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      RCircos: an R package for Circos 2D track plots

      product-review
      1 , 1 , 1 ,
      BMC Bioinformatics
      BioMed Central
      Software, RCircos, R package, Circos, Genomic data visualization

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          Abstract

          Background

          Circos is a Perl language based software package for visualizing similarities and differences of genome structure and positional relationships between genomic intervals. Running Circos requires extra data processing procedures to prepare plot data files and configure files from datasets, which limits its capability of integrating directly with other software tools such as R. Recently published R Bioconductor package ggbio provides a function to display genomic data in circular layout based on multiple other packages, which increases its complexity of usage and decreased the flexibility in integrating with other R pipelines.

          Results

          We implemented an R package, RCircos, using only R packages that come with R base installation. The package supports Circos 2D data track plots such as scatter, line, histogram, heatmap, tile, connectors, links, and text labels. Each plot is implemented with a specific function and input data for all functions are data frames which can be objects read from text files or generated with other R pipelines.

          Conclusion

          RCircos package provides a simple and flexible way to make Circos 2D track plots with R and could be easily integrated into other R data processing and graphic manipulation pipelines for presenting large-scale multi-sample genomic research data. It can also serve as a base tool to generate complex Circos images.

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          Most cited references5

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          ggbio: an R package for extending the grammar of graphics for genomic data

          We introduce ggbio, a new methodology to visualize and explore genomics annotations and high-throughput data. The plots provide detailed views of genomic regions, summary views of sequence alignments and splicing patterns, and genome-wide overviews with karyogram, circular and grand linear layouts. The methods leverage the statistical functionality available in R, the grammar of graphics and the data handling capabilities of the Bioconductor project. The plots are specified within a modular framework that enables users to construct plots in a systematic way, and are generated directly from Bioconductor data structures. The ggbio R package is available at http://www.bioconductor.org/packages/2.11/bioc/html/ggbio.html.
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            Visualizing genomes: techniques and challenges.

            As our ability to generate sequencing data continues to increase, data analysis is replacing data generation as the rate-limiting step in genomics studies. Here we provide a guide to genomic data visualization tools that facilitate analysis tasks by enabling researchers to explore, interpret and manipulate their data, and in some cases perform on-the-fly computations. We will discuss graphical methods designed for the analysis of de novo sequencing assemblies and read alignments, genome browsing, and comparative genomics, highlighting the strengths and limitations of these approaches and the challenges ahead.
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              Is Open Access

              Visualizing multidimensional cancer genomics data

              Cancer genomics projects employ high-throughput technologies to identify the complete catalog of somatic alterations that characterize the genome, transcriptome and epigenome of cohorts of tumor samples. Examples include projects carried out by the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). A crucial step in the extraction of knowledge from the data is the exploration by experts of the different alterations, as well as the multiple relationships between them. To that end, the use of intuitive visualization tools that can integrate different types of alterations with clinical data is essential to the field of cancer genomics. Here, we review effective and common visualization techniques for exploring oncogenomics data and discuss a selection of tools that allow researchers to effectively visualize multidimensional oncogenomics datasets. The review covers visualization methods employed by tools such as Circos, Gitools, the Integrative Genomics Viewer, Cytoscape, Savant Genome Browser, StratomeX and platforms such as cBio Cancer Genomics Portal, IntOGen, the UCSC Cancer Genomics Browser, the Regulome Explorer and the Cancer Genome Workbench.
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                Author and article information

                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2013
                10 August 2013
                : 14
                : 244
                Affiliations
                [1 ]Genetics Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 37, Room 6138, 37 Convent Drive, Bethesda, MD 20892-4265, USA
                Article
                1471-2105-14-244
                10.1186/1471-2105-14-244
                3765848
                23937229
                c2bdf4aa-d618-49a4-a71e-8d757e9dd2e5
                Copyright ©2013 Zhang et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 March 2013
                : 7 August 2013
                Categories
                Software

                Bioinformatics & Computational biology
                software,rcircos,r package,circos,genomic data visualization

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