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Related Concept Videos

Proteomics01:33

Proteomics

7.2K
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...
7.2K

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Updated: May 26, 2025

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
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A Scalable, Web-Based Platform for Proteomics Data Processing, Result Storage and Analysis.

Markus Schneider1, Daniel P Zolg1, Patroklos Samaras1

  • 1MSAID GmbH, Garching b. München 85748, Germany.

Journal of Proteome Research
|February 21, 2025
PubMed
Summary
This summary is machine-generated.

The MSAID Platform automates proteomics data processing, transforming raw data into biological insights. This cloud-based solution enhances efficiency and accessibility for researchers, streamlining complex proteomic workflows.

Keywords:
AWSCHIMERYSSaaScloudcompute infrastructuredata processingpipelineplatformproteomicsscalable

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Exponential growth in proteomics data overwhelms traditional processing pipelines.
  • Existing workflows are fragmented, rely on manual scripting, and are difficult to scale.
  • Challenges include high maintenance costs and limited accessibility for researchers.

Purpose of the Study:

  • To introduce the MSAID Platform, a unified, automated solution for proteomics data processing.
  • To consolidate fragmented software into a cohesive, API-driven pipeline.
  • To make advanced proteomic analysis accessible to a wider scientific audience.

Main Methods:

  • Utilizes a cloud-native search algorithm (CHIMERYS).
  • Employs scalable cloud compute instances and data lakes for efficient processing.
  • Offers unified services covering the entire workflow from raw data to biological insights.
  • Provides flexible user interaction via web interface, CLI client, or API.

Main Results:

  • Facilitates efficient processing of large proteomics datasets.
  • Enables automation of data processing through command-line interface.
  • Supports systematic storage, analysis, and visualization of results.
  • Data lake allows for elastic storage growth and unified querying for large-scale analyses.

Conclusions:

  • The MSAID Platform streamlines research by automating complex proteomic workflows.
  • It enhances efficiency, scalability, and accessibility of proteomics data analysis.
  • The platform democratizes advanced proteomic insights for a broader scientific community.