<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/38819?offset=40 https://bioinformaticsonline.com/bookmarks/view/29487/shinyheatmap Fri, 21 Oct 2016 05:12:11 -0500 https://bioinformaticsonline.com/bookmarks/view/29487/shinyheatmap <![CDATA[Shinyheatmap]]> Background: Transcriptomics, metabolomics, metagenomics, and other various next-generation sequencing (-omics) fields are known for their production of large datasets. Visualizing such big data has posed technical challenges in biology, both in terms of available computational resources as well as programming acumen. Since heatmaps are used to depict high-dimensional numerical data as a colored grid of cells, efficiency and speed have often proven to be critical considerations in the process of successfully converting data into graphics. For example, rendering interactive heatmaps from large input datasets (e.g., 100k+ rows) has been computationally infeasible on both desktop computers and web browsers. In addition to memory requirements, programming skills and knowledge have frequently been barriers-to-entry for creating highly customizable heatmaps. Results: We propose shinyheatmap: an advanced user-friendly heatmap software suite capable of efficiently creating highly customizable static and interactive biological heatmaps in a web browser. shinyheatmap is a low memory footprint program, making it particularly well-suited for the interactive visualization of extremely large datasets that cannot typically be computed in-memory due to size restrictions. Conclusions: shinyheatmap is hosted online as a freely available web server with an intuitive graphical user interface: http://shinyheatmap.com. The methods are implemented in R, and are available as part of the shinyheatmap project at: https://github.com/Bohdan-Khomtchouk/shinyheatmap.

More at http://biorxiv.org/content/early/2016/09/21/076463 

Address of the bookmark: http://shinyheatmap.com/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/30897/finestructure-v2-globetrotter Mon, 13 Feb 2017 08:40:23 -0600 https://bioinformaticsonline.com/bookmarks/view/30897/finestructure-v2-globetrotter <![CDATA[fineSTRUCTURE v2 & GLOBETROTTER]]> Software available at this site

  • FineSTRUCTURE version 2, a pipeline for running ChromoPainter and FineSTRUCTURE for population inference. A GUI is available for interpretation. Download from the Downloads page.
  • FineSTRUCTURE R scripts, a facility for exploring the results when the GUI is unavailable.
  • GLOBETROTTER, the admixture dating method based on ChromoPainter. Download from the Downloads page.
  • AdmixturePainting, A set of R tools to inmterpret the results of ADMIXTURE and STRUCTURE-like mixture models.
  • RADpainter, finestructure and ChromoPainter for RAD tag data used for non-model organisms.
  • Scripts to perform many types of conversion. Included in the main software download from the Downloads page.
What this page is This page provides information about and downloads for methodology for Chromosome Painting. It is not a facility to analyse your genome. Sorry if you were misled by the punchy name!
About Chromosome Painting Painting is an efficient way of identifying important haplotype information from dense genotype data. It describes ancestry in an efficient way suitable for a range of further analyses, including population identification and admixture dating.

Address of the bookmark: http://paintmychromosomes.com/

]]> Shruti Paniwala https://bioinformaticsonline.com/bookmarks/view/33592/circular-plots-in-r Mon, 19 Jun 2017 06:20:27 -0500 https://bioinformaticsonline.com/bookmarks/view/33592/circular-plots-in-r <![CDATA[Circular plots in R]]>

Circular plots are useful to represent complicated informations. They are used in 2 specific cases: when you have long axis and numerous categories, and when you want to show relationships between elements. The circos gallery displays several examples of circular plots, what gives a nice overview of the possibilities. Circos is the most famous

tool to create circular plots. Thanks to Zuguang Gu, the Circlize library makes the circos functions available in R! It implements low-level graphic functions for adding common graphics in a circular layout. This page aims to learn you how to use the library, so I strongly advise to read the graphics in the proposed order!

image

http://www.r-graph-gallery.com/portfolio/circular-plot/

Address of the bookmark: http://www.r-graph-gallery.com/portfolio/circular-plot/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/34400/ioniser-tools-for-the-quality-assessment-of-data-produced-by-oxford-nanopore%E2%80%99s-minion-sequencer Thu, 23 Nov 2017 10:24:19 -0600 https://bioinformaticsonline.com/bookmarks/view/34400/ioniser-tools-for-the-quality-assessment-of-data-produced-by-oxford-nanopore%E2%80%99s-minion-sequencer <![CDATA[IONiseR: tools for the quality assessment of data produced by Oxford Nanopore’s MinION sequencer]]> This package is intended to provide tools for the quality assessment of data produced by Oxford Nanopore’s MinION sequencer. It includes a functions to generate a number plots for examining the statistics that we think will be useful for this task.

However, nanopore sequencing is an emerging and rapidly developing technology. It is not clear what will be most informative. We hope that IONiseR will provide a framework for visualisation of metrics that we haven’t thought of, and welcome feedback at mike.smith@embl.de.

If you’re not interested in the quality assement of the raw or event level data, and want to jump straight to the getting FASTQ format files from fast5 files you can go straight to the final section of this document.

