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Exploring Massive, Genome Scale Datasets with the GenometriCorr Package

Author

Listed:
  • Alexander Favorov
  • Loris Mularoni
  • Leslie M Cope
  • Yulia Medvedeva
  • Andrey A Mironov
  • Vsevolod J Makeev
  • Sarah J Wheelan

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.

Suggested Citation

  • Alexander Favorov & Loris Mularoni & Leslie M Cope & Yulia Medvedeva & Andrey A Mironov & Vsevolod J Makeev & Sarah J Wheelan, 2012. "Exploring Massive, Genome Scale Datasets with the GenometriCorr Package," PLOS Computational Biology, Public Library of Science, vol. 8(5), pages 1-12, May.
    • Handle: RePEc:plo:pcbi00:1002529
    DOI: 10.1371/journal.pcbi.1002529
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    1. Adel Al Jord & Gaëlle Letort & Soline Chanet & Feng-Ching Tsai & Christophe Antoniewski & Adrien Eichmuller & Christelle Da Silva & Jean-René Huynh & Nir S. Gov & Raphaël Voituriez & Marie-Émilie Terr, 2022. "Cytoplasmic forces functionally reorganize nuclear condensates in oocytes," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    2. Pedro Madrigal & Siwei Deng & Yuliang Feng & Stefania Militi & Kim Jee Goh & Reshma Nibhani & Rodrigo Grandy & Anna Osnato & Daniel Ortmann & Stephanie Brown & Siim Pauklin, 2023. "Epigenetic and transcriptional regulations prime cell fate before division during human pluripotent stem cell differentiation," Nature Communications, Nature, vol. 14(1), pages 1-23, December.
    3. Darshan Bryner & Stephen Criscione & Andrew Leith & Quyen Huynh & Fred Huffer & Nicola Neretti, 2017. "GINOM: A statistical framework for assessing interval overlap of multiple genomic features," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-16, June.
    4. Tomas Zelenka & Antonios Klonizakis & Despina Tsoukatou & Dionysios-Alexandros Papamatheakis & Sören Franzenburg & Petros Tzerpos & Ioannis-Rafail Tzonevrakis & George Papadogkonas & Manouela Kapsetak, 2022. "The 3D enhancer network of the developing T cell genome is shaped by SATB1," Nature Communications, Nature, vol. 13(1), pages 1-22, December.
    5. Alan Y. Du & Jason D. Chobirko & Xiaoyu Zhuo & Cédric Feschotte & Ting Wang, 2024. "Regulatory transposable elements in the encyclopedia of DNA elements," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    6. Liana Goehring & Sarah Keegan & Sudipta Lahiri & Wenxin Xia & Michael Kong & Judit Jimenez-Sainz & Dipika Gupta & Ronny Drapkin & Ryan B. Jensen & Duncan J. Smith & Eli Rothenberg & David Fenyö & Tony, 2024. "Dormant origin firing promotes head-on transcription-replication conflicts at transcription termination sites in response to BRCA2 deficiency," Nature Communications, Nature, vol. 15(1), pages 1-18, December.

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