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Related Concept Videos

Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

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Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...
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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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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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Tempest: Accelerated MS/MS Database Search Software for Heterogeneous Computing Platforms.

Mark E Adamo1, Scott A Gerber1,2,3

  • 1Norris Cotton Cancer Center, Geisel School at Dartmouth, Lebanon, New Hampshire.

Current Protocols in Bioinformatics
|September 8, 2016
PubMed
Summary
This summary is machine-generated.

The Tempest software now uses OpenCL for broader parallel processing across CPUs and GPUs, accelerating peptide identification in mass spectrometry. This enhances computational efficiency for analyzing large-scale proteomics datasets.

Keywords:
GPGPUOpenCLdatabase searchmass spectrometryneutral lossparallel computingpeptide identificationproteomics

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Area of Science:

  • Computational biology
  • Proteomics
  • Bioinformatics

Background:

  • Mass spectrometry/mass spectrometry (MS/MS) database search algorithms identify peptides by matching theoretical fragmentation patterns to experimental spectra.
  • The original Tempest software utilized a CPU-GPU model for parallel processing.

Purpose of the Study:

  • To expand the Tempest software's parallel processing capabilities.
  • To enable seamless integration with diverse hardware architectures including multicore CPUs, GPUs, and coprocessors.

Main Methods:

  • Incorporation of OpenCL for cross-platform parallelization.
  • Development of three protocols for configuring and running Tempest searches.
  • Leveraging Tempest's feature set for optimized results.

Main Results:

  • Achieved seamless parallelization across multicore CPUs, GPUs, integrated graphics chips, and general-purpose coprocessors.
  • Provided protocols for efficient configuration and execution of Tempest searches.
  • Demonstrated enhanced performance for MS/MS database searching.

Conclusions:

  • The expanded Tempest software offers versatile and efficient parallel processing for peptide identification.
  • OpenCL integration significantly broadens hardware compatibility and computational power.
  • Optimized use of Tempest features leads to improved results in proteomics data analysis.