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Mass Spectrometry Data Analysis in Proteomics

Overview of attention for book
Cover of 'Mass Spectrometry Data Analysis in Proteomics'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter -1597452749 Introduction to Proteomics
  3. Altmetric Badge
    Chapter -1597452713 Extracting Monoisotopic Single-Charge Peaks From Liquid Chromatography-Electrospray Ionization-Mass Spectrometry
  4. Altmetric Badge
    Chapter -1597452701 Calibration of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Peptide Mass Fingerprinting Spectra
  5. Altmetric Badge
    Chapter -1597452689 Protein Identification by Peptide Mass Fingerprinting
  6. Altmetric Badge
    Chapter -1597452673 Generating Unigene Collections of Expressed Sequence Tag Sequences for Use in Mass Spectrometry Identification
  7. Altmetric Badge
    Chapter -1597452663 Protein identification by tandem mass spectrometry and sequence database searching.
  8. Altmetric Badge
    Chapter -1597452629 Virtual Expert Mass Spectrometrist v3.0: An Integrated Tool for Proteome Analysis
  9. Altmetric Badge
    Chapter -1597452611 Quantitation With Virtual Expert Mass Spectrometrist
  10. Altmetric Badge
    Chapter -1597452597 Sequence Handling by Sequence Analysis Toolbox v1.0
  11. Altmetric Badge
    Chapter -1597452581 Interpretation of Collision-Induced Fragmentation Tandem Mass Spectra of Posttranslationally Modified Peptides
  12. Altmetric Badge
    Chapter -1597452555 Retention Time Prediction and Protein Identification
  13. Altmetric Badge
    Chapter -1597452541 Quantitative Proteomics by Stable Isotope Labeling and Mass Spectrometry
  14. Altmetric Badge
    Chapter -1597452531 Quantitative Proteomics for Two-Dimensional Gels Using Difference Gel Electrophoresis
  15. Altmetric Badge
    Chapter -1597452509 Proteomic Data Exchange and Storage: Using Proteios
  16. Altmetric Badge
    Chapter -1597452489 Proteomic Data Exchange and Storage: The Need for Common Standards and Public Repositories
  17. Altmetric Badge
    Chapter -1597452479 Organization of Proteomics Data With YassDB
  18. Altmetric Badge
    Chapter -1597452461 Analysis of Carbohydrates by Mass Spectrometry
  19. Altmetric Badge
    Chapter -1597452447 Useful Mass Spectrometry Programs Freely Available on the Internet
Attention for Chapter -1597452663: Protein identification by tandem mass spectrometry and sequence database searching.
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Mentioned by

wikipedia
1 Wikipedia page

Readers on

mendeley
243 Mendeley
citeulike
5 CiteULike
connotea
3 Connotea
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Chapter title
Protein identification by tandem mass spectrometry and sequence database searching.
Chapter number -1597452663
Book title
Mass Spectrometry Data Analysis in Proteomics
Published in
Methods in molecular biology, December 2006
DOI 10.1385/1-59745-275-0:87
Pubmed ID
Book ISBNs
978-1-58829-563-7, 978-1-59745-275-5
Authors

Alexey I. Nesvizhskii, Nesvizhskii, Alexey I.

Abstract

The shotgun proteomics strategy, based on digesting proteins into peptides and sequencing them using tandem mass spectrometry (MS/MS), has become widely adopted. The identification of peptides from acquired MS/MS spectra is most often performed using the database search approach. We provide a detailed description of the peptide identification process and review the most commonly used database search programs. The appropriate choice of the search parameters and the sequence database are important for successful application of this method, and we provide general guidelines for carrying out efficient analysis of MS/MS data. We also discuss various reasons why database search tools fail to assign the correct sequence to many MS/MS spectra, and draw attention to the problem of false-positive identifications that can significantly diminish the value of published data. To assist in the evaluation of peptide assignments to MS/MS spectra, we review the scoring schemes implemented in most frequently used database search tools. We also describe statistical approaches and computational tools for validating peptide assignments to MS/MS spectra, including the concept of expectation values, reversed database searching, and the empirical Bayesian analysis of PeptideProphet. Finally, the process of inferring the identities of the sample proteins given the list of peptide identifications is outlined, and the limitations of shotgun proteomics with regard to discrimination between protein isoforms are discussed.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 243 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Russia 3 1%
South Africa 2 <1%
Singapore 2 <1%
Austria 1 <1%
Sweden 1 <1%
France 1 <1%
Germany 1 <1%
Spain 1 <1%
United States 1 <1%
Other 0 0%
Unknown 230 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 72 30%
Researcher 33 14%
Student > Master 32 13%
Student > Bachelor 30 12%
Student > Doctoral Student 17 7%
Other 30 12%
Unknown 29 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 101 42%
Biochemistry, Genetics and Molecular Biology 41 17%
Chemistry 19 8%
Medicine and Dentistry 12 5%
Computer Science 8 3%
Other 25 10%
Unknown 37 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 October 2007.
All research outputs
#7,454,066
of 22,788,370 outputs
Outputs from Methods in molecular biology
#2,318
of 13,096 outputs
Outputs of similar age
#42,035
of 156,701 outputs
Outputs of similar age from Methods in molecular biology
#20
of 47 outputs
Altmetric has tracked 22,788,370 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,096 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 76% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 156,701 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 47 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.