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Related Concept Videos

Proteomics01:33

Proteomics

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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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Efficient Processing of Models for Large-scale Shotgun Proteomics Data.

Himanshu Grover1, Vanathi Gopalakrishnan2

  • 1Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206-3701 USA ( hig2@pitt.edu ).

International Conference on Collaborative Computing : Networking, Applications and Worksharing (Collaboratecom). International Conference on Collaborative Computing: Networking, Applications, and Worksharing
|October 14, 2014
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Summary
This summary is machine-generated.

Mass-spectrometry (MS) based proteomics generates large datasets. This study optimizes data analysis through database indexing and parallelization, improving peptide and protein identification efficiency for systems biology research.

Keywords:
BioinformaticsHigh-throughput ProteomicsIndexingMultiprocessingParallelization

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Area of Science:

  • Proteomics and Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Mass-spectrometry (MS) based proteomics is crucial for understanding cellular protein composition.
  • Bioinformatics analysis is essential for interpreting large and complex MS datasets.
  • Peptide and protein identification involves computationally intensive database searches and scoring.

Purpose of the Study:

  • To present strategies for efficient handling of large MS proteomics datasets.
  • To improve the computational workflow for peptide and protein identification.
  • To identify challenges and opportunities in parallelizable computational biology problems.

Main Methods:

  • Implementation of database indexing for faster data retrieval.
  • Application of parallelization techniques across multiprocessor architectures.
  • Workflow optimization for large-scale MS data analysis.

Main Results:

  • Demonstrated enhanced efficiency in processing large MS datasets.
  • Achieved improved performance in peptide and protein identification.
  • Provided practical insights into computational challenges and solutions.

Conclusions:

  • Database indexing and parallelization significantly enhance MS data analysis efficiency.
  • Optimized computational approaches are vital for advancing systems biology.
  • The developed methods offer a scalable solution for complex, parallelizable bioinformatics tasks.