Ph.D. in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent or Master’s in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent with 4 years or 5 years of Bachelor’s degree having 1st Division or 60% marks or equivalent overall grade point average, with at least two years of research experience as evidenced from fellowship/ associateship/ training/ other engagements.

Knowledge in System Biology/ Statistical and computational Genomics/ Bioinformatics
Knowledge in computer programming, LINUX OS.
Expertise in use of R/other Bioinformatics software

More at http://iasri.res.in/employment/2017/cabin_advertisement_RA_SRF_YP_Mar2017.pdf

Phenomics of Moisture Deficit Stress Tolerance and Nitrogen Use December 31, 2019

Research Associate (RA) Two (2)

Ph.D. in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent or System Administrator/ Computer expert for database development, development of phenome data bank and virtual phenomics facility, data archiving and Efficiency in Rice and Wheat-Phase II (Funded by National Agricultural Science Fund, ICAR) Master’s in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent with 4 years or 5 years of Bachelor’s degree having 1st Division or 60% marks or equivalent overall grade point average, with at least two years of research experience as evidenced from fellowship/ associateship/ training/ other engagements. maintenance; Development of image analysis algorithms, APIs and IAPs.

Knowledge in System Biology/ Statistical and computational Genomics/ Bioinformatics
Knowledge of programming in LINUX/R/Perl/JAVA/PHP/JSP and use of various software & tools.
December 31, 2019

Ph.D. in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science / Computer Application or equivalent or Master’s in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent with 4 years or 5 years of Bachelor’s degree having 1st Division or 60% marks or equivalent overall grade point average, with at least two years of research experience as evidenced from fellowship/ associateship/ training/ other engagements.

Knowledge of Statistical and Computational Genomics/ Bioinformatics.
Knowledge of programming in LINUX/R/Perl/JAVA/PHP/JSP and use of various software & tools.
March 31, 2020

1. Learn and get updated

Some of the bad biological programmers only learn new technical or non-technical things when it’s absolutely necessary. The good biological programmers learn new technical skills proactively. But great biological programmers not only learn new technical skills on their own but also learn non-technical skills, and have an open mind to sources of knowledge that others may shut out.

In other concrete term, the bad biological programmer learn Perl's regular expression when they started a project on comparative genomics; the good biological programmer learned it a year before because it looked interesting; and the great biological programmer also read about the BioPerl packages, genomics, DNA string, genomic theories, or some similar course of study so that they could understand the results and explain it biologically.

2. Not a merely coder!!!

I often encountered with biological programmer who call themself a hard-core computer programmer and avoid biology. I can almost guarantee that if you are one of them then you are not doing research but merely writing "dry" codes.

According to my supervisor most of the computational biologist, don't know what they are doing biologically. Even they struggle to explain their own programs output and results. Therefore, It is highly advisable to learn basic of biology which can assist you to explain the result and understand your discovery. Always remember you are a researcher not a coder.

3. Be Social with biologist

The computational biologist spends most of the time in from of computers, writing codes. They always think their job is to produce working codes, not technical research perfections. But, they are completely wrong. You should not forget that apart from your computational skills you also need some biologist, other than your supervisor, to explain and make you understand the complex biological mechanism.

I highly recommend your to interact with biotech researchers and learn how do they explain their one graph (which they generally produce after one year of work) biologically. Remember, the origin of your research project is complex biological phenomenon, which is more complex than that of your limited programming rules.

4. Do not search, research for answers

Researching for answers means more than typing several keywords into a search engine or posting a question at Stack Overflow or the BioStars forums. I have entered problems into search engines that generate no results, and every question I posted on Stack Overflow or the BioStars forums never got anything resembling an answer, yet I solved the issues and moved on. I’m not a magician — I just know how to find answers or discover root causes.

Many problems are situational, and if you depend on search engines and forums, you can waste a lot of time going down a rabbit hole and possibly never getting a solution. Learn to perform root cause analysis, learn enough about the underlying system to look for other clues and solutions, and learn to take a long distance view of an issue before deep diving into it.

5. Love and defend your research

You cannot rise to the top in this research profession without loving your work. There are some very good “it’s just a job” biological programmers (I’ve been one at times), but if that is your outlook, you won’t be willing to do whatever it takes to succeed. This idea gets a lot of folks in a huff, because they feel it is a personal insult. “I’m a good programmer, but I have other priorities and can’t make work my life.” I understand completely; I have other priorities too. As much as I hate to say it, when I am passionate about my work, I am willing (though not eager) to abandon my other priorities to finish the job. It is not an insult to say that if you aren’t willing to pull out all the stops you can’t be the best, it is a fact.

You must be passionate about more than programming — you must also be excited about your research, the tools and technology you are using, and so on. I have seen very good and even great biological programmers operating at mediocre levels because something was not a good fit, such as they hated the project or were using a technology they disliked. Therefore, like your research project and get excited about your discoveries. You have not only to discover but also defend your finding with scientific words.

