SigWinR; the SigWin-detector updated and ported to R

<p>Abstract</p> <p>Background</p> <p>Our SigWin-detector discovers significantly enriched windows of (genomic) elements in any sequence of values (genes or other genomic elements in a DNA sequence) in a fast and reproducible way. However, since it is grid based, only (l...

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Main Authors: Breit Timo M, Bruning Oskar, Inda Márcia A, Rauwerda Han, de Leeuw Wim C
Format: Article
Language:English
Published: BMC 2009-10-01
Series:BMC Research Notes
Online Access:http://www.biomedcentral.com/1756-0500/2/205
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spelling doaj-60af0a40e035473ca93f9ebac9b3fb1a2020-11-25T02:54:18ZengBMCBMC Research Notes1756-05002009-10-012120510.1186/1756-0500-2-205SigWinR; the SigWin-detector updated and ported to RBreit Timo MBruning OskarInda Márcia ARauwerda Hande Leeuw Wim C<p>Abstract</p> <p>Background</p> <p>Our SigWin-detector discovers significantly enriched windows of (genomic) elements in any sequence of values (genes or other genomic elements in a DNA sequence) in a fast and reproducible way. However, since it is grid based, only (life) scientists with access to the grid can use this tool. Therefore and on request, we have developed the SigWinR package which makes the SigWin-detector available to a much wider audience. At the same time, we have introduced several improvements to its algorithm as well as its functionality, based on the feedback of SigWin-detector end users.</p> <p>Findings</p> <p>To allow usage of the SigWin-detector on a desktop computer, we have rewritten it as a package for R: SigWinR. R is a free and widely used multi platform software environment for statistical computing and graphics. The package can be installed and used on all platforms for which R is available. The improvements involve: a visualization of the input-sequence values supporting the interpretation of Ridgeograms; a visualization allowing for an easy interpretation of enriched or depleted regions in the sequence using windows of pre-defined size; an option that allows the analysis of circular sequences, which results in rectangular Ridgeograms; an application to identify regions of co-altered gene expression (ROCAGEs) with a real-life biological use-case; adaptation of the algorithm to allow analysis of non-regularly sampled data using a constant window size in physical space without resampling the data. To achieve this, support for analysis of windows with an even number of elements was added.</p> <p>Conclusion</p> <p>By porting the SigWin-detector as an R package, SigWinR, improving its algorithm and functionality combined with adequate performance, we have made SigWin-detector more useful as well as more easily accessible to scientists without a grid infrastructure.</p> http://www.biomedcentral.com/1756-0500/2/205
collection DOAJ
language English
format Article
sources DOAJ
author Breit Timo M
Bruning Oskar
Inda Márcia A
Rauwerda Han
de Leeuw Wim C
spellingShingle Breit Timo M
Bruning Oskar
Inda Márcia A
Rauwerda Han
de Leeuw Wim C
SigWinR; the SigWin-detector updated and ported to R
BMC Research Notes
author_facet Breit Timo M
Bruning Oskar
Inda Márcia A
Rauwerda Han
de Leeuw Wim C
author_sort Breit Timo M
title SigWinR; the SigWin-detector updated and ported to R
title_short SigWinR; the SigWin-detector updated and ported to R
title_full SigWinR; the SigWin-detector updated and ported to R
title_fullStr SigWinR; the SigWin-detector updated and ported to R
title_full_unstemmed SigWinR; the SigWin-detector updated and ported to R
title_sort sigwinr; the sigwin-detector updated and ported to r
publisher BMC
series BMC Research Notes
issn 1756-0500
publishDate 2009-10-01
description <p>Abstract</p> <p>Background</p> <p>Our SigWin-detector discovers significantly enriched windows of (genomic) elements in any sequence of values (genes or other genomic elements in a DNA sequence) in a fast and reproducible way. However, since it is grid based, only (life) scientists with access to the grid can use this tool. Therefore and on request, we have developed the SigWinR package which makes the SigWin-detector available to a much wider audience. At the same time, we have introduced several improvements to its algorithm as well as its functionality, based on the feedback of SigWin-detector end users.</p> <p>Findings</p> <p>To allow usage of the SigWin-detector on a desktop computer, we have rewritten it as a package for R: SigWinR. R is a free and widely used multi platform software environment for statistical computing and graphics. The package can be installed and used on all platforms for which R is available. The improvements involve: a visualization of the input-sequence values supporting the interpretation of Ridgeograms; a visualization allowing for an easy interpretation of enriched or depleted regions in the sequence using windows of pre-defined size; an option that allows the analysis of circular sequences, which results in rectangular Ridgeograms; an application to identify regions of co-altered gene expression (ROCAGEs) with a real-life biological use-case; adaptation of the algorithm to allow analysis of non-regularly sampled data using a constant window size in physical space without resampling the data. To achieve this, support for analysis of windows with an even number of elements was added.</p> <p>Conclusion</p> <p>By porting the SigWin-detector as an R package, SigWinR, improving its algorithm and functionality combined with adequate performance, we have made SigWin-detector more useful as well as more easily accessible to scientists without a grid infrastructure.</p>
url http://www.biomedcentral.com/1756-0500/2/205
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