• Bioinformatics - RSS feed of current and advance online publications
• Genome Research - current & advance
• Genome Biology - editors picks, latest, most viewed, most forwarded. (Hit the RSS icon under each tab).
• PLoS Genetics - new articles
• PLoS Computational Biology - new articles
• Nature Genetics - current TOC and AOP
• Nature Reviews Genetics - current TOC and AOP

• Bioinformatics
• BMC Bioinformatics
• Briefings in Bioinformatics
• Genome Biology
• Genome Research: current and AOP
• Microbiome
• Nature Genetics, current & AOP
• Nature Reviews Genetics, current & AOP
• Nucleic Acids Research
• PLOS Computational Biology
• PLOS Genetics

Blogs. Some of these blogs are very relevant to bioinfo jobs. Others are more personal interest.

• The OpenHelix Blog
• Ensembl blog
• Galaxy News
• Blue Collar Bioinformatics
• Homologus
• Golden Helix - our 2 SNPs
• Genomics Law Report
• R-bloggers (aggregates feeds from >350 blogs about R)
• Genomes Unzipped
• Jason Moore's Epistasis Blog
• 23andMe - the Spitoon

• Variance Explained: David Robinson’s blog (Data Scientist at Stack Overflow, works in R and Python).
• Global Biodefense: News on pathogens, outbreaks, and preparedness, with periodic posts on genomics and bioinformatics-related developments and funding opportunities.
• In between lines of code: Lex Nederbragt’s blog on biology, sequencing, bioinformatics, …
• Simply Statistics: A statistics blog by Rafa Irizarry, Roger Peng, and Jeff Leek.
• Bits of DNA: Reviews and commentary on computational biology by Lior Pachter (fair warning: dialogue here can get a bit heated!).
• Blue Collar Bioinformatics: articles related tool validation and the open source bioinformatics community.
• Microbiome Digest – Bik’s Picks: A daily digest of scientific microbiome papers, by Elisabeth Bik, Science Editor at uBiome.
• Living in an Ivory Basement: Titus Brown’s blog on metagenomics, open science, testing, reproducibility, and programming.
• Enseqlopedia: James Hadfield’s blog on all things NGS.
• Epistasis Blog: Jason Moore’s computational biology blog.
• RStudio Blog: announcements about new RStudio functionality, updates about the tidyverse, and more.
• nextgenseek.com: Next-Gen Sequencing Blog covering new developments in NGS data & analysis.
• RNA-Seq Blog: Transcriptome Research & Industry News.
• The Allium: We all need a little humor in our lives. Like The Onion, but for science.

Forums.

• Seqanswers - bioinformatics forum
• Seqanswers - RNA-Seq forum
• BioStar
• BOL

Project Title : “Integrating genome scale metabolic analysis of model plant pathogen Ralstonia solanacearum with RNAseq and fluxomics”

Essential qualification : M.Tech. in CSE/IT (With specialization in Computational Biology/Bioinformatics) or M.Sc. in Bioinformatics/Biosciences/Molecular Biology Biotechnology preferably with NET/GATE/BET. Candidates should have minimum 55 % mark both in 10th and 10+2 Science examinations and mathematics at 10+2 Science. Desirable: Preference will be given to the candidates having experience in computational analysis of genome sequences or similar projects.

No. of Post : 01

Remuneration : Rs. 25,000/- for the 1st two years and Rs. 28,000/- for the 3rd year for SRF and applicable to the candidate having post graduate degree in Basic Science with NET/GATE/BET qualification or post graduate degree in professional course. Rs. 12,000/- for the 1st two years and Rs. 14,000/- for the 3 rd year for SRF,

Age : 28 years

Duration : Three (03) years or till completion of the project or until further order, whichever is earlier.

Hiring Process : Walk - In
Job Role: Research/JRF/SRF

Walk-in-interview will be held on 17th February, 2017, 11.15 a.m. at the office of the Head, Department of Computer Science and Engineering, Tezpur University.

Interested candidates may appear before the interview board with original documents from 10th standard onwards and photocopies of mark sheets, certificates, testimonials, caste certificate (if applicable), experience certificate certificates of NET/GATE/BET or similar examination qualifications, any other testimonials and a copy of recent curriculum vitae (CV) on the day of interview.

More at http://www.tezu.ernet.in/ProjectWalkin/Advt-DoRD-CSE-SSS-20-295-112-A.pdf

Reference: http://www.unitedformedicalresearch.com/wp-content/uploads/2013/06/The-Impact-of-Genomics-on-the-US-Economy.pdf

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.

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