BOL: Related items

Circular Visualization in R

Jit — Wed, 05 Jul 2017 04:11:30 -0500

This is the documentation of the circlize package. Examples in the book are generated under version 0.4.1.

If you use circlize in your publications, I would be appreciated if you can cite:

Gu, Z. (2014) circlize implements and enhances circular visualization in R. Bioinformatics. DOI: 10.1093/bioinformatics/btu393

Address of the bookmark: http://zuguang.de/circlize_book/book/

IONiseR: tools for the quality assessment of data produced by Oxford Nanopore’s MinION sequencer

Jit — Thu, 23 Nov 2017 10:24:19 -0600

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

d3Network:Tools for creating D3 JavaScript network, tree, dendrogram, and Sankey graphs from R.

Jit — Fri, 06 Apr 2018 12:10:45 -0500

Mike Bostock’s D3.js is great for creating interactive network graphs with JavaScript. The d3Network package makes it easy to create these network graphs from R. The main idea is that you should able to take an R data frame with information about the relationships between members of a network and create full network graphs with one command.

Address of the bookmark: http://christophergandrud.github.io/d3Network/

ASAR: Advanced metagenomic Sequence Analysis in R

Jit — Mon, 09 Jul 2018 05:20:50 -0500

An interactive data analysis tool for selection, aggregation and visualization of metagenomic data is presented. Functional analysis with a SEED hierarchy and pathway diagram based on KEGG orthology based upon MG-RAST annotation results is available.

To read the manual, please click the link https://askarbek-orakov.github.io/ASAR/

Address of the bookmark: https://github.com/Askarbek-orakov/ASAR

MetaPlotR: a Perl/R pipeline for plotting metagenes of nucleotide modifications and other transcriptomic sites

Neel — Mon, 05 Nov 2018 08:12:45 -0600

An increasing number of studies are mapping protein binding and nucleotide modifications sites throughout the transcriptome. Often, these sites cluster in certain regions of the transcript, giving clues to their function. Hence, it is informative to summarize where in the transcript these sites occur. A metagene is a simple and effective tool for visualizing the distribution of sites along a simplified transcript model. In this work, we introduce MetaPlotR, a Perl/R pipeline for creating metagene plots.

Address of the bookmark: https://github.com/olarerin/metaPlotR

GeneOverlap: An R package to test and visualize gene overlaps

Jit — Thu, 27 Dec 2018 19:45:52 -0600

Overlapping gene lists can reveal biological meanings and may lead to novel hypotheses. For example, histone modification is an important cellular mechanism that can pack and re-pack chromatin. By making the chromatin structure more dense or loose, the gene expression can be turned on or off. Tri-methylation on lysine 4 of histone H3 (H3K4me3) is associated with gene activation and its genome-wide enrichment can be mapped by using ChIP-seq experiments. Because of its activating role, if we overlap the genes that are bound by H3K4me3 with the genes that are highly expressed, we should expect a positive association. Similary, we can perform such kind of overlapping between the gene lists of different histone modifications with that of various expression groups and establish each histone modification’s role in gene regulation.

Address of the bookmark: https://bioconductor.org/packages/release/bioc/vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf

chromoMap-An R package for Interactive Visualization and Annotation of Chromosomes

Jit — Sat, 07 Sep 2019 10:45:31 -0500

chromoMap provides interactive, configurable and elegant graphics visualization of chromosomes or chromosomal regions allowing users to map chromosome elements (like genes,SNPs etc.) on the chromosome plot.Each chromosome is composed of loci(representing a specific range determined based on chromosome length) that, on hover, shows details about the annotations in that locus range. The plots can be saved as HTML documents that can be shared easily. In addition, you can include them in R Markdown or in R Shiny applications.

Some of the prominent features of the package are:

visualizing polyploidy simultaneously on the same plot.
annotating groups of elements as distinct colors.
creating chromosome heatmaps.
adjusting chromosome range or visualizing chromosome regions such as genes
adding labels to the plot
adding hyperlinks to each element

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

Github replacement !

Rahul Nayak — Thu, 26 Sep 2019 03:42:39 -0500

For a number of reasons researchers have been trying out GitLab as a replacement for for both GitHub and various continuous integration systems, and have been exploring configurations useful for model-fitting pipelines. Researchers turned one of these into an example repository that shows how to use GitLab together with the Rocker Docker images and the drake build system to reproducibly run a project pipeline, using the cacheing functionality across all three tools to make things reasonably speedy and enable both local and remote builds.

Check it out at https://gitlab.com/ecohealthalliance/drake-gitlab-docker-example.

Scientist Bioinformatics Positions

Thu, 30 Jan 2020 06:53:40 -0600

Bioinformatics-Multi_Omics_Integration

https://www.researchgate.net/job/939073_Senior_Scientist_Bioinformatics-Multi_Omics_Integration

Senior_Scientist_Bioinformatics-Transcriptomics_Analysis

https://www.researchgate.net/job/939075_Senior_Scientist_Bioinformatics-Transcriptomics_Analysis-Belgium_France_Switzerland_The_Netherlands

Senior Scientist Bioinformatics - Network Analytics

https://www.researchgate.net/job/939070_Senior_Scientist_Bioinformatics-Network_Analytics_Belgium_France_Switzerland_the_Netherlands

Team Leader Bioinformatics Data Sciences - Mechelen, Belgium

https://www.researchgate.net/job/938787_Team_Leader_Bioinformatics_Data_Sciences-Mechelen_Belgium

Instructions for Creating Your Own R Package

Jit — Wed, 19 Feb 2020 01:22:48 -0600

The following is a step-by-step guide to creating your own R package. Even beyond this course, youmay find this useful for storing functions you create for your own research or for editing existingR packages to suit your needs.

This guide contains three different sets of instructions. If you use RStudio, you can follow the “Ba-sic Instructions” in Section 2 which involve using RStudio’s interface.

Address of the bookmark: http://web.mit.edu/insong/www/pdf/rpackage_instructions.pdf