<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/39956?offset=90 https://bioinformaticsonline.com/bookmarks/view/34504/minion-gc-an-r-script-to-do-some-qc-on-minion-data Sun, 03 Dec 2017 15:19:18 -0600 https://bioinformaticsonline.com/bookmarks/view/34504/minion-gc-an-r-script-to-do-some-qc-on-minion-data <![CDATA[MinION_GC: An R script to do some QC on MinION data]]> 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

]]> Radha Agarkar 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/blog/view/37586/julia-programming-language-a-python-and-r-rival Sat, 25 Aug 2018 04:46:39 -0500 https://bioinformaticsonline.com/blog/view/37586/julia-programming-language-a-python-and-r-rival <![CDATA[Julia Programming Language, a Python and R rival]]> 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.

    ]]>     Radha Agarkar     https://bioinformaticsonline.com/bookmarks/view/38646/visnetwork-an-r-package-for-network-visualization-using-visjs-javascript-library Wed, 09 Jan 2019 11:00:32 -0600 https://bioinformaticsonline.com/bookmarks/view/38646/visnetwork-an-r-package-for-network-visualization-using-visjs-javascript-library <![CDATA[visNetwork: an R package for network visualization, using vis.js javascript library]]>

    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/

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/38541/geneoverlap-an-r-package-to-test-and-visualize-gene-overlaps Thu, 27 Dec 2018 19:45:52 -0600 https://bioinformaticsonline.com/bookmarks/view/38541/geneoverlap-an-r-package-to-test-and-visualize-gene-overlaps <![CDATA[GeneOverlap: An R package to test and visualize gene overlaps]]> 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

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/39917/chromomap-an-r-package-for-interactive-visualization-and-annotation-of-chromosomes Sat, 07 Sep 2019 10:45:31 -0500 https://bioinformaticsonline.com/bookmarks/view/39917/chromomap-an-r-package-for-interactive-visualization-and-annotation-of-chromosomes <![CDATA[chromoMap-An R package for Interactive Visualization and Annotation of Chromosomes]]> 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

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/40463/%E2%80%98dockr%E2%80%99-the-r-container Mon, 23 Dec 2019 09:56:49 -0600 https://bioinformaticsonline.com/bookmarks/view/40463/%E2%80%98dockr%E2%80%99-the-r-container <![CDATA[‘dockr’: the R container]]> dockr 0.8.6 is now available on CRAN. dockr is a minimal toolkit to build a lightweight Docker container image for your R package, in which the package itself is available. The Docker image seeks to mirror your R session as close as possible with respect to R specific dependencies. Both dependencies on CRAN R packages as well as local non-CRAN R packages will be included in the Docker container image.

    If you want to know, how Docker works, and why you should consider using Docker, please take a look at the Docker website.

    Address of the bookmark: https://www.docker.com/why-docker

    ]]>     Jit     https://bioinformaticsonline.com/bookmarks/view/40964/panev-an-r-package-for-a-pathway-based-network-visualization Sun, 09 Feb 2020 12:41:52 -0600 https://bioinformaticsonline.com/bookmarks/view/40964/panev-an-r-package-for-a-pathway-based-network-visualization <![CDATA[PANEV: an R package for a pathway-based network visualization]]> PANEV (PAthway NEtwork Visualizer) is an R package set for gene/pathway-based network visualization. Based on information available on KEGG, it visualizes genes within a network of multiple levels (from 1 to n) of interconnected upstream and downstream pathways. The network graph visualization helps to interpret functional profiles of a cluster of genes.

    https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3371-7

    Address of the bookmark: https://github.com/vpalombo/PANEV

    ]]>     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     https://bioinformaticsonline.com/bookmarks/view/42299/platypus-%E2%80%93-r-package-for-object-detection-and-image-segmentation Mon, 09 Nov 2020 02:56:25 -0600 https://bioinformaticsonline.com/bookmarks/view/42299/platypus-%E2%80%93-r-package-for-object-detection-and-image-segmentation <![CDATA[Platypus – R package for object detection and image segmentation.]]> platypus is an R package for object detection and semantic segmentation. Currently using 

    platypus you can perform:
    • multi-class semantic segmentation using U-Net architecture
    • multi-class object detection using YOLOv3 architecture

    You can install the latest version of platypus with remotes package:

    remotes::install_github("maju116/platypus")

    Note that in order to install platypus you need to install keras and tensorflow packages and Tensorflow version >= 2.0.0 ( Tensorflow 1.x will not be supported!)

    Address of the bookmark: https://github.com/maju116/platypus

    ]]>     BioStar