Exploring massive, genome scale datasets with the genometricorr package

Handle URI:
http://hdl.handle.net/10754/325275
Title:
Exploring massive, genome scale datasets with the genometricorr package
Authors:
Favorov, Alexander; Mularoni, Loris; Cope, Leslie M.; Medvedeva, Yulia; Mironov, Andrey A.; Makeev, Vsevolod J.; Wheelan, Sarah J.
Abstract:
We have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of high-dimensional datasets, rather than providing immediate answers. The software enables several biologically motivated approaches to these data and here we describe the rationale and implementation for each approach. Our models and statistics are implemented in an R package that efficiently calculates the spatial correlation between two sets of genomic intervals (data and/or annotated features), for use as a metric of functional interaction. The software handles any type of pointwise or interval data and instead of running analyses with predefined metrics, it computes the significance and direction of several types of spatial association; this is intended to suggest potentially relevant relationships between the datasets. Availability and implementation: The package, GenometriCorr, can be freely downloaded at http://genometricorr.sourceforge.net/. Installation guidelines and examples are available from the sourceforge repository. The package is pending submission to Bioconductor. © 2012 Favorov et al.
KAUST Department:
Computational Bioscience Research Center (CBRC)
Citation:
Favorov A, Mularoni L, Cope LM, Medvedeva Y, Mironov AA, et al. (2012) Exploring Massive, Genome Scale Datasets with the GenometriCorr Package. PLoS Comput Biol 8: e1002529. doi:10.1371/journal.pcbi.1002529.
Publisher:
Public Library of Science
Journal:
PLoS Computational Biology
Issue Date:
31-May-2012
DOI:
10.1371/journal.pcbi.1002529
PubMed ID:
22693437
PubMed Central ID:
PMC3364938
Type:
Article
ISSN:
1553734X
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC)

Full metadata record

DC FieldValue Language
dc.contributor.authorFavorov, Alexanderen
dc.contributor.authorMularoni, Lorisen
dc.contributor.authorCope, Leslie M.en
dc.contributor.authorMedvedeva, Yuliaen
dc.contributor.authorMironov, Andrey A.en
dc.contributor.authorMakeev, Vsevolod J.en
dc.contributor.authorWheelan, Sarah J.en
dc.date.accessioned2014-08-27T09:44:19Z-
dc.date.available2014-08-27T09:44:19Z-
dc.date.issued2012-05-31en
dc.identifier.citationFavorov A, Mularoni L, Cope LM, Medvedeva Y, Mironov AA, et al. (2012) Exploring Massive, Genome Scale Datasets with the GenometriCorr Package. PLoS Comput Biol 8: e1002529. doi:10.1371/journal.pcbi.1002529.en
dc.identifier.issn1553734Xen
dc.identifier.pmid22693437en
dc.identifier.doi10.1371/journal.pcbi.1002529en
dc.identifier.urihttp://hdl.handle.net/10754/325275en
dc.description.abstractWe have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of high-dimensional datasets, rather than providing immediate answers. The software enables several biologically motivated approaches to these data and here we describe the rationale and implementation for each approach. Our models and statistics are implemented in an R package that efficiently calculates the spatial correlation between two sets of genomic intervals (data and/or annotated features), for use as a metric of functional interaction. The software handles any type of pointwise or interval data and instead of running analyses with predefined metrics, it computes the significance and direction of several types of spatial association; this is intended to suggest potentially relevant relationships between the datasets. Availability and implementation: The package, GenometriCorr, can be freely downloaded at http://genometricorr.sourceforge.net/. Installation guidelines and examples are available from the sourceforge repository. The package is pending submission to Bioconductor. © 2012 Favorov et al.en
dc.language.isoenen
dc.publisherPublic Library of Scienceen
dc.rightsFavorov et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.rightsArchived with thanks to PLoS Computational Biologyen
dc.subjectaccuracyen
dc.subjectcomputer interfaceen
dc.subjectcontrolled studyen
dc.subjectdata analysis softwareen
dc.subjectepigeneticsen
dc.subjectfungal genomeen
dc.subjectgene expression profilingen
dc.subjectgene insertionen
dc.subjectgene sequenceen
dc.subjectgenetic codeen
dc.subjectgenetic databaseen
dc.subjecthuman genomeen
dc.subjectpredictionen
dc.subjectprocess developmenten
dc.subjectpromoter regionen
dc.subjectreproducibilityen
dc.subjectretroposonen
dc.subjectRNA geneen
dc.subjectsensitivity and specificityen
dc.subjecttranscription initiation siteen
dc.subjectChromosomesen
dc.subjectDatabases, Geneticen
dc.subjectEpigenomicsen
dc.subjectGenetic Locien
dc.subjectGenomeen
dc.subjectGenomicsen
dc.subjectInformation Storage and Retrievalen
dc.subjectInterneten
dc.subjectModels, Geneticen
dc.subjectModels, Statisticalen
dc.subjectRNA, Transferen
dc.subjectSoftwareen
dc.subjectStatistics, Nonparametricen
dc.subjectUser-Computer Interfaceen
dc.titleExploring massive, genome scale datasets with the genometricorr packageen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journalPLoS Computational Biologyen
dc.identifier.pmcidPMC3364938en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Oncology, Division of Biostatistics and Bioinformatics, Johns Hopkins University School of Medicine, Baltimore, MD, United Statesen
dc.contributor.institutionVavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russian Federationen
dc.contributor.institutionResearch Institute of Genetics and Selection of Industrial Microorganisms, Moscow, Russian Federationen
dc.contributor.institutionDepartment of Bioengineering and Bioinformatics, Moscow State University, Moscow, Russian Federationen
dc.contributor.institutionInstitute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russian Federationen
dc.contributor.institutionInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spainen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorMedvedeva, Yuliaen

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