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Related Concept Videos

Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

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...
Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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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Related Experiment Video

Updated: May 27, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Published on: October 11, 2016

MR-Tandem: parallel X!Tandem using Hadoop MapReduce on Amazon Web Services.

Brian Pratt1, J Jeffry Howbert, Natalie I Tasman

  • 1Insilicos LLC, Seattle WA, USA.

Bioinformatics (Oxford, England)
|November 11, 2011
PubMed
Summary

MR-Tandem enhances the X!Tandem peptide search engine for parallel processing using Hadoop MapReduce. This scalable solution, especially with Amazon Web Services, enables large-scale proteomic data analysis for researchers.

Related Experiment Videos

Last Updated: May 27, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Published on: October 11, 2016

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Proteomics

Background:

  • Large-scale proteomic data analysis requires efficient computational tools.
  • Existing peptide search engines like X!Tandem may face limitations with massive datasets.
  • Parallel processing frameworks offer a solution for accelerating complex bioinformatics tasks.

Purpose of the Study:

  • To adapt the X!Tandem peptide search engine for parallel execution on Hadoop clusters.
  • To enable reliable and scalable analysis of large-scale proteomic datasets.
  • To provide a cost-effective solution for researchers with limited local compute resources.

Main Methods:

  • MR-Tandem modifies the X!Tandem C++ executable to function as a Hadoop Streaming mapper and reducer.
  • A Python script orchestrates the execution on Hadoop clusters, including Amazon Web Services (AWS) Elastic Map Reduce (EMR).
  • The software is designed for easy integration into existing X!Tandem workflows with minimal modifications.

Main Results:

  • MR-Tandem successfully enables parallel execution of X!Tandem searches on Hadoop clusters.
  • The system supports reliable processing of large-scale peptide identification tasks.
  • Integration with AWS allows for inexpensive, on-demand creation of Hadoop clusters for flexible compute power.

Conclusions:

  • MR-Tandem provides a scalable and efficient solution for large-scale proteomic data analysis.
  • The tool seamlessly integrates with existing X!Tandem infrastructure, lowering adoption barriers.
  • Researchers can leverage cloud computing for previously infeasible analysis volumes.