No of Vacancies: 01
Pay Scale: Rs. 9300-34800 + Grade Pay Rs. 4600/-

Educational Qualification and Experience: (From Recognized University / Institute) M.Sc. in Biotechnology, Molecular Biology, Human Genomics / Biochemistry / Biophysics.

Age Limit: 18-50 years

Please see Detailed Advertisement Link for full info: https://drive.google.com/file/d/0Bz3xO6e_7OeeZllINnRyWlN5UFE/view

· Enzyme immobilization supports for select enzyme systems.

· New tools for microbial strain and genome engineering using
state-of-the-art methodologies.

· Rapid high throughput assays to screen microorganisms from various
sources for enzymatic activities of interest.

· High throughput combinatorial approaches to the formulation of growth
media in support of microbial enrichments, strain isolations and growth
optimization.

· Meta-transcriptomics for the discovery of new enzymes.

· Strain adaptation techniques in defined chemostat environments for
microbial strain development.

The internships are based at the Experimental Station R&D Center in
Wilmington, DE.

If interested, apply fast!

For more information and to apply, go to:

http://careers.dupont.com/jobsearch/job-details/industrial-biosciences-summer-internship/008549W-10/

SLURM is similar in many ways to most other queue systems. You write a batch script then submit it to the queue manager. The queue manager then schedules your job to run on the queue (or partition in SLURM parlance) that you designate. Below we will provide an outline of how to submit jobs to SLURM, how SLURM decides when to schedule your job and how to monitor progress.

SLURM has a number of valuable features compared to other job management systems:

• Kill and Requeue SLURM’s ability to kill and requeue is superior to that of other systems. It waits for jobs to be cleared before scheduling the high priority job. It also does kill and requeue on memory rather than just on core count.
• Memory Memory requests are sacrosanct in SLURM. Thus the amount of memory you request at run time is guaranteed to be there. No one can infringe on that memory space and you cannot exceed the amount of memory that you request.
• Accounting Tools SLURM has a back end database which stores historical information about the cluster. This information can be queried by the users who are curious about how much resources they have used.

Summary of SLURM commands

The table below shows a summary of SLURM commands. These commands are described in more detail below along with links to the SLURM doc site.

Address of the bookmark: https://github.com/dentearl/mafTools

Essential Qualification:

1. Master's degree in Bioinformatics / Computer Applications / Cognitive Science /Neuroscience/ Neuropsychology or equivalent. (Advantage will be given to NET JRF qualified candidate) Additional

Desirable Skills:

1) Programming skills (e.g., C++, Matlab, Python) 2) Experience/competent in working Window and Linux based programme.

Please mail your CV and covering letter to dr.uttam.kumar@gmail.com.

To know more about lab works please visit http://uttambrainlab.co.in/lab/.

Last date: 15/06/16

Advertisement: http://cbmr.res.in/wp-content/uploads/2016/05/Advertisement-19-5-2016.pdf

Pay Scale : Rs. 32,500/-
How to apply
Candidates fulfilling these requirements should pre-register by sending their application in the prescribed format with recent CV and contact details of 2 referees by e-mail to ‘program.office@actrec.gov.in’ latest by 17.00 hrs on 23-06-2016. The interviews would be held on 27-06-2016 and only pre-registered candidates will be eligible to appear for interview. Candidates should report between 09.30 to 10.00 a.m. in Steno Pool, 3rd floor, Khanolkar Shodhika, ACTREC, Kharghar, Navi Mumbai.

More at http://www.actrec.gov.in/

Blobsplorer is unlikely to be of use on its own as it requires contig data to be supplied in a format that involves considerable preprocessing (see below for a description). The easiest way to use Blobsplorer is as part of a workflow using scripts from here.

Address of the bookmark: http://nematodes.org/martin/blobsplorer/blobsplorer.html

LPI scores are inversely proportional to the phylogenetic distance between database match sequences and the query genome. These scores are useful not only for large-scalede novo predictions of horizontally transferred proteins, but can also serve as an independent quality control test for potential horizontal transfer candidates identified by alternative methods, especially those based on nucleic acid signatures. Candidates having high LPI scores are unlikely to have been horizontally transferred, since they are highly conserved among closely related organisms.

One unique and powerful feature of the DarkHorse HGT Candidate database is the opportunity to explore the phylogenetic background of potential HGT donors as well as recipients. The breadth of the database allows not only query sequences, but also their database match partners to be evaluated for sequence similarity or novelty compared to taxonomically related organisms.

DarkHorse is configurable for varying degrees of phylogenetic granularity and protein sequence conservation. Users should consult the references cited below for a complete explanation of parameter selection and result interpretation. A brief tutorial page is also available on-line.

Address of the bookmark: http://darkhorse.ucsd.edu/download.html

We developed a novel, self-contained technique named Near HGT, based on the synteny index, to measure the divergence of a gene from its native genomic environment and used it to identify candidate HGT events between closely related strains. The method confirms candidate transferred genes based on the constant relative mutability (CRM). Using CRM, the algorithm assigns a confidence score based on “unusual” sequence divergence. A gene exhibiting exceptional deviations according to both synteny and mutability criteria, is considered a validated HGT product. We first employed the technique to a set of three E. coli strains and detected several highly probable horizontally acquired genes. We then compared the method to existing HGT detection tools using a larger strain data set.

When combined with additional approaches our new algorithm provides richer picture and brings us closer to the goal of detecting all newly acquired genes in a particular strain.

Availability: The method is publicly available athttp://research.haifa.ac.il/~ssagi/software/nearHGT.zip

Address of the bookmark: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004408