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Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and signal-to-noise ratio for the analyte. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.
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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.
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Mass spectrometry is an analytical technique used to determine the molecular mass and molecular formula of a compound. The basic principle of mass spectrometry is to generate ions from the analyte molecule and measure these ion abundances against their molecular mass.  One common type of ionization, known as electrospray ionization or EI, bombards the analyte molecules in the gas phase with high-energy electron beams. The electron beams displace an electron from the molecule and leave...
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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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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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High Performance Computing Framework for Tera-Scale Database Search of Mass Spectrometry Data.

Muhammad Haseeb1, Fahad Saeed1,2,3

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

Nature Computational Science
|November 1, 2021
PubMed
Summary
This summary is machine-generated.

HiCOPS accelerates database peptide search algorithms using high-performance computing (HPC), improving speed by over 10-fold. This framework enhances computational efficiency for systems biology studies on supercomputers.

Keywords:
bulk synchronous parallelhigh performance computingmass spectrometrypeptide identificationproteomics

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Mass Spectrometry Data Analysis

Background:

  • Database peptide search algorithms are crucial for mass spectrometry (MS) data analysis.
  • Existing high-performance computing (HPC) algorithms suffer from inefficient parallel designs and high overheads, limiting their scalability for systems biology.
  • There is a need for optimized computational frameworks to enhance the efficiency of peptide identification.

Purpose of the Study:

  • To develop an efficient HPC framework, named HiCOPS, for accelerating database peptide search algorithms.
  • To improve the performance and resource utilization of peptide search on distributed-memory supercomputers.
  • To provide a generalizable framework applicable to various search algorithms.

Main Methods:

  • Development of the HiCOPS HPC framework for distributed-memory supercomputers.
  • Implementation of novel parallel design techniques and optimizations.
  • Formulation of a mathematical model for performance analysis and optimization.
  • Evaluation of performance metrics including speed, strong-scale efficiency, hardware utilization, load balance, and communication/IO overheads.

Main Results:

  • HiCOPS achieved an average speed improvement of over 10-fold compared to existing HPC software.
  • Demonstrated superior parallel performance and near-optimal results for key performance metrics.
  • Showcased high hardware utilization and efficient load balancing.
  • Reported significantly reduced inter-process communication and I/O overheads.

Conclusions:

  • HiCOPS offers a significant advancement in the computational efficiency of database peptide search.
  • The framework's algorithm-independent design allows for broad applicability to current and future bioinformatics tools.
  • HiCOPS enables larger and more complex systems biology studies through accelerated MS data analysis.