<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/30897?offset=320 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/videolist/watch/12288/genomic-medicine-bruce-korf-2014 Tue, 24 Jun 2014 07:58:44 -0500 https://bioinformaticsonline.com/videolist/watch/12288/genomic-medicine-bruce-korf-2014 <![CDATA[Genomic Medicine - Bruce Korf (2014)]]> May 21, 2014 - Current Topics in Genome Analysis 2014 A lecture series covering contemporary areas in genomics and bioinformatics. More: http://www.genome.gov/COURSE2014]]> 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/opportunity/view/12594/faculty-positions-at-central-university-of-punjab Mon, 07 Jul 2014 23:33:33 -0500 <![CDATA[Faculty Positions at Central University of Punjab]]> Faculty Positions: Rolling/Open Advertisement Advt.No: T-10 (2013)

Pay Scale: Pay Band Rs.15600-39100 with AGP of Rs.6,000/-

Essential Qualifications for Professors, Associate Professors, and Assistant Professors: As per “UGC REGULATIONS ON MINIMUM QUALIFICATIONS FOR APPOINTMENT OF TEACHERS AND OTHER ACADEMIC STAFF IN UNIVERSITIES AND COLLEGES AND MEASURES FOR THE MAINTENANCE OF STANDARDS IN HIGHER EDUCATION 2010“ and the 2nd Amendments to the regulation issued in June 2013.

For details: http://www.ugc.ac.in/oldpdf/regulations/revised_finalugcregulationfinal10.pdf http://www.ugc.ac.in/pdfnews/8539300_English.pdf and University rules.

Procedure to apply:

Application forms along with API form complete in all respect along with necessary documents and application fee of Rs. 500/-. (Rs. 250/- for Scheduled Caste/Scheduled Tribe/Person with disabilities) should be sent to:

Registrar, Central University of Punjab, City Campus, Mansa Road, Bathinda-151001

For more info visit: http://www.centralunipunjab.com/Teaching/Final%20Details-t10-2013.pdf, http://www.centralunipunjab.com/Teaching/Advertisement-t10-2013.jpg

Last Apply Date: 31 Dec 2014

]]> 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/12787/integrative-genomics-viewer-igv-tutorial Sat, 12 Jul 2014 15:16:23 -0500 https://bioinformaticsonline.com/bookmarks/view/12787/integrative-genomics-viewer-igv-tutorial <![CDATA[Integrative Genomics Viewer (IGV) tutorial]]> The Integrative Genomics Viewer (IGV) from the Broad Center allows you to view several types of data files involved in any NGS analysis that employs a reference genome, including how reads from a dataset are mapped, gene annotations, and predicted genetic variants.

http://www.broadinstitute.org/igv/

Address of the bookmark: https://wikis.utexas.edu/display/bioiteam/Integrative+Genomics+Viewer+%28IGV%29+tutorial

]]> Neel 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/12963/cosmos-our-workflow-management-system-for-ngs-data Wed, 23 Jul 2014 07:29:14 -0500 https://bioinformaticsonline.com/bookmarks/view/12963/cosmos-our-workflow-management-system-for-ngs-data <![CDATA[COSMOS, our workflow management system for NGS data]]> COSMOS, our Python-based management system for implementing large-scale parallel workflows focusing on, but not restricted to, large-scale short-read "NGS" sequencing data is open-access published via Advance Access in Bioinformatics (Gafni et al. 2014).  It is also available for download for non-commercial academic and research purposes at:

 http://cosmos.hms.harvard.edu/.

Address of the bookmark: https://cosmos.hms.harvard.edu/

]]> 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/researchlabs/view/12995/national-center-for-bioinformatics-ncb Wed, 23 Jul 2014 14:10:49 -0500 <![CDATA[National Center for Bioinformatics (NCB)]]> NCB is offering M.Phil and Ph.D programs in the area of Bioinformatics. The major goal of NCB is to promote quality training and research in the area of Bioinformatics. Bioinformatics originated as a cross-disciplinary field as the need for computational sections to research problem raised in biomedicine.

More at http://ncb.qau.edu.pk/

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