Address of the bookmark: https://www.bioconductor.org/packages/devel/bioc/vignettes/IONiseR/inst/doc/IONiseR.html

]]> Jit https://bioinformaticsonline.com/news/view/36418/r-350-has-been-released Thu, 26 Apr 2018 11:31:58 -0500 https://bioinformaticsonline.com/news/view/36418/r-350-has-been-released <![CDATA[R 3.5.0 has been Released!]]>
  • The latest version of R is a major release! It comes with a ton of new features, including performance and speed improvements
  • All R packages will now be byte-compiled, hence boosting packages installed from GitHub
  • You may need to re-install all previously installed R packages; old scripts however will continue to work normally

    • More at https://cran.r-project.org/doc/manuals/r-release/NEWS.html

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/37732/making-2d-hilbert-curve Mon, 17 Sep 2018 05:43:35 -0500 https://bioinformaticsonline.com/bookmarks/view/37732/making-2d-hilbert-curve <![CDATA[Making 2D Hilbert Curve]]> Hilbert curve is a type of space-filling curves that folds one dimensional axis into a two dimensional space, but still keeps the locality. It has advantages to visualize data with long axis in following two aspects:

    1. greatly improve resolution of the visualization fron n to √nn;
    2. easy to visualize clusters because generally data points in the axis will also be close in the 2D space.

    This package aims to provide an easy and flexible way to visualize data through Hilbert curve. The implementation and example figures are based on following sources:

    Address of the bookmark: https://bioconductor.org/packages/devel/bioc/vignettes/HilbertCurve/inst/doc/HilbertCurve.html

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/38443/genoplotr-plot-gene-and-genome-maps-project Wed, 12 Dec 2018 08:33:41 -0600 https://bioinformaticsonline.com/bookmarks/view/38443/genoplotr-plot-gene-and-genome-maps-project <![CDATA[genoPlotR - plot gene and genome maps project!]]> genoPlotR is a R package to produce reproducible, publication-grade graphics of gene and genome maps. It allows the user to read from usual format such as protein table files and blast results, as well as home-made tabular files.

    Features

    • Linear representation of several segments of DNA
    • Comparisons represented by areas between the segments (like Artemis, for example)
    • Reads from common formats: Genbank, EMBL, blast, Mauve, and from user-generated tab files
    • Plot several subsegments of the same segment on the same line, separated by a //
    • Automatic or manual placement of the segments on the plot
    • Add annotations to all the lines
    • Create smart, automatic annotations for genomes, based on gene names
    • Add a user-generated tree
    • Add a global scale or a scale to each line
    • Use user-defined graphical functions to represent genes

    Address of the bookmark: http://genoplotr.r-forge.r-project.org/

    ]]>     Abhimanyu Singh     https://bioinformaticsonline.com/bookmarks/view/39956/alluvial-diagram Sat, 21 Sep 2019 07:31:08 -0500 https://bioinformaticsonline.com/bookmarks/view/39956/alluvial-diagram <![CDATA[alluvial diagram]]> Alluvial diagram is a variant of a Parallel Coordinates Plot (PCP) but for categorical variables. Variables are assigned to vertical axes that are parallel. Values are represented with blocks on each axis. Observations are represented with alluvia (sing. “alluvium”) spanning across all the axes.

    Address of the bookmark: https://cran.r-project.org/web/packages/alluvial/vignettes/alluvial.html

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/40721/efs-an-ensemble-feature-selection-tool-implemented-as-r-package-and-web-application Tue, 28 Jan 2020 05:12:23 -0600 https://bioinformaticsonline.com/bookmarks/view/40721/efs-an-ensemble-feature-selection-tool-implemented-as-r-package-and-web-application <![CDATA[EFS: an ensemble feature selection tool implemented as R-package and web-application]]> The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble.

    https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0142-8

    Address of the bookmark: http://efs.heiderlab.de/

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/41272/rainbowr-reliable-association-inference-by-optimizing-weights-with-r-r-package-for-snp-set-gwas-and-multi-kernel-mixed-model Fri, 28 Feb 2020 23:27:37 -0600 https://bioinformaticsonline.com/bookmarks/view/41272/rainbowr-reliable-association-inference-by-optimizing-weights-with-r-r-package-for-snp-set-gwas-and-multi-kernel-mixed-model <![CDATA[RAINBOWR: Reliable Association INference By Optimizing Weights with R (R package for SNP-set GWAS and multi-kernel mixed model)]]> RAINBOWR(Reliable Association INference By Optimizing Weights with R) is a package to perform several types of GWAS as follows.

    • Single-SNP GWAS with RGWAS.normal function
    • SNP-set (or gene set) GWAS with RGWAS.multisnp function (which tests multiple SNPs at the same time)
    • Check epistatic (SNP-set x SNP-set interaction) effects with RGWAS.epistasis (very slow and less reliable)

    https://github.com/KosukeHamazaki/RAINBOWR

    https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007663

    https://cran.r-project.org/web/packages/RAINBOWR/index.html

    Address of the bookmark: https://github.com/KosukeHamazaki/RAINBOWR

    ]]>     Surabhi Chaudhary