Experience Required: 3-5 years of experience
No of Positions : Multiple
Qualifications: Candidates with minimum qualification as M.Sc Bioinformatics with 3-5 years of experience in Life sciences R&D or Pharma Industry.
Ph.D candidates with research experience in Bioinformatics with publications in International journal and minimum 2 years of industry experience in clinical genomics will be preferred for this position.

Requirement:

1. Must have basic understanding of molecular biology and Genomics.
2. Experience in application development or must have expertise in programming using either of Perl/Python.
3. Experience in statistical programming using R/Bioconductor/Matlab.
4. Strong concept in statistical and mathematical modelling.
5. Experience in designing and developing the bioinformatics pipeline.
6. Must have minimum 2+ years of hands on experience in NSG data analysis such as RNA-Seq,Exome-Seq ,Chip-Seq and downstream analysis.
7. Knowledge in WGS ,WES, Targeted re-sequencing,GWAS and population genomics will be preferred.
8. Must have experience working on opensource software/Framework and commercial software for NGS data analysis and reporting.
9. Should be aware of handling big data and guiding team members on multiple projects simultaneously.
More at http://www.ocimumbio.com/careers1/
10. Should have experience coordinating with different groups of clinical research scientist for various project requirements.
11. Ability to work as team as well as independently with minimal support.

More at http://www.bioinf.uni-leipzig.de/Software/segemehl/

Manual http://www.bioinf.uni-leipzig.de/Software/segemehl/segemehl_manual_0_1_7.pdf

Address of the bookmark: http://hoffmann.bioinf.uni-leipzig.de/LIFE/segemehl.html

More at https://www.youtube.com/watch?v=vdGuy1GAJrQ

Address of the bookmark: https://sourceforge.net/projects/accnet/

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/

Cleaning your data in this way is often required: Reads from small-RNA sequencing contain the 3’ sequencing adapter because the read is longer than the molecule that is sequenced. Amplicon reads start with a primer sequence. Poly-A tails are useful for pulling out RNA from your sample, but often you don’t want them to be in your reads.

Cutadapt helps with these trimming tasks by finding the adapter or primer sequences in an error-tolerant way. It can also modify and filter reads in various ways. Adapter sequences can contain IUPAC wildcard characters. Also, paired-end reads and even colorspace data is supported. If you want, you can also just demultiplex your input data, without removing adapter sequences at all.

Cutadapt comes with an extensive suite of automated tests and is available under the terms of the MIT license.

If you use cutadapt, please cite DOI:10.14806/ej.17.1.200 .

Address of the bookmark: https://cutadapt.readthedocs.io/en/stable/installation.html#quickstart

ORFfinder is a graphical analysis tool for finding open reading frames (ORFs). We’ve been working on a few updates, and we’d like to find out what you think about them. Read on to find out what you can do with the new ORFfinder.

Smart BLAST (https://ncbiinsights.ncbi.nlm.nih.gov/2015/07/29/smartblast/)

Select one or a group of ORFs and BLAST several databases at once, and use the newly developed SmartBLAST to verify protein names. Looking for the traditional results from BLAST? They’re there too.

More at https://bitbucket.org/henryhzhang/rcircos/src

Address of the bookmark: https://bitbucket.org/henryhzhang/rcircos/src

http://biogps.org/#goto=welcome

Address of the bookmark: http://biogps.org/#goto=welcome