Skip Navigation


Bioinformatics Advance Access originally published online on October 16, 2008
Bioinformatics 2008 24(23):2796-2797; doi:10.1093/bioinformatics/btn513
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrowOA All Versions of this Article:
24/23/2796    most recent
btn513v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Google Scholar
Right arrow Articles by Prince, J. T.
Right arrow Articles by Marcotte, E. M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Prince, J. T.
Right arrow Articles by Marcotte, E. M.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

mspire: mass spectrometry proteomics in Ruby

John T. Prince 1,2 and Edward M. Marcotte 1,2,3,*

1Institute for Cellular and Molecular Biology, 2Center for Systems and Synthetic Biology and 3Department of Chemistry and Biochemistry, University of Texas, Austin, TX 78712, USA

*To whom correspondence should be addressed.


   Abstract

Summary: Mass spectrometry-based proteomics stands to gain from additional analysis of its data, but its large, complex datasets make demands on speed and memory usage requiring special consideration from scripting languages. The software library ‘mspire’—developed in the Ruby programming language—offers quick and memory-efficient readers for standard xml proteomics formats, converters for intermediate file types in typical proteomics spectral-identification work flows (including the Bioworks .srf format), and modules for the calculation of peptide false identification rates.

Availability: Freely available at http://mspire.rubyforge.org. Additional data models, usage information, and methods available at http://bioinformatics.icmb.utexas.edu/mspire

Contact: marcotte{at}icmb.utexas.edu

Associate Editor: John Quackenbush


Received on May 15, 2008; revised on September 26, 2008; accepted on October 1, 2008

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
BioinformaticsHome page
S. R. Ramakrishnan, C. Vogel, T. Kwon, L. O. Penalva, E. M. Marcotte, and D. P. Miranker
Mining gene functional networks to improve mass-spectrometry-based protein identification
Bioinformatics, November 15, 2009; 25(22): 2955 - 2961.
[Abstract] [Full Text] [PDF]



Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.