Difference between revisions of "NucleR"
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{{Bioinformatics application | {{Bioinformatics application | ||
|sw summary=nucleR is a R/Bioconductor package for working with tiling arrays and next generation sequencing. It uses a novel aproach in this field which comprises a deep profile cleaning using Fourier Transform and peak scoring for a quick and flexible nucleosome calling | |sw summary=nucleR is a R/Bioconductor package for working with tiling arrays and next generation sequencing. It uses a novel aproach in this field which comprises a deep profile cleaning using Fourier Transform and peak scoring for a quick and flexible nucleosome calling | ||
− | |bio domain=ChIP- | + | |bio domain=ChIP-on-chip, ChIP-seq, DNA packaging, Epigenomics |
− | |bio method=Annotation, Peak calling | + | |bio method=Annotation, Peak calling |
− | |bio tech= | + | |bio tech=Illumina, 454, SOLiD, Ion Torrent, Illumina HiSeq, |
|created by=Flores O, Orozco M | |created by=Flores O, Orozco M | ||
|created at=Molecular Modelling & Bioinformatics Unit. Joint programme IRB-BSC & University of Barcelona | |created at=Molecular Modelling & Bioinformatics Unit. Joint programme IRB-BSC & University of Barcelona | ||
|maintained=Yes | |maintained=Yes | ||
− | |input format= | + | |input format=ShortRead, BioConductor, |
− | |output format=WIG | + | |output format=WIG, BED,BioConductor, |
− | |sw feature=Multicore, Integrated solution, | + | |sw feature=Multicore, Integrated solution, |
− | |language= | + | |language=R, |
− | |licence= | + | |licence=LGPLv3 |
|os=Cross-Platform | |os=Cross-Platform | ||
}} | }} | ||
== Description == | == Description == | ||
We present a new tool, nucleR, integrated in the open source, multiplatform R/Bioconductor framework. The approach is based on a fast, non-parametric detection of all nucleosome dyads and scoring of the calls. A good performance is achieved by filtering the noise using Fast Fourier Transform (FFT). The user has full freedom to export, select, merge or process suggested nucleosome calls in any desired way, making the method completely flexible. Algorithms presented here are suitable for most Tiling Arrays and single or paired ended Next Generation Sequencing platforms | We present a new tool, nucleR, integrated in the open source, multiplatform R/Bioconductor framework. The approach is based on a fast, non-parametric detection of all nucleosome dyads and scoring of the calls. A good performance is achieved by filtering the noise using Fast Fourier Transform (FFT). The user has full freedom to export, select, merge or process suggested nucleosome calls in any desired way, making the method completely flexible. Algorithms presented here are suitable for most Tiling Arrays and single or paired ended Next Generation Sequencing platforms | ||
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{{Links}} | {{Links}} | ||
{{References}} | {{References}} | ||
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{{Link box}} | {{Link box}} |
Latest revision as of 10:23, 11 January 2016
Application data |
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Created by | Flores O, Orozco M |
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Biological application domain(s) | ChIP-on-chip, ChIP-seq, DNA packaging, Epigenomics |
Principal bioinformatics method(s) | Annotation, Peak calling |
Technology | Illumina, 454, SOLiD, Ion Torrent, Illumina HiSeq |
Created at | Molecular Modelling & Bioinformatics Unit. Joint programme IRB-BSC & University of Barcelona |
Maintained? | Yes |
Input format(s) | ShortRead, BioConductor |
Output format(s) | WIG, BED, BioConductor |
Software features | Multicore, Integrated solution |
Programming language(s) | R |
Licence | LGPLv3 |
Operating system(s) | Cross-Platform |
Summary: nucleR is a R/Bioconductor package for working with tiling arrays and next generation sequencing. It uses a novel aproach in this field which comprises a deep profile cleaning using Fourier Transform and peak scoring for a quick and flexible nucleosome calling
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Description
We present a new tool, nucleR, integrated in the open source, multiplatform R/Bioconductor framework. The approach is based on a fast, non-parametric detection of all nucleosome dyads and scoring of the calls. A good performance is achieved by filtering the noise using Fast Fourier Transform (FFT). The user has full freedom to export, select, merge or process suggested nucleosome calls in any desired way, making the method completely flexible. Algorithms presented here are suitable for most Tiling Arrays and single or paired ended Next Generation Sequencing platforms
Links
References
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