ESSENTIAL QUALIFICATIONS:
We are looking for a motivated candidate with keen interest in bioinformatics and microbial genome evolution. The candidate must have a Master’s Degree in Bioinformatics, Computational Biology, Computer Science, Microbiology, Biology or a related field with good academic record.

DESIRABLE QUALIFICATIONS
Hands on research experience on handling next generation sequencing data and phylogenetic reconstruction methods. Excellent programming skills (Perl/Python/Java/Php) and experience in working on Unix/Linux platform is preferred. Furthermore; good knowledge is required in statistics (R/Matlab) and the application of bioinformatics analysis tools.

AGE:
Below 28 years as on 15th November, 2016.

EMOLUMENTS:
Rs. 25,000 + 20% HRA for NET/GATE qualified and Post Graduate in Professional Degree course qualified candidates and
Rs. 12,000/- + 20% HRA for others.

DURATION:
Initial appointment will be given for one year and further extension will be based on the performance till termination of the project.
Only those fulfilling the above criteria need apply and will be called for interview. In the event of more than 10 candidates being short-listed by screening the applications, a written test will be conducted before the selection interview and only those who are successful in the written test will be interviewed. No TA/ DA will be given for appearing in the interview.

Suitably qualified candidates may send applications in the prescribed format (Download here) with a photograph, a copy of full resume indicating the percentage of Marks obtained and attested photocopies of credentials & experience to reach the undersigned on or before 15th November, 2016. Envelopes must be superscripted with abbreviated title of the project, advertisement number and job title. Selection to the position will not entitle the candidate to any future positions at RGCB (permanent or otherwise). As with all project positions at RGCB, the position will be co terminus with end of the project.

Research Assistant Bioinformatics recruitment in National Institute Of Cancer Prevention & Research (ICMR) on Contract basis
Project entitled: “Next generation EGFR inhibitor identification using ligand based QSAR technique”

Essential: M.Sc. in Bioinformatics or related field. Desirable: Experience in QSAR and structure based drug designing.
Age: 28 years
No.of Post: 1

Pay Scale : Rs.27000

Application format is attached and should be sent by post to Dr. Subhash M Agarwal, Scientist D, Division of Bioinformatics, National Institute of Cancer Prevention & Research (ICMR), Plot No. I-7, Sector-39, Noida 201301 (U.P).

More at http://www.icmr.nic.in/icmrnews/NICPR_Advertisement%20for%20RA.pdf

More at
https://zfin.org/ZDB-LAB-110408-1
http://www.imcb.a-star.edu.sg/php/venkatesh.php

Positions available

Applications were invited from for the following posts in an industry sponsored project. The project entitled "OsHK3b technology and Know How", valid for a period upto February, 2018.

Post 3: Senior Research Fellow (Computational Biologist / Metabolic engineering)

Salary: As per DBT rule.

Duration: All the above posts are purely temporary and liable to be terminated at any time without prior notice or ceased/withdrawn by the funding agency.

Age limit: The upper age limit for SRF shall be 32 years, which is relaxed upto 5 years in the case of candidates belonging to Schedule Castes/Schedule Tribes, Women, Physically Handicapped and OBC applicants.

Essential Qualifications: Masters/B Tech/Mtech in Basic Sciences with at least 2yrs of research experience in Bioinformatics/Computational Biology related to Database /portal building & maintenance, high throughput data handling and analysis etc. For M.Sc/B.Tech, Published paper in peer-reviewed Journal and for M.Tech, thesis submission in computational biology is a must. Selection preference will be given to candidates with a good knowledge of Python and/or R. Knowledge of JAVA will also get a special consideration.

Desired Skills: Will be expected to manage ongoing research activities in the project, interact with Experimental group, manage the project data analysis, prepare file reports and associated project work etc. Familiarity with plant systems biology and genomics /metabolite resources related to plant metabolomics is desirable.

1. The post applied for must be clearly written on the Envelope containing the application
2. Applications received after last date shall not be entertained, School will not be responsible for any postal delay.
3. No application will be accepted via hand delivery or via e-mail. Please send printed & signed applications with detailed CV on or before 31st January, 2017 by post to the following address:

Prof. Ashwani Pareek
(Project Investigator)
Stress Physiology and Molecular Biology Laboratory (Room No-413),
School of Life Sciences,
Jawaharlal Nehru University,
New Delhi, India – 110067
Email: ashwanipareek@gmail.com

Applications are invited for:

Junior Research Fellow, in a DBT funded project, is available in Translational Health Group, ICGEB, New Delhi

Qualifications:

Education: M.Sc. (preferably in Biotechnology, Life Sciences or Zoology, Chemistry, Bioinformatics). Candidates with hands on experience on GC-MS data acquisition and analysis will be given preference. Bioinformatics expertise required.

