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      Software for Computing and Annotating Genomic Ranges


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          We describe Bioconductor infrastructure for representing and computing on annotated genomic ranges and integrating genomic data with the statistical computing features of R and its extensions. At the core of the infrastructure are three packages: IRanges, GenomicRanges, and GenomicFeatures. These packages provide scalable data structures for representing annotated ranges on the genome, with special support for transcript structures, read alignments and coverage vectors. Computational facilities include efficient algorithms for overlap and nearest neighbor detection, coverage calculation and other range operations. This infrastructure directly supports more than 80 other Bioconductor packages, including those for sequence analysis, differential expression analysis and visualization.

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          Maintaining knowledge about temporal intervals

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            ShortRead: a bioconductor package for input, quality assessment and exploration of high-throughput sequence data

            Summary: ShortRead is a package for input, quality assessment, manipulation and output of high-throughput sequencing data. ShortRead is provided in the R and Bioconductor environments, allowing ready access to additional facilities for advanced statistical analysis, data transformation, visualization and integration with diverse genomic resources. Availability and Implementation: This package is implemented in R and available at the Bioconductor web site; the package contains a ‘vignette’ outlining typical work flows. Contact: mtmorgan@fhcrc.org
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              Heritable individual-specific and allele-specific chromatin signatures in humans.

              The extent to which variation in chromatin structure and transcription factor binding may influence gene expression, and thus underlie or contribute to variation in phenotype, is unknown. To address this question, we cataloged both individual-to-individual variation and differences between homologous chromosomes within the same individual (allele-specific variation) in chromatin structure and transcription factor binding in lymphoblastoid cells derived from individuals of geographically diverse ancestry. Ten percent of active chromatin sites were individual-specific; a similar proportion were allele-specific. Both individual-specific and allele-specific sites were commonly transmitted from parent to child, which suggests that they are heritable features of the human genome. Our study shows that heritable chromatin status and transcription factor binding differ as a result of genetic variation and may underlie phenotypic variation in humans.

                Author and article information

                Role: Editor
                PLoS Comput Biol
                PLoS Comput. Biol
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                August 2013
                August 2013
                8 August 2013
                : 9
                : 8
                [1 ]Bioinformatics and Computational Biology, Genentech, Inc., South San Francisco, California, United States of America
                [2 ]European Molecular Biology Laboratory Genome Biology Unit, Heidelberg, Germany
                [3 ]The European Bioinformatics Institute, Cambridge, United Kingdom
                [4 ]Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
                [5 ]Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
                University of California, San Diego, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Analyzed the data: ML VJC. Contributed reagents/materials/analysis tools: ML HP PA MC MTM. Wrote the paper: ML WH RG MTM VJC.


                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.

                Page count
                Pages: 10
                This work was funded by the National Institutes of Health, National Human Genome Research Group through grants P41 HG004059 and U41 HG004059 and (for VJC) by National Heart, Lung and Blood Institute grants R01 HL086601, R01 HL093076 and R01 HL094635. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Research Article
                Computational Biology
                Functional Genomics
                Genome Analysis Tools
                Genome Expression Analysis
                Genome Sequencing
                Sequence Analysis
                Computer Science

                Quantitative & Systems biology
                Quantitative & Systems biology


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