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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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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Ribosome Profiling02:24

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
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Bioinformatics Methods to Deduce Biological Interpretation from Proteomics Data.

Krishna Patel1,2, Manika Singh1, Harsha Gowda3,4

  • 1Institute of Bioinformatics, Discoverer Building, International Technology Park, Whitefield, Bangalore, 560066, India.

Methods in Molecular Biology (Clifton, N.J.)
|December 16, 2016
PubMed
Summary
This summary is machine-generated.

Interpreting large proteomics datasets is difficult. This chapter reviews user-friendly bioinformatics tools and analyses to help biologists understand complex proteomic data effectively.

Keywords:
EnrichmentFunRichGene ontologyNetPathPathwaysPhosphoproteomePost-translational modificationsReactome

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput proteomics generates vast datasets, posing significant challenges for biological interpretation.
  • Meaningful analysis of large-scale proteomics data requires specialized computational approaches.

Purpose of the Study:

  • To provide a comprehensive overview of analytical methods for large-scale proteomics data.
  • To highlight key bioinformatics tools and resources for facilitating data interpretation.
  • To guide biologists in utilizing computational approaches for proteomics research.

Main Methods:

  • Review of established and emerging bioinformatics tools for proteomics data analysis.
  • Description of various analytical techniques applicable to large-scale datasets.
  • Identification of user-friendly web-based and stand-alone software solutions.

Main Results:

  • A curated selection of computational tools and resources for proteomics data analysis is presented.
  • Guidance on performing diverse biological interpretations of complex proteomics datasets is provided.
  • Emphasis on the accessibility and usability of these tools for biologists.

Conclusions:

  • Numerous bioinformatics tools are available to simplify the interpretation of high-throughput proteomics data.
  • Biologists can leverage these user-friendly resources to gain deeper biological insights from large datasets.
  • Effective utilization of computational tools is crucial for advancing proteomics research.