| Title |
From Raw Data to Biological Discoveries: A Computational Analysis Pipeline for Mass Spectrometry-Based Proteomics
|
|---|---|
| Published in |
Journal of the American Society for Mass Spectrometry, May 2015
|
| DOI | 10.1007/s13361-015-1161-7 |
| Pubmed ID | |
| Authors |
Mathieu Lavallée-Adam, Sung Kyu Robin Park, Salvador Martínez-Bartolomé, Lin He, John R. Yates |
| Abstract |
In the last two decades, computational tools for mass spectrometry-based proteomics data analysis have evolved from a few stand-alone software solutions serving specific goals, such as the identification of amino acid sequences based on mass spectrometry spectra, to large-scale complex pipelines integrating multiple computer programs to solve a collection of problems. This software evolution has been mostly driven by the appearance of novel technologies that allowed the community to tackle complex biological problems, such as the identification of proteins that are differentially expressed in two samples under different conditions. The achievement of such objectives requires a large suite of programs to analyze the intricate mass spectrometry data. Our laboratory addresses complex proteomics questions by producing and using algorithms and software packages. Our current computational pipeline includes, among other things, tools for mass spectrometry raw data processing, peptide and protein identification and quantification, post-translational modification analysis, and protein functional enrichment analysis. In this paper, we describe a suite of software packages we have developed to process mass spectrometry-based proteomics data and we highlight some of the new features of previously published programs as well as tools currently under development. Graphical Abstract ᅟ. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 3% |
| Unknown | 76 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 19 | 24% |
| Researcher | 18 | 23% |
| Student > Master | 8 | 10% |
| Other | 7 | 9% |
| Student > Postgraduate | 4 | 5% |
| Other | 10 | 13% |
| Unknown | 12 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 21 | 27% |
| Biochemistry, Genetics and Molecular Biology | 21 | 27% |
| Chemistry | 10 | 13% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 4% |
| Computer Science | 3 | 4% |
| Other | 6 | 8% |
| Unknown | 14 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 10 May 2016.
All research outputs
#19,944,994
of 25,374,647 outputs
Outputs from Journal of the American Society for Mass Spectrometry
#2,943
of 3,833 outputs
Outputs of similar age
#193,292
of 281,630 outputs
Outputs of similar age from Journal of the American Society for Mass Spectrometry
#26
of 39 outputs
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