonstrate significant improvements in processing capabilities.

Conclusions:

Distributed computing is essential for modern proteomics research.
Understanding the benefits and pitfalls is crucial for effective implementation.
Future advancements in distributed systems will further support proteomic data analysis.