Chromosomal patterns of genomic signals represent molecular ngerprints that may reveal how the local structural organization of a genome impacts the functional control mechanisms. Thus, the integrative analysis of multiple sources of genomic data and information deepens the resolution and enhances the interpretation of stand-alone high-throughput data. In this note, we present PREDA (Position RElated Data Analysis), an R package for detecting regional variations in genomics data. PREDA identies relevant chromosomal patterns in high-throughput data using a smoothing approach that accounts for distance and density variability of genomics features. Custom-designed data structures allow efciently managing diverse signals in different genomes. A variety of smoothing functions and statistics empower exible and robust workows. The modularity of package design allows an easy deployment of custom analytical pipelines. Tabular and graphical representations facilitate downstream biological interpretation of results. © The Author 2011. Published by Oxford University Press. All rights reserved.

PREDA: An R-package to identify regional variations in genomic data

Solari A.;
2011-01-01

Abstract

Chromosomal patterns of genomic signals represent molecular ngerprints that may reveal how the local structural organization of a genome impacts the functional control mechanisms. Thus, the integrative analysis of multiple sources of genomic data and information deepens the resolution and enhances the interpretation of stand-alone high-throughput data. In this note, we present PREDA (Position RElated Data Analysis), an R package for detecting regional variations in genomics data. PREDA identies relevant chromosomal patterns in high-throughput data using a smoothing approach that accounts for distance and density variability of genomics features. Custom-designed data structures allow efciently managing diverse signals in different genomes. A variety of smoothing functions and statistics empower exible and robust workows. The modularity of package design allows an easy deployment of custom analytical pipelines. Tabular and graphical representations facilitate downstream biological interpretation of results. © The Author 2011. Published by Oxford University Press. All rights reserved.
2011
27
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5044552
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