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Related Concept Videos

Proteomics01:33

Proteomics

10.1K
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...
10.1K

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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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Near Real-Time Processing of Proteomics Data Using Hadoop.

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  • 11 School of Computing, University of Dundee , Nethergate, Dundee, United Kingdom .

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|July 23, 2016
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Summary

This study introduces a big data solution using Hadoop and MapReduce for efficient processing of life science data. The methods enable timely analysis of proteomic data from mass spectrometers to identify biological peaks.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics and Proteomics

Background:

  • Life sciences research, particularly in genomics and proteomics, generates massive datasets.
  • Effective data management and processing are critical for extracting biological insights.
  • Timely preprocessing of complex data is a significant challenge in the field.

Purpose of the Study:

  • To present a near real-time data processing solution for the life sciences community.
  • To address data management and processing challenges using big data techniques.
  • To apply these techniques to proteomic data generated by mass spectrometry.

Main Methods:

  • Utilized the Hadoop framework for handling large-scale raw data.
  • Implemented a preprocessing pipeline using Java and the MapReduce framework.
  • Developed methods for identifying 2D and 3D peaks within the proteomic data.

Main Results:

  • Demonstrated a viable near real-time processing solution for proteomic data.
  • Successfully applied big data techniques to manage and process mass spectrometry outputs.
  • Developed a framework for efficient identification of significant peaks in complex biological datasets.

Conclusions:

  • Hadoop and MapReduce offer effective solutions for big data challenges in life sciences.
  • The presented methods facilitate timely preprocessing and analysis of proteomic data.
  • This approach enhances the ability to gain biological insights from large-scale experiments.