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Related Concept Videos

Proteomics01:33

Proteomics

8.9K
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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Ribosome Profiling02:24

Ribosome Profiling

3.9K
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
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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Related Experiment Video

Updated: Nov 18, 2025

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

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Methods for Proteogenomics Data Analysis, Challenges, and Scalability Bottlenecks: A Survey.

Muhammad Usman Tariq1, Muhammad Haseeb1, Mohammed Aledhari2

  • 1School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA.

IEEE Access : Practical Innovations, Open Solutions
|February 4, 2021
PubMed
Summary
This summary is machine-generated.

Big Data Proteogenomics integrates genomics and proteomics for novel protein discovery. Current tools struggle with large datasets, highlighting the need for scalable solutions like high-performance computing (HPC).

Keywords:
Proteogenomicsbig datagenomicshigh-performance computingmass spectrometryproteomicsworkflow

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Area of Science:

  • Bioinformatics
  • Genomics
  • Proteomics

Background:

  • Big Data Proteogenomics combines high-throughput mass spectrometry (MS) proteomics and next-generation sequencing (NGS) genomics.
  • Integrated analysis aids in discovering novel proteins from genomic and transcriptomic data.
  • Numerous proteogenomic tools have emerged for tasks like protein mapping and genome annotation.

Purpose of the Study:

  • To review current tools for analyzing big data proteogenomics datasets.
  • To critically analyze the merits and pitfalls of existing techniques.
  • To identify bottlenecks and recommend future design improvements for scalability.

Main Methods:

  • Review of existing proteogenomic analysis tools.
  • Critical analysis of scalability issues in processing large datasets.
  • Evaluation of high-performance computing (HPC) as a scalable solution.

Main Results:

  • Most current proteogenomic tools lack scalability for large datasets.
  • Processing a small dataset (1 million spectra, 3 GB genome) can take over 15 days.
  • Scalability is a major challenge due to the large size of proteogenomic databases.

Conclusions:

  • Existing proteogenomic tools face significant scalability challenges.
  • Future workflows must address bottlenecks to handle increasing data volumes.
  • High-performance computing (HPC) offers a promising solution for scalable big data proteogenomics analysis.