the study of the sequence-structure relationship
(application -> structure prediction)
the study of the structure-function relationship
(application -> function prediction)

Therefore, anything related to the configuration (sequence) and conformation (structure) in atomic systems of proteins and nucleic acids, and the interaction of these with other elements (function) is of our major interest.

Lab page @ http://melolab.org/mbl/

Now, two research groups have engineered new optogenetic proteins that can be used to efficiently silence neurons. One of the two new proteins comes from the lab of Karl Deisseroth, a psychiatrist and neuroscientist at Stanford University who helped develop optogenetics as a research tool. His group’s new “off” switch for neurons was created by changing 10 of the 333 amino acids in an existing optogenetic protein, which itself had been engineered by combining natural proteins from green algae. That advance “creates a powerful tool that allows neuroscientists to apply a brake in any specific circuit with millisecond precision,” said Thomas Insel, director of the National Institute of Mental Health, in a released statement. The other new silencing protein, developed by scientists at the Humboldt University of Berlin and collaborators, was created by changing amino acids in the same existing optogenetic protein. 

Some researchers are also looking to optogenetics as a potential treatment for patients with a variety of conditions (see “For Mice, and Maybe Men, Pain Is Gone in a Flash,” and “Flipping on the Lights to Halt Seizures”) but there are huge challenges to overcome. The method requires genetic modification of cells to make them light-sensitive. It also requires implanted light sources for all but the shallowest of nerve endings.

Summary: The application of protein–protein docking in large-scale interactome analysis is a major challenge in structural bioinformatics and requires huge computing resources. In this work, we present MEGADOCK 4.0, an FFT-based docking software that makes extensive use of recent heterogeneous supercomputers and shows powerful, scalable performance of over 97% strong scaling.

Availability and Implementation: MEGADOCK 4.0 is written in C++ with OpenMPI and NVIDIA CUDA 5.0 (or later) and is freely available to all academic and non-profit users at: http://www.bi.cs.titech.ac.jp/megadock.

Contact: akiyama@cs.titech.ac.jp

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2014/08/06/bioinformatics.btu532.short

MutaBind guides you through this process, step by step, starting with selecting a protein complex and inputting PDB code or uploading PDB files. You can also retrieve results with a job ID number, view help documents, and review the MutaBind method and references.

More at http://www.ncbi.nlm.nih.gov/projects/mutabind/index.fcgi/

http://abi.inf.uni-tuebingen.de/Services/YLoc/webloc.cgi

Address of the bookmark: https://abi.inf.uni-tuebingen.de/Research/Systems%20Biology/protein-subcellular-localization

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.

For knowledge about Linux and their importance amongst bioinformatician plesae read this article "An introduction to Linux for bioinformatics" by Paul Stothard.

Linux cheat sheet at http://bioinformaticsonline.com/file/view/87/linux-cheat-sheet

Please browse for futher useful linux pages on right hand side ...

NAME OF THE POST : Bioinformatician (Part time 3 days in a week) (One Position only)

DURATION : One Year

NAME OF THE PROJECT : Next generation sequencing facility

EDUCATIONAL QUALIFICATIONS : At least a Masters degree in Bioinformatics and Bachelors degree in any stream of life sciences

REQUIREMENTS :

Around 5 years of experience and proven track record in next generation sequence data analysis (supported by publications in peer-reviewed journals), ability to analyze transcriptomics, Chip-seq, and small RNA –seq data.

: Should have the ability to analyze raw primary data generated by Illumina next generation sequencing platforms and create / troubleshoot custom analysis Pipelines.

Should have ability to handle all downstream secondary and tertiary data analysis using commercially available as well as open source softwares (transcriptomics, ChIP-seq, small RNA-seq)

Apart from these, the applicant should have knowledge of the following: Programming: Perl and Python. Operating system:

Linux and Windows. NGS Analysis tools: Maq, BWA, Bowtie, SAM tools, BEDTools, MACS, Galaxy, FastQC, Bismark, MEDIPS, Tophat, Cufflinks, AvadisNGS, CLC Genomics Workbench, Galaxy, BaseSpace, Trinity Statistics: Microsoft Excel and R. Database: MySQL Genome Browser: UCSC, Ensemble, IGV, IGB Motif Analysis Tools: MEME Suite, Transfac and RSAT Functional Annotation Tools: DAVID, GeneCodis, Gene Cards Networking Tools: Cytoscape

EMOLUMENTS : The incumbent will be paid a fee of Rs. 2000/- per sitting/ per day.

SCIENTIST NAME : Dr. Arnab Mukhopadhyay,

Staff Scientific V Next generation sequencing facility

SCIENTIST’S E-MAIL ID : arnab@nii.ac.in

WALK IN INTERVIEW ON : 18th September, 2015

REGISTRATION OF CANDIDATES: 10.30 AM to 11.00 AM

PLEASE NOTE- 1. CANDIDATE MAY FILL UP APPLICATION IN THE PRECRIBED FORMAT ALONG WITH NECESSARY DOCUMENTS FOR VERIFICATION. 2. APPLICATIONS CONTAINING INCOMPLETE INFORMATION SHALL NOT BE ENTERTAINED. 3. DATE OF PASSING THE EXAMINATIONS MUST BE INDICATED CLEARLY. 4. ONLY REGISTERED CANDIDATES WILL BE INTERVIEWED. 5. NO TA/DA WILL BE PAID FOR ATTENDING THE INTERVIEW PRESCRIBED FORM 1. NAME 2. FATHER’S NAME 3. MOTHER’S NAME 4. DATE OF BIRTH 5. SEX (MALE/FEMALE) 6. CATEGORY (SC/ ST/ OBC/ PH) 7. ADDRESS a. (CORRSPONDENCE) b. (PERMANENT) 8. E MAIL, TELEPHONE NO. & MOBILE No (if any) 9. ACADEMIC & PROFESSIONAL QUALIFICATIONS NAME OF EXAMINATION PASSED WITH SUBJECTS YEAR OF PASSING BOARD/ UNIVERSITY PERCENTAGE/ DIVISION REMARKS 10. PAST EXPERIENCE & PRESENT EMPLOYMENT, IF ANY 11. CANDIDATES SHOULD STATE CLEARLY WHETHER THEY HAVE BEEN AWARDED PH.D DEGREE OR THESIS HAS BEEN SUBMITTED. 12. HAVE YOU APPLIED FOR A POSITION EARLIER IN THE INSTITUTE? IF SO:- (1) THE DETAILS OF THE PROJECT AND PROJECT INVESTIGATOR (2) IF CALLED FOR INVERVIEW, RESULTS THEREOF

More at http://www1.nii.res.in/sites/default/files/walkininterview-18sept2015.pdf

The Laboratory of Statistics and Computational tools for Bioinformatics (BioinfoLab) is hosted at the Istituto per le Applicazioni del Calcolo "Mauro Picone" - CNR . The laboratory has been officially opened in 2012 with the support of Programma Operativo Nazionale "Ricerca e Competitività" 2007-2013 (PON "R&C"), and it incorporates several expertise and research activities started since 2007, and supported by several CNR projects. Main interest of BioinfoLab is to develop novel statistical methods and computational tools for the analysis of high dimensional data arising from "Multi-omics" applications. In particular, current activities involve the analysis of ChIP-seq and RNA-seq experiments.

More at http://bioinfo.na.iac.cnr.it/BioinfoLab/index.html