<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/36111?offset=30 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/38385/decipher-a-software-toolset-for-deciphering-and-managing-biological-sequences-efficiently-using-the-r Sun, 09 Dec 2018 19:06:17 -0600 https://bioinformaticsonline.com/bookmarks/view/38385/decipher-a-software-toolset-for-deciphering-and-managing-biological-sequences-efficiently-using-the-r <![CDATA[DECIPHER; a software toolset for deciphering and managing biological sequences efficiently using the R]]> DECIPHER is a software toolset that can be used for deciphering and managing biological sequences efficiently using the R programming language. The R package is distributed as platform independent source code under the GPL version 3 license. Some functionality of the program is accessible online through web tools.

     

    Address of the bookmark: http://www2.decipher.codes/

    ]]>     BioStar     https://bioinformaticsonline.com/bookmarks/view/38819/upsetr-an-r-package-for-the-visualization-of-intersecting-sets-and-their-properties Mon, 28 Jan 2019 18:38:44 -0600 https://bioinformaticsonline.com/bookmarks/view/38819/upsetr-an-r-package-for-the-visualization-of-intersecting-sets-and-their-properties <![CDATA[UpSetR: An R Package for the Visualization of Intersecting Sets and their Properties]]> UpSetR generates static UpSet plots. The UpSet technique visualizes set intersections in a matrix layout and introduces aggregates based on groupings and queries. The matrix layout enables the effective representation of associated data, such as the number of elements in the aggregates and intersections, as well as additional summary statistics derived from subset or element attributes.

    For further details about the original technique see the UpSet website. You can also check out the UpSetR shiny app. Here is the source code for the shiny wrapper.

    A Python package called py-upset to create UpSet plots has been created by GitHub user ImSoErgodic.

    Address of the bookmark: https://github.com/hms-dbmi/UpSetR/

    ]]>     BioStar     https://bioinformaticsonline.com/bookmarks/view/39884/retrieving-taxonomic-information-with-r Thu, 29 Aug 2019 01:38:39 -0500 https://bioinformaticsonline.com/bookmarks/view/39884/retrieving-taxonomic-information-with-r <![CDATA[Retrieving Taxonomic Information with R]]> This vignette will introduce users to the retrieval of taxonomic information with myTAI. The taxonomy() function implemented in myTAI relies on the powerful package taxize. Nevertheless, taxonomic information retrieval has been customized for the myTAI standard and for organism specific information retrieval.

    Specifically, the taxonomy() function implemented in myTAI can be used to classify genomes according to phylogenetic classification into Phylostrata (Phylostratigraphy) or to retrieve species specific taxonomic information when performing Divergence Stratigraphy (see Introduction for details).

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

    ]]>     Rahul Nayak     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/opportunity/view/41041/post-doc-computational-biology-bioinformatics-network-biology-data-science-ngs-mfd Sat, 15 Feb 2020 06:13:35 -0600 <![CDATA[Post Doc Computational Biology, Bioinformatics - Network Biology & Data Science, NGS (m/f/d)]]> https://www.jobvector.de/jobs-stellenangebote/biologie-life-sciences/forschung-entwicklung/post-doc-computational-biology-bioinformatics-network-biology-data-science-ngs-129867.html?suid=e522e9793b41817e52ac58d6963b94e2519920df

    Requirements
    Doctoral degree in Bioinformatics, Computational Biology, (Bio)physics/-mathematics, Biochemistry/Biology or similar with strong quantitative and numeric focus
    Ability to numerically process complex and large data sets
    Good programming skills (R/Bioconductor and/or Python preferred, Linux is a plus)
    Experience in analyzing next-generation sequencing data sets using network biology
    Scientific publication record in applied bioinformatics
    Familiarity with single cell NGS analyses and other –omics techniques is a plus, but not essential

    ]]>         https://bioinformaticsonline.com/bookmarks/view/41362/genemates-an-r-package-for-detecting-horizontal-gene-co-transfer-between-bacteria-using-gene-gene-associations-controlled-for-population-structure Sat, 07 Mar 2020 05:52:20 -0600 https://bioinformaticsonline.com/bookmarks/view/41362/genemates-an-r-package-for-detecting-horizontal-gene-co-transfer-between-bacteria-using-gene-gene-associations-controlled-for-population-structure <![CDATA[GeneMates: an R package for Detecting Horizontal Gene Co-transfer between Bacteria Using Gene-gene Associations Controlled for Population Structure]]> GeneMates is an R package implementing a network approach to identify horizontal gene co-transfer (HGcoT) between bacteria using whole-genome sequencing (WGS) data. It is particularly useful for investigating intra-species HGcoT, where presence-absence status of acquired genes is usually confounded by bacterial population structure due to clonal reproduction.

    https://www.biorxiv.org/content/10.1101/2020.02.29.970970v1

    Address of the bookmark: https://github.com/wanyuac/GeneMates

    ]]>     Rahul Nayak     https://bioinformaticsonline.com/bookmarks/view/42313/crbhits-from-conditional-reciprocal-best-hits-to-codon-alignments-and-kaks-in-r Wed, 11 Nov 2020 23:06:03 -0600 https://bioinformaticsonline.com/bookmarks/view/42313/crbhits-from-conditional-reciprocal-best-hits-to-codon-alignments-and-kaks-in-r <![CDATA[CRBHits: From Conditional Reciprocal Best Hits to Codon Alignments and Ka/Ks in R]]> CRBHits is a coding sequence (CDS) analysis pipeline in R (R Core Team, 2019). It reimplements the Conditional Reciprocal Best Hit (CRBH) algorithm crb-blast and covers all necessary steps from sequence similarity searches, codon alignments to Ka/Ks calculations and synteny. The new R package targets ecology, population and evolutionary biologists working in the field of comparative genomics.

    Address of the bookmark: https://gitlab.gwdg.de/mpievolbio-it/crbhits

    ]]>     Shruti Paniwala     https://bioinformaticsonline.com/bookmarks/view/44541/powerful-books-for-learning-data-analysis-with-r Tue, 28 May 2024 07:42:56 -0500 https://bioinformaticsonline.com/bookmarks/view/44541/powerful-books-for-learning-data-analysis-with-r <![CDATA[Powerful books for learning data analysis with R]]> R is powerful tool for data analysis, visualization, and machine learning. And it costs $0 to use! Here are six FREE books you can use to learn R today:

    https://csgillespie.github.io/efficientR/

    https://r-graphics.org/

    https://rstudio-education.github.io/hopr/

    https://r-pkgs.org/

    https://r4ds.had.co.nz/

     

    Address of the bookmark: https://r-graphics.org/

    ]]>     LEGE