Skip Navigation


Bioinformatics Advance Access originally published online on August 10, 2009
Bioinformatics 2009 25(19):2609-2610; doi:10.1093/bioinformatics/btp477
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrowOA All Versions of this Article:
25/19/2609    most recent
btp477v1
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 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 Wegrzyn, J. L.
Right arrow Articles by Neale, D. B.
PubMed
Right arrow PubMed Citation
Right arrow Articles by Wegrzyn, J. L.
Right arrow Articles by Neale, D. B.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 2009 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.

PineSAP—sequence alignment and SNP identification pipeline

Jill L. Wegrzyn 1,*, Jennifer M. Lee 2, John Liechty 1 and David B. Neale 1

1Department of Plant Sciences and 2Department of Evolution and Ecology, Address One Shields Ave. University of California, Davis, CA 95616, USA

*To whom correspondence should be addressed.


  Abstract

Summary: The Pine Alignment and SNP Identification Pipeline (PineSAP) provides a high-throughput solution to single nucleotide polymorphism (SNP) prediction using multiple sequence alignments from re-sequencing data. This pipeline integrates a hybrid of customized scripting, existing utilities and machine learning in order to increase the speed and accuracy of SNP calls. The implementation of this pipeline results in significantly improved multiple sequence alignments and SNP identifications when compared with existing solutions. The use of machine learning in the SNP identifications extends the pipeline's application to any eukaryotic species where full genome sequence information is unavailable.

Availability: All code used for this pipeline is freely available at the Dendrome project website (http://dendrome.ucdavis.edu/adept2/resequencing.html)

Contact: jlwegrzyn{at}ucdavis.edu

Associate Editor: John Quackenbush

Received on May 22, 2009; revised on July 31, 2009; accepted on August 2, 2009

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




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.