BOL: Related items

MinION_GC: An R script to do some QC on MinION data

Radha Agarkar — Sun, 03 Dec 2017 15:19:18 -0600

Other tools focus on getting data out of the fastq or fast5 files, which is slow and computationally intensive. The benefit of this approach is that it works on a single, small, .txt summary file. So it's a lot quicker than most other things out there: it takes about a minute to analyse a 4GB flowcell on my laptop.

https://github.com/roblanf/minion_qc

Address of the bookmark: https://github.com/roblanf/minion_qc

R 3.5.0 has been Released!

Jit — Thu, 26 Apr 2018 11:31:58 -0500

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

Julia Programming Language, a Python and R rival

Radha Agarkar — Sat, 25 Aug 2018 04:46:39 -0500

Big data has grown to become one of the most lucrative fields. In fact, data scientists are some of the most sought people. They are usually hired to analyze, control and parse large chunks of data. Implementing these actions using traditional techniques is not a walk in the park. This is why most data scientists prefer using programming languages such as R and Python. However, there is one more programming language that can do the job. That is Julia programming language.

What Is Julia Language?

Julia is a programming language that came into the limelight in 2012. It is a general-purpose programming language that was designed for solving scientific computations. Julia was meant to be an alternative to Python, R and other programming languages that were mainly used for manipulating data. This is because it has numerous features that can minimize the complexities of numerical computations.

Julia optimizes on the best features of Python and R while at the same time overlooks their weaknesses. This explains why it is viewed as an alternative to these programming languages. For instance, it utilizes the readability and simplicity of Python then performs faster.

Julia is the most preferred programming language for data scientists and mathematicians. This is because its core features are similar to the ones that are used on most data software. Also, the language is ideal for these two subjects because its syntax is similar to the standard mathematical formulas.

Key Features Of Julia Language
Uses JIT Compilation
Parallelism
Dynamic Typing
Simple Syntax
Allows Metaprogramming
Accessible to Libraries
-1-Array Indexing

Julia Vs Python And R Programming Languages
1. Speed
Julia is faster than both Python and R. This is a very critical aspect that is given special attention in the big data programming. The high speed of Julia is because of JIT compilers. You will need to install external libraries on Python to achieve similar speed.

2. Syntax
Julia has a math-friendly syntax. The syntax of this programming language is similar to the mathematical formulas hence can be used to perform mathematical and scientific computations. This syntax makes it easier to learn than Python.

3. Parallelism
Although both Python and R use parallelism, Julia uses a top-level parallelism. Julia allows the processor to perform to the optimum level than what Python and R can achieve.

4. Versatility
Julia programming language is more versatile than Python and R. It allows a programmer to move from different codes and functions with ease.

The only area that Python and R are superior to Julia is in terms of community. Given that Julia is a new programming language, it has a small community as compared to others which have been around for years.

In overall Julia programming language is a better alternative that you can use to handle Big data projects. Despite having a small community, it is one of those programming languages that you can easily learn.

visNetwork: an R package for network visualization, using vis.js javascript library

Jit — Wed, 09 Jan 2019 11:00:32 -0600

visNetwork is an R package for network visualization, using vis.js javascript library (http://visjs.org/). All remarks and bugs are welcome on github : https://github.com/datastorm-open/visNetwork.

Features

Based on htmlwidgets, so :

compatible with shiny, R Markdown documents, and RStudio viewer

The package proposes all the features available in vis.js API, and even more with special features for R :

easy to use
custom shapes, styles, colors, sizes, …
works smooth on any modern browser for up to a few thousand nodes and edges
interactivity controls (highlight, collapsed nodes, selection, zoom, physics, movement of nodes, tooltip, events, …)
visualize rpart tree

Address of the bookmark: https://datastorm-open.github.io/visNetwork/

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

PheWAS: R package is designed to provide an accessible interface to the phenome wide association study

BioStar — Thu, 30 Jul 2020 22:06:37 -0500

The PheWAS R package is designed to provide an accessible interface to the phenome wide association study. For a description of the methods available and some simple examples, please see the package vignette or the R documentation. For installation help, see below. ##Installing the PheWAS Package The PheWAS package can be installed using the devtools package. The following code when executed in R will get you started:

install.packages("devtools")
#It may be necessary to install required as not all package dependencies are installed by devtools:
install.packages(c("dplyr","tidyr","ggplot2","MASS","meta","ggrepel","DT"))
devtools::install_github("PheWAS/PheWAS")
library(PheWAS)

Address of the bookmark: https://github.com/PheWAS/PheWAS