<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/30355?offset=590 https://bioinformaticsonline.com/file/view/2741/bioinformatician-dreams Wed, 21 Aug 2013 10:50:45 -0500 https://bioinformaticsonline.com/file/view/2741/bioinformatician-dreams <![CDATA[Bioinformatician Dreams]]> Bioinformatician life is interconnected, they always dream for a powerful server, little more space on server as they are generating lots of data per run, dream to publish results in good impact journals, meetings reminders :) and research analysis off course!!! 

]]> Jitendra Narayan https://bioinformaticsonline.com/pages/view/35805/python-learning-resources-for-bioinformatics-and-computational-biologist Fri, 02 Mar 2018 06:54:15 -0600 https://bioinformaticsonline.com/pages/view/35805/python-learning-resources-for-bioinformatics-and-computational-biologist <![CDATA[Python learning resources for bioinformatics and computational biologist !]]> Python is a general-purpose language, which means it can be used to build just about anything, which will be made easy with the right tools/libraries.

Professionally, Python is great for backend web development, data analysis, artificial intelligence, and scientific computing. Many developers have also used Python to build productivity tools, games, and desktop apps, so there are plenty of resources to help you learn how to do those as well.

For pros and cons visit http://www.bestprogramminglanguagefor.me/why-learn-python and http://bioinformaticsonline.com/discussion/view/459/python-vs-perl

More resources at https://github.com/CodementorIO/Python-Learning-Resources

Following are the list of useful python programming resources:

]]> Jit https://bioinformaticsonline.com/videolist/watch/4072/bioinformatics Wed, 28 Aug 2013 19:16:33 -0500 https://bioinformaticsonline.com/videolist/watch/4072/bioinformatics <![CDATA[BIOINFORMATICS]]> This is a promo video for the brand new cross-boarder branch of study - BIOINFORMATICS. It´s a co-operation between Johannes Kepler University in Linz (Austria) and University of South Bohemia in České Budějovice (Czech Republic). Written, Edited and Directed by, DOP, VFX: Jan Míka Sound by: Mirek Šmilauer Narrator: Jack Bright Produced by: FILMOFON (http://www.filmofon.cz) Released: Nov 2012]]> https://bioinformaticsonline.com/pages/view/36603/learning-python-programming-a-bioinformatician-perspective Mon, 14 May 2018 16:33:03 -0500 https://bioinformaticsonline.com/pages/view/36603/learning-python-programming-a-bioinformatician-perspective <![CDATA[Learning Python Programming - a bioinformatician perspective !]]> Python Programming is a general purpose programming language that is open source, flexible, powerful and easy to use. One of the most important features of python is its rich set of utilities and libraries for data processing and analytics tasks. In the current era of big biological data, python and biopython is getting more popularity due to its easy-to-use features which supports big data processing.

In this tutorial series article, I will explore features and packages of python which are widely used in the big data, NGS, and bioinformatics. I will also walk through a real biological example which shows NGS data processing with the help of python packages and programming.

Python has a couple of points to recommend it to biologists and scientists specifically:

  • It's widely used in the scientific community
  • It has a couple of very well designed libraries for doing complex scientific computing (although we won't encounter them in this book)
  • It lend itself well to being integrated with other, existing tools
  • It has features which make it easy to manipulate strings of characters (for example, strings of DNA bases and protein amino acid residues, which we as biologists are particularly fond of)

In general, following are some of the important features of python which makes it a perfect fit for rapid application development.

  • Python is interpreted language so the program does not need to be compiled. Interpreter parses the program code and generates the output.
  • Python is dynamically typed, so the variables types are defined automatically.
  • Python is strongly typed. So the developers need to cast the type manually.
  • Less code and more use makes it more acceptable.
  • Python is portable, extendable and scalable.

There are two major Python versions, Python 2 and Python 3. Python 2 and 3 are quite different. This tutorial uses Python 3, because it more semantically correct and supports newer features.

I will post tutorial on daily basis on this page. Check the sub-pages on right side.