Fellowship: As per DBT guidelines.

Tenure: The position is purely on temporary basis with an initial tenure of six months and based on satisfactory performance may continue until the completion of the project.

Closing date for applications: 04/03/2017

Please send a "TWO PAGE" CV by email to: th.icgeb@gmail.com on or before the last date.

Research Associate, in a DBT funded project, is available in Translational Health Group, ICGEB, New Delhi

Qualifications:

Education: Ph.D. (in Biology, Biotechnology, Chemistry, Bioinformatics). Candidates with hands on experience on GC-MS data acquisition and analysis will be given preference.

Fellowship: As per DBT guidelines.

Tenure: The position is purely on temporary basis with an initial tenure of six months and based on satisfactory performance may continue until the completion of the project.

Closing date for applications: 04/03/2017

Please send a "TWO PAGE" CV by email to: th.icgeb@gmail.com on or before the last date.

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

Applications are invited for the STUDENTSHIP and TRAINEESHIP vacancies to carry out project/research work in the DBT - Bioinformatics Infrastructure Facility with consolidated stipend of Rs.5,000/- per month.

Essential Qualification

Student Trainee: Those who have completed M.Sc., Bioinformatics/Biophysics/Life sciences or Pursuing M.Tech., Bioinformatics/Biotechnology

Duration : 3-4 Months

Student Trainee: Those who are pursuing M.Sc Bioinformatics/Biophysics/ Life sciences/others

Duration : 2-3 Months

Mail your CV on or before 25th November 2013 to shirai2011@gmail.com and hard copy to "Dr. D. Velmurugan, Professor & Head, CAS in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai 600 025". Also, the applicants are requested to attend the interview on 29th November, 2013 at 11 A.M.

Advertisement:

www.unom.ac.in/uploads/announcements/bifadvertisement_20131114080003_23240.pdf

Genomes and genomics:

Bioinformatics and Genomics:

Structural genomics tutorial:

Comparative Genomics Tutorial:

GENOME TUTORIAL:

Tools and resources for identifying protein families, domains and motifs

Bioinformatics Tools 
Tips, Tutorials, and Terminology for Using Selected Resources in Genome Database Guide:

A Web-Based Comparative Genomics Tutorial for Investigating Microbial Genomes:

Free Online Tutorials Teach Anyone How to Use Genome Databases:

Circos to create concise, explanatory, unique and print-ready visualizations of your data:

Genomics and Comparative Genomics Learning Module:

Computational Challenges in Comparative Genomics

A Tutorial:

A Comparative Genomics Resource for Grains:

PLAZA: A Comparative Genomics Resource to Study Gene and Genome Evolution in Plants:

VISTA:

Software for Genomics

1. Artemis Artemis is a free genome viewer and annotation tool that allows visualization of sequence features and the results of analyses within the context of the sequence, and its six-frame translation.
2. Chromas It will display and prints chromatogram files from ABI automated DNA sequencers, and Staden SCF files which the analysis programs for ALF, Li-Cor and Visible Genetics OpenGene sequencers can create.
3. Glimmer A system for finding genes in microbial DNA, especially the genomes of bacteria and archaea.Glimmer (Gene Locator and Interpolated Markov Modeler) uses interpolated Markov models (IMMs) to identify the coding regions and distinguish them from noncoding DN
4. Glimmer HMM A fast and accurate gene finder based on a GHMM architecture, developed specifically for eukaryotes. It incorporates splice site models adapted from the GeneSplicer program and uses interpolated Markov models for evaluating the coding regions.
5. Glimmer M A gene finder derived from Glimmer, but developed specifically for eukaryotes. It is based on a dynamic programming algorithm that considers all combinations of possible exons for inclusion in a gene model and chooses the best of these combinations. The d
6. MUMmer MUMmer is a system for rapidly aligning entire genomes, whether in complete or draft form.
7. pDRAW pDRAW32 is being developed as a free time hobby project. It is far from finished, but as it has reached a point where it could be helpful for many labs, it is now available to the scientific community.
8. Sequin Sequin is a stand-alone software tool developed by the NCBI for submitting and updating entries to the GenBank, EMBL, or DDBJ sequence databases. It is capable of handling simple submissions that contain a single short mRNA sequence, and complex submissio
9. Staden The Staden Package consists of a series of tools for DNA sequence preparation (pregap4), assembly (gap4), editing (gap4) and DNA/protein sequence analysis (spin).

