<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/27328?offset=670 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/researchlabs/view/5209/anders-krogh-lab Mon, 30 Sep 2013 19:07:40 -0500 <![CDATA[Anders Krogh Lab]]> In a lot of my work in bioinformatics, I have been using hidden Markov models (HMMs). As a postdoc with David Haussler at UCSC we developed the so-called profile HMMs (refs). Since then I have applied HMMs to membrane proteins (refs) and gene identification (refs) and have worked on methods for such things as discriminative estimation of HMMs (refs) and alternative decoding algorithms etc. (refs).

Now my main interests are in gene regulation, where we work on promoter analysis; non-coding RNA, where miRNAs and structure prediction are the main areas; and protein structure, where the group is working on methods for structure prediction from sequence. To read more about these topics, please see the research pages.

Lab page @ http://wiki.binf.ku.dk/User:Krogh

]]> 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/opportunity/view/4946/crcri-bioinfomatics-walk-in-on-08102013 Fri, 27 Sep 2013 10:59:53 -0500 <![CDATA[CRCRI Bioinfomatics Walk In on 08.10.2013]]> Walk-in-Interview for recruitment of one Project Fellow for a period of 10 months purely on temporary basis is proposed to be held at Central Tuber Crops Research Institute, Sreekariyam, Thiruvananthapuram for a KSCSTE funded project entitled “PARTICIPATORY DEVELOPMENT OF A WEB BASED USER FRIENDLY CASSAVA EXPERT SYSTEM”

Salary: Rs. 10,000/- per month.

Age limit: 35 for men and 40 for women & SC/ST.

Qualification: First class in M. Sc (Agriculture)/MCA/M.Sc (IT)/ M. Sc (Computer Application)/M.Sc (Bioinformatics)/M.Sc (Geoinformatics).

Desirable: Two years experience in web design and web programming.

Date & time of interview: 08.10.2013, 10 am

Interested candidates may appear for an interview at this institute along with their application in plain paper containing the following particulars viz. (1) Name (2) Father/Husband/Guardian’s Name (3) date of birth & age as on 01.10.2013 (4) Permanent address (5) Address for communication (6) Email address and Telephone No. with code (7) Qualification (8) National fellowship like ICAR/CSIR/UGC etc. if any (9) Whether SC/ST/OBC (10) Details of experience (Attested copies of degree certificate, proof of age, mark sheets). Original certificates should be produced for verification.

No TA/DA will be admissible to the candidates attending the test. The selected candidate will have to join immediately.

Advertisement: http://www.ctcri.org/careers/mithra_SRF.doc

]]> 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/5187/bioinformatics-algorithms-part-1-with-pavel-pevzner-phillip-e-c-compeau Mon, 30 Sep 2013 11:34:09 -0500 https://bioinformaticsonline.com/videolist/watch/5187/bioinformatics-algorithms-part-1-with-pavel-pevzner-phillip-e-c-compeau <![CDATA[Bioinformatics Algorithms (Part 1) with Pavel Pevzner, Phillip E. C. Compeau,]]>

The course Bioinformatics Algorithms (Part 1) by Pavel Pevzner, Phillip E. C. Compeau, and Nikolay Vyahhi from University of California, San Diego will be offered free of charge to everyone on the Coursera platform. Sign up at http://www.coursera.org/course/bioinformatics.

]]> https://bioinformaticsonline.com/opportunity/view/5255/walk-in-interview-indian-agricultural-statistics-research-institute Wed, 02 Oct 2013 15:40:17 -0500 <![CDATA[Walk-in-Interview @ Indian Agricultural Statistics Research Institute]]> Indian Agricultural Statistics Research Institute
Library Avenue, Pusa, New Delhi – 110012

Walk-in-Interview

Walk-in-interview will be held on October 5, 2013 at 10:00 A.M. at IASRI, New Delhi for a project “A New Distributed Computing Framework for Data Mining” funded by Department of Electronics and Information Technology, Government of India for the following posts. The appointment will be on contractual basis upto 14th October, 2015 or till the termination of the project whichever is earlier and the incumbent shall not have any claim for regular appointment under ICAR.

Research Associate

Ph.D. in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent or

Post-Graduation in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent with 1st Division and at least two years of research experience

 Knowledge of Statistical Analysis /Bioinformatics tools for computational genomics.

 Knowledge of R/Perl programming language

Research Associate

Ph.D. in Computer Science/ Computer Application / Bioinformatics/ Agricultural
Statistics/ Statistics or equivalent or

Post-Graduation in Computer Science/ Computer Application /Bioinformatics/ Agricultural Statistics/ Statistics or equivalent with 1st Division and at least two years of research experience

 Expertise in Java programming.
 Knowledge of system administration and networking under Linux environment.
 Knowledge of parallel programming and cluster computing.

Emoluments for Research Associate: Consolidated Rs:24000/- per month + HRA (for Ph.D. Degree holders) and Rs:23000/- per month + HRA (for Master’s Degree holders)

Age Limit: Age should be not more than 40 years (5 years relaxation for SC/ST/women candidates and 3 years for OBC candidates as on date of interview).

Interested candidates are requested to appear for Walk-in-Interview on the date and time as specified above in Room No. 106, Training Cum Administrative Block of the Institute along with their application giving bio-data with attested copies of certificates, degrees, testimonials, etc. and one passport size photograph.

Original certificates/ Degrees are needed to be produced at the time of interview.

No T.A. /D.A. will be paid for appearing in the interview.

Advertisement: http://www.iasri.res.in/employment/employment.htm

]]> 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/researchlabs/view/5422/shendure-lab Wed, 09 Oct 2013 14:21:58 -0500 <![CDATA[Shendure Lab]]> The Shendure Lab is part of the Department of Genome Sciences at the University of Washington (Seattle, WA). The mission of the lab is to develop and apply new technologies in genomics and molecular biology. Most projects in the lab exploit new DNA sequencing technologies (Shendure et al., Nature Reviews Genetics 2004; Shendure & Ji, Nature Biotechnology 2008; Shendure & Lieberman Aiden, Nature Biotechnology 2012), and generally fall into one of six areas: 1) next-generation human genetics; 2) genome contiguity & completeness; 3) massively parallel functional analysis; 4) molecular tagging; 5) synthetic biology; 6) translational genomics. Our interests in each of these areas are outlined briefly below, and a full list of publications is available via PubMed. http://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=shendure
More http://krishna.gs.washington.edu/research.html

Lab page @ http://krishna.gs.washington.edu/index.html

]]> https://bioinformaticsonline.com/bookmarks/view/40959/bioinformatics-related-group Sun, 09 Feb 2020 03:17:16 -0600 https://bioinformaticsonline.com/bookmarks/view/40959/bioinformatics-related-group <![CDATA[Bioinformatics related group]]> FaBI emerged from the respective groups of the four founding societies GI (German Informatics Society), DECHEMA (Society for Chemical Engineering and Biotechnology), GBM (Society for Biochemistry and Molecular Biology) and GDCh (German Chemical Society). In fall 2015, the GMDS (German Society for Medical Informatics, Biometry, and Epidemiology) joined FaBI. FaBI represents more than 750 members today and considers itself as a joint representation of interests of bioinformatics research in Germany and as an interlocutor for politics, economy, and society aiming at a strong informatics-based life science research.

Address of the bookmark: https://bioinformatik.de/en/bioinformatics-in-germany/research/research-groups.html

]]> Rahul Agarwal