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Related Concept Videos

Proteomics01:33

Proteomics

7.4K
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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Peptide Identification Using Tandem Mass Spectrometry01:33

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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High-Resolution Mass Spectrometry (HRMS)01:15

High-Resolution Mass Spectrometry (HRMS)

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The resolution of a mass spectrometer depends on the efficiency of separating ions with different ion masses. The mass of an atom is approximated to the sum of the masses of protons and neutrons inside, considering the masses of protons and neutrons as equal. However, the masses of the proton (1.6726 × 10−24 g) and neutron (1.6749 × 10−24 g) are not truly equal. There is a minor error in the expression of atomic masses relative to the simplest atom of hydrogen. For...
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Updated: Jul 20, 2025

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Targeted proteomics data interpretation with DeepMRM.

Jungkap Park1, Christopher Wilkins2, Dmitry Avtonomov2

  • 1Bertis, Inc., Seoul 06108, Republic of Korea.

Cell Reports Methods
|August 3, 2023
PubMed
Summary
This summary is machine-generated.

DeepMRM, a deep learning tool, automates targeted proteomics data analysis, improving accuracy and efficiency. This software reduces manual interpretation, enhancing reproducibility and scalability in clinical proteomics research.

Keywords:
Skylinemachine learningmultiple reaction monitoringobject detectionpeak detectionquality controlquantificationselected reaction monitoringtargeted proteomics

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Proteomics

Background:

  • Targeted proteomics is crucial in clinical research but suffers from time-consuming manual data interpretation.
  • This manual process limits the transferability, reproducibility, and scalability of targeted proteomics approaches.
  • Automating data analysis is essential for advancing clinical proteomics applications.

Purpose of the Study:

  • To introduce DeepMRM, a novel deep learning-based software package for automated targeted proteomics data analysis.
  • To reduce manual intervention in the interpretation of targeted proteomics data.
  • To improve the accuracy, reproducibility, and scalability of targeted proteomics.

Main Methods:

  • Developed DeepMRM using deep learning algorithms for object detection.
  • Evaluated DeepMRM performance on both internal and public targeted proteomics datasets.
  • Compared DeepMRM's accuracy against the established tool Skyline.

Main Results:

  • DeepMRM demonstrated superior accuracy in targeted proteomics data analysis compared to Skyline.
  • The software effectively minimizes the need for manual data interpretation.
  • DeepMRM offers enhanced accuracy and efficiency for analyzing complex proteomics data.

Conclusions:

  • DeepMRM significantly improves the automation and accuracy of targeted proteomics data analysis.
  • The software addresses key limitations in reproducibility and scalability faced by current methods.
  • DeepMRM is available as a stand-alone tool and integrated into Skyline to encourage adoption.