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PineSAP—sequence alignment and SNP identification pipeline

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 m...

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Detalles Bibliográficos
Autores principales: Wegrzyn, Jill L., Lee, Jennifer M., Liechty, John, Neale, David B.
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2752621/
https://www.ncbi.nlm.nih.gov/pubmed/19667082
http://dx.doi.org/10.1093/bioinformatics/btp477
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author Wegrzyn, Jill L.
Lee, Jennifer M.
Liechty, John
Neale, David B.
author_facet Wegrzyn, Jill L.
Lee, Jennifer M.
Liechty, John
Neale, David B.
author_sort Wegrzyn, Jill L.
collection PubMed
description 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@ucdavis.edu
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spelling pubmed-27526212009-10-01 PineSAP—sequence alignment and SNP identification pipeline Wegrzyn, Jill L. Lee, Jennifer M. Liechty, John Neale, David B. Bioinformatics Applications Note 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@ucdavis.edu Oxford University Press 2009-10-01 2009-08-10 /pmc/articles/PMC2752621/ /pubmed/19667082 http://dx.doi.org/10.1093/bioinformatics/btp477 Text en http://creativecommons.org/licenses/by-nc/2.0/uk/ 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.
spellingShingle Applications Note
Wegrzyn, Jill L.
Lee, Jennifer M.
Liechty, John
Neale, David B.
PineSAP—sequence alignment and SNP identification pipeline
title PineSAP—sequence alignment and SNP identification pipeline
title_full PineSAP—sequence alignment and SNP identification pipeline
title_fullStr PineSAP—sequence alignment and SNP identification pipeline
title_full_unstemmed PineSAP—sequence alignment and SNP identification pipeline
title_short PineSAP—sequence alignment and SNP identification pipeline
title_sort pinesap—sequence alignment and snp identification pipeline
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2752621/
https://www.ncbi.nlm.nih.gov/pubmed/19667082
http://dx.doi.org/10.1093/bioinformatics/btp477
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