For more software @ http://bioinformaticsonline.com/bookmarks/view/926/list-of-popular-bioinformatics-softwaretools

The most common newbie questions are:

Should I try to use these free open source programs?  Why are we not trying GUI software for computational analysis? Should I use commercial bioinformatics programs/software?”

1. Let’s be open

We generally think free and cheap are useless. But this concept is not applicable when we discuss open source software. Mostly, the bioinformatics software is developed by highly competitive biological programmers who believe in open sharing of knowledge. They come under Open Bioinformatics Foundation or O|B|F which is a non-profit, volunteer run organization focused on supporting open source programming in bioinformatics. The best part about open source tools/software is that they’re free to download the source code and read exactly what the program does. If you are so inclined, you can view all of the parts of the program and see the logical flow of the pipeline. In addition, open source makes an excellent learning tool for any beginning bioinformatician. Moreover, you can modify existing open source programs to deal with cutting-edge problems or to customize your pipeline. Apart from your computational and analysis work, most of the reviewer also prefers the open source based results so that they can validate the results if validation required.

2. Code headache

As a bioinformatician you are supposed to know the basics of programming languages, and if you are not good at it, then please learn it as soon as possible because you are not a bio-analyst but biological programmers. The open source programs usually lack dedicated service and support teams (often because they were the product of an overworked doc/postdoc!) so you are responsible for troubleshooting your own errors most of the time. We commonly receive the HELP email to support and assist to setup the pipeline; you can also find this kind of request on any QA forum. I personally believe this coding horror brings the biggest downside of open-source programs; where you need some programming skills in order to implement the program in your pipeline. But, if you are not able to fix the pipeline and modify the open source code according to your requirements them you should re-think on your bioinformatician name tag!!!

3. Dive into the codes

Some of the biologist turn bioinformatician says “if you can do the same thing with commercial software then why to get migraine with weird codes”, well this statement looks to me that guys are keen to learn swimming but still don’t like to get wet. If you are still using paid software and doing your work by customer support and clicking some of the well-designed GUI button then perhaps you are not interested in learning and trying new and challenging bioinformatics works. You are missing the basic flavour of bioinformatics. Let’s dive into the coding world, I am sure your will enjoy it. I recommend your to swim freely in code’s sea, and enjoy the journey; do not merely watch it from the outside.  

4. Paid does not mean better

The bioinformatics company which are specializes in bioinformatics solutions develop well designed/packed, user friendly software by using a large number of specialised scientist, programmers and support staff. They also provide good services to accomplice your biological analysis work. This means that if you hit a ‘snag’ with your data, help is likely only a phone call away! These companies price their products competitively against the cost of a dedicated bioinformatician. You may be able to afford the program, but not the additional staff! Additionally, most of the functionality that you need in your analysis is already coded into the program. Need to plot a graph? Just click this button right here. It is that easy. But, as a bioinformatician this is not generally well encouraged approach in biological analysis work, because the software is not available to everyone and your data can’t be validated. Moreover, there is very less chances that anyone will repeat your work or love to do similar kind of research (because not all the labs in the world are rich like yours).

5. Take a caution

In biological analysis work, in which you deal GB/TB of data are having maximum chances of getting errors, so please be careful and always cross check your data before coming to any conclusion. Even an error in two line code can alter your entire analysis and display weird results. Some of the scientist blindly believes on commercial software, which is entirely wrong. Using proprietary tools does not absolve you of the need to actually read and research the type of analysis that you are doing. This is particularly true in the case of genome assembly and annotation.

At the end, I would like to tell only one think that open source solutions allows you to do more cutting edge analysis than the commercial tools. So let’s go for it.

Disclaimer:

This is my personal view. I have nothing to do with any company or open source community. The views expressed on these pages are mine alone and not those of my current/past employers. I do reserve the right to remove comments left by spammers or off-topic comments.