]]> Rahul Nayak https://bioinformaticsonline.com/bookmarks/view/3046/r-and-bioconductor-tutorial Fri, 23 Aug 2013 08:23:59 -0500 https://bioinformaticsonline.com/bookmarks/view/3046/r-and-bioconductor-tutorial <![CDATA[R and Bioconductor Tutorial]]> This tutorial is intended to introduce users quickly to the basics of R, focusing on a few common tasks that  biologists need to perform  some basic analysis:  load a table, plot some graphs, and perform some basic statistics. More extensive tutorials can be found on the project website and via bioconductor (not covered here).

You can add more tutorial links in comments if found new pages.

Address of the bookmark: http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual

]]> Jitendra Narayan https://bioinformaticsonline.com/bookmarks/view/39370/multiphate-bioinformatics-pipeline-for-functional-annotation-of-phage-isolates Thu, 16 May 2019 00:17:39 -0500 https://bioinformaticsonline.com/bookmarks/view/39370/multiphate-bioinformatics-pipeline-for-functional-annotation-of-phage-isolates <![CDATA[multiPhATE: bioinformatics pipeline for functional annotation of phage isolates]]> multiple-genome Phage Annotation Toolkit and Evaluator (multiPhATE). multiPhATE is a throughput pipeline driver that invokes an annotation pipeline (PhATE) across a user-specified set of phage genomes. This tool incorporates a de novo phage gene-calling algorithm and assigns putative functions to gene calls using protein-, virus-, and phage-centric databases. 

 

Address of the bookmark: https://github.com/carolzhou/multiPhATE

]]> Abhimanyu Singh https://bioinformaticsonline.com/videolist/watch/4043/what-is-bioinformatics Wed, 28 Aug 2013 06:53:05 -0500 https://bioinformaticsonline.com/videolist/watch/4043/what-is-bioinformatics <![CDATA[What is Bioinformatics?]]> Illustration and Animation: Rachel Robinson Script: Tiffany Trent Voice-over: Kris Monger Sound: Glisten Carefully by Guennadi Malyshevski]]> https://bioinformaticsonline.com/videolist/watch/4093/ibm-research-computational-biology-center Thu, 29 Aug 2013 08:43:59 -0500 https://bioinformaticsonline.com/videolist/watch/4093/ibm-research-computational-biology-center <![CDATA[IBM Research Computational Biology Center]]> The IBM Computational Biology Center embraces activities at Yorktown Heights, with strong affiliations with activities at Almaden and other IBM Research Centers. Computational Biology (CompBio) including bioinformatics is the study of how computer systems can manage, analyze, and simulate the complex structures and processes inherent in living systems. CompBio Research at IBM spans pattern recognition in sequences, structures and processes, the studying of systems ranging from single protein molecules through to complex molecular interactions, and the data analysis, interpretation and reverse-engineering of complex disease-lifestyle-genomic interactions for fuller biological understanding. "CompBio" has a flavor of its own independant of its parents, biology and computer science. Nonetheless, CompBio is inherently a multi- disciplinary field with important applications in biology, chemical physics, materials science, agriculture, chemistry and ultimately nanotechnology.]]> https://bioinformaticsonline.com/bookmarks/view/40272/seq-a-high-performance-pythonic-language-for-bioinformatics Sat, 23 Nov 2019 08:58:12 -0600 https://bioinformaticsonline.com/bookmarks/view/40272/seq-a-high-performance-pythonic-language-for-bioinformatics <![CDATA[Seq: A high-performance, Pythonic language for bioinformatics]]>  

Seq is a programming language for computational genomics and bioinformatics. With a Python-compatible syntax and a host of domain-specific features and optimizations, Seq makes writing high-performance genomics software as easy as writing Python code, and achieves performance comparable to (and in many cases better than) C/C++.

Learn more by following the tutorial or from the cookbook.

Address of the bookmark: https://seq-lang.org

]]> Rahul Nayak https://bioinformaticsonline.com/videolist/watch/4271/may-17-2013-differential-network-analysis Wed, 04 Sep 2013 16:22:28 -0500 https://bioinformaticsonline.com/videolist/watch/4271/may-17-2013-differential-network-analysis <![CDATA[May 17, 2013 - Differential Network Analysis]]> Steve Horvath presents Differential Network Analysis at the UCLA Human Genetics/Biostatistics Network Course]]>