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Exploring massive, genome scale datasets with the GenometriCorr package.

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.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.

SUBMITTER: Favorov A

PROVIDER: S-EPMC3364938 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature

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Exploring massive, genome scale datasets with the GenometriCorr package.

Favorov Alexander A Mularoni Loris L Cope Leslie M LM Medvedeva Yulia Y Mironov Andrey A AA Makeev Vsevolod J VJ Wheelan Sarah J SJ

PLoS computational biology 20120531 5

<h4>Unlabelled</h4>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 efficient ...[more]

PMID: 22693437

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Project description:BackgroundThe advent of population-scale genome projects has revolutionized our biological understanding of parasitic protozoa. However, while hundreds to thousands of nuclear genomes of parasitic protozoa have been generated and analyzed, information about the diversity, structure and evolution of their mitochondrial genomes remains fragmentary, mainly because of their extraordinary complexity. Indeed, unicellular flagellates of the order Kinetoplastida contain structurally the most complex mitochondrial genome of all eukaryotes, organized as a giant network of homogeneous maxicircles and heterogeneous minicircles. We recently developed KOMICS, an analysis toolkit that automates the assembly and circularization of the mitochondrial genomes of Kinetoplastid parasites. While this tool overcomes the limitation of extracting mitochondrial assemblies from Next-Generation Sequencing datasets, interpreting and visualizing the genetic (dis)similarity within and between samples remains a time-consuming process.ResultsHere, we present a new analysis toolkit-rKOMICS-to streamline the analyses of minicircle sequence diversity in population-scale genome projects. rKOMICS is a user-friendly R package that has simple installation requirements and that is applicable to all 27 trypanosomatid genera. Once minicircle sequence alignments are generated, rKOMICS allows to examine, summarize and visualize minicircle sequence diversity within and between samples through the analyses of minicircle sequence clusters. We showcase the functionalities of the (r)KOMICS tool suite using a whole-genome sequencing dataset from a recently published study on the history of diversification of the Leishmania braziliensis species complex in Peru. Analyses of population diversity and structure highlighted differences in minicircle sequence richness and composition between Leishmania subspecies, and between subpopulations within subspecies.ConclusionThe rKOMICS package establishes a critical framework to manipulate, explore and extract biologically relevant information from mitochondrial minicircle assemblies in tens to hundreds of samples simultaneously and efficiently. This should facilitate research that aims to develop new molecular markers for identifying species-specific minicircles, or to study the ancestry of parasites for complementary insights into their evolutionary history.

Dataset Information

Exploring massive, genome scale datasets with the GenometriCorr package.

Publications

Exploring massive, genome scale datasets with the GenometriCorr package.

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