MSBooster: improving peptide identification rates using deep learning-based features.

Kevin L Yang, Fengchao Yu, Guo Ci Teo, Kai Li, Vadim Demichev, Markus Ralser, Alexey I Nesvizhskii

July 2023 Nat Commun

Synopsis of Social media discussions

Several posts highlight the innovative use of deep learning to enhance peptide identification, emphasizing words like 'improving', 'fast', and 'integrated'. The tone reflects curiosity and recognition of technological advancements, with authors referring to the tool’s utility in workflows like immunopeptidomics and single-cell proteomics, suggesting a high perceived impact on the field.

A
Agreement
 Moderate agreement

Most discussions express overall approval or recognition of MSBooster's potential benefits in proteomics research, though some comments are more neutral or brief.

I
Interest
 High level of interest

Posts demonstrate high interest by frequently mentioning deep learning, peptide identification, and the relevance to mass spectrometry, reflecting curiosity about technological advances.

E
Engagement
 Moderate level of engagement

Many mentions reference specific methods and implications such as 'rescoring peptide-spectrum matches' or integration with existing tools, indicating moderate to deep engagement.

I
Impact
 High level of impact

The discussions suggest that users perceive this publication as potentially influential in advancing proteomics workflows, with words like 'improving', 'fast', and 'reliable' highlighting its perceived impact.

Social Mentions

YouTube

1 Videos

Twitter

44 Posts

Metrics

Video Views

17

Total Likes

56

Extended Reach

1,087,675

Social Features

45

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

Enhancing Peptide Identification in Mass Spectrometry with Deep Learning

Enhancing Peptide Identification in Mass Spectrometry with Deep Learning

MSBooster advances peptide identification in mass spectrometry by leveraging deep learning to predict peptide properties such as retention time, ion mobility, and spectra, improving accuracy and integrating seamlessly with existing proteomics workflows.

STEM RTCL TV

October 23, 2023

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Abstract Synopsis

  • MSBooster is a new tool designed to improve peptide identification in mass spectrometry experiments by using deep learning to predict peptide properties like retention time, ion mobility, and MS/MS spectra.
  • It works alongside existing tools like MSFragger and Percolator to rescoring peptide-spectrum matches, enhancing the accuracy of identifications across various workflows including immunopeptidomics and single-cell proteomics.
  • MSBooster is fast, reliable, and fully integrated into the popular FragPipe platform, making it a versatile addition to current proteomics data analysis methods.]