Thanks to all of you for reading.

Know of any other open source software for PacBio data? Email us.

Software categories:

• De novo assembly
• Structural Variations Detection
• Reference-based alignment
• Consensus and variant calling
• RNA analysis
• Epigenetic base modifications and methylation
• Barcoding
• Genome Browsers
• Run QC
• Frameworks and APIs

Address of the bookmark: https://github.com/PacificBiosciences/DevNet/wiki/Compatible-Software

MEME Suite software development; gene expression; mathematical modelling; gene regulation and transcription

Specialization:
Pattern recognition and modelling in computational biology

Link @ http://www.imb.uq.edu.au/tim-bailey

Address of the bookmark: https://github.com/hugheslab/snpgenie

Link @ http://www.newcastle.edu.au/research-and-innovation/centre/cibm/about-us

Address of the bookmark: http://marinetics.org/teaching/hts/Assembly.html

Classes for
Multiple alignment (Bio::Alignment),
Gene Ontology(Bio::GO),
PDB (Bio::PDB),
FANTOM database(Bio::FANTOM),
GFF (Bio::GFF) and KEGG
Orthology (Bio::KEGG::KO).

They also added support for many applications such as PSORT, SOSUI, TargetP, TMHMM, GenScan, ClustalW, MAFFT, and KEGG API.

Wiki Links
http://bioruby.open-bio.org/wiki/BioRubyOnRails
http://dev.bioruby.org/en/

BioRuby in Anger
http://dev.bioruby.org/en/?BioRuby+in+Anger

BioRuby RDocs
http://bioruby.org/rdoc/

BioRuby Tutorial Website
http://dev.bioruby.org/en/?Tutorial.rd

Why BioRuby Hub for BioRuby
http://www.linuxjournal.com/article/5915

Social Coding Hub for BioRuby
http://www.linuxjournal.com/article/5915

Bioinformatics on Rails: BioRuby Tutorial
http://bioinforuby.blogspot.com/2008/02/bioruby-tutorial.html

RRA BioRuby
http://raa.ruby-lang.org/project/bioruby/

BioRuby Project Discussion Group
http://portal.open-bio.org/mailman/listinfo/bioruby

BioRuby related Projects: Project tree
http://rubyforge.org/softwaremap/trove_list.php?form_cat=252

Reference
http://www.jsbi.org/journal/GIW03/GIW03P191.pdf

To begin, input your data using one of the three input styles.

1. "File" takes a correctly formatted.csv file.
2. "List" takes up to 6 different lists that contain unique elements, similar to that used in the web applications BioVenn (Hulsen et al., 2008) and jvenn (Bardou et al., 2014)
3. "Expression" takes the input used by the venneuler R package (Wilkinson, 2015)

Address of the bookmark: https://gehlenborglab.shinyapps.io/upsetr/

Job description
We are looking for a motivated, self-driven post-doctoral researcher to work with large-scale sequence data analysis. The position is for 24 months and located at Mathematical Statistics, Department of Mathematical Sciences in Erik Kristiansson’s research group. We are focused on methods development for and analysis of next generation DNA sequencing, in particular comparative metagenomics and gene expression analysis (RNA-seq). We have strong interdisciplinary profile and are actively collaborating with several experimental groups, especially within the environmental sciences, ecology, infectious diseases and cancer genomics. More information is available at http://bioinformatics.math.chalmers.se.

The Post-doctoral position is an appointment that offers an opportunity to qualify for further research positions within academia or industry. The majority of your working time is devoted to your own research, normally as a member of a research group. Included in your work is also to take part in supervision of Ph.D. students and M.Sc thesis students. Teaching of undergraduate students may also be included to a small extent.

The employment is limited to a maximum of 2 years (1+1).

Qualifications
The applicant should have Ph.D. degree preferably in bioinformatics, mathematics, statistics, computer science or equivalent by the start of the appointment. Experience from analysis of large-scale data, in particular from next generation DNA sequencing, is highly valued. The applicant should also be proficient in programming (e.g. Python/Java/C) and comfortable with Unix/Linux systems. Interaction with experimental biologists is central and good collaborative skills are therefore important. Fluency in written and spoken English is a strong requirement. As a post-doctoral researcher you are expected to work independently and to be able to supervise/co-supervise PhD and Master’s students.

Application procedure
The application should be marked with Ref 20130126 and written in English. The application should be sent electronically via Chalmers webpage.

Application deadline: September 8, 2013.

For questions, please contact:
Ass prof. Erik Kristiansson, Matematiska Vetenskaper, erik.kristiansson@chalmers.se, +46 31-772 3521, +46 70-5259751.

Chalmers continuously strive to be an attractive employer. Equality and diversity are substantial foundations in all activities at Chalmers.