• Using state of the art tools, easily extended for other viruses

• Tool and database updates for critical components via Conda

• Built using modern design patterns with Conda and Snakemake

• Extensible and easy to customize

• Submission Ready Genomes

• Customizable reporting with comprehensive visualization

https://ikim-essen.github.io/uncovar/

Github https://github.com/IKIM-Essen/uncovar

Address of the bookmark: https://ikim-essen.github.io/uncovar/

Essential qualifications: First class M. Sc. in any branch of life sciences and qualified CSIR-UGC NET/GATE Examination.

Desirable qualifications: Practical experience in biochemical and biophysical studies of proteins

Emoluments: as per DBT norms

The project is tenable for two years, initially for one year.

Age: Below 28 years (relaxable in the case of SC/ST/OBC/women candidates)

Candidates are requested to bring two sets of complete applications on plain paper furnishing bio-data and copies of attested certificates along with originals (for verification) on the date of interview.

No TA/DA is admissible for candidates appearing at the interview.

Dr. Rajat Banerjee
Assistant Professor
Department of Biotechnology and
Dr. B. C. Guha Centre for Genetic Engineering and Biotechnology
University College of Science
35, Ballygunge Circular Road
Kolkata 700019

Advertisement: www.caluniv.ac.in/news/jrf_biotech_2.pdf

The Power of Evo 2: AI Meets DNA

Evo 2 is the largest AI model for biology ever created, trained on an astonishing 9.3 trillion DNA "letters" (nucleotides) carefully selected from genomes spanning the entire tree of life. This massive dataset ensures that Evo 2 can recognize patterns and relationships in genetic sequences at an unparalleled scale.

For the first time, scientists can design DNA with AI, moving beyond simple sequence analysis to active DNA generation. Evo 2 enables researchers to predict, modify, and even create entire genetic sequences, opening new possibilities in medicine, agriculture, and synthetic biology.

Decoding the Dark Genome

One of the biggest challenges in genetics is understanding the non-coding regions of DNA—vast stretches of the genome that do not code for proteins but play crucial roles in regulating gene expression. These regions control when and how genes are activated, influencing everything from development to disease.

Evo 2 is designed to decode these non-coding elements, helping researchers uncover their functions and use this knowledge to develop gene-based therapies, synthetic life forms, and precision agriculture solutions.

From Reading DNA to Writing It

To put Evo 2’s impact into perspective:

• Previous AI models could "read" DNA like a book, analyzing genetic sequences and identifying patterns.
• Evo 2 can "write" entirely new DNA, designing functional genes, chromosomes, and even full genomes from scratch.

This means scientists can now engineer biological systems with AI, designing new proteins, metabolic pathways, and genetic circuits to address real-world challenges.

A Step Toward Generative Biology

The Arc Institute describes Evo 2 as a major step toward "generative biology"—a revolutionary approach where AI is used to create novel biological structures rather than just analyzing existing ones. This could lead to breakthroughs such as:

• New medicines: AI-generated enzymes and proteins tailored for targeted therapies.
• Disease-resistant crops: Genetically optimized plants for higher yield and climate resilience.
• Synthetic organisms: Custom-designed microbes for bioremediation, biofuel production, and industrial applications.

An Open-Source Revolution

Unlike many proprietary AI models, Evo 2 is open source, making its capabilities accessible to researchers worldwide. This democratization of AI-driven biology means that scientists from different disciplines can collaborate, experiment, and innovate, accelerating discoveries in genetic engineering and synthetic biology.

With Evo 2, the boundaries of what’s possible in DNA design, genetic engineering, and biological innovation are being redrawn. The future of life sciences is no longer just about understanding life’s code—it’s about writing it.

Required Skills: no special skills required for this job post
Required Experience:
Experience in computer aided drug design and or biochemical testing of natural or synthetic compounds is desired
Required Education:
M.Sc. / M.Tech.

Required Job Profile:
Candidate must possess M.Sc. in bioinformatics or computational biology or biotechnology or any branch of life sciences or pharmacology or chemical sciences or M.Tech. in any branch of life sciences with at least fifty five percent marks with NET or GATE.

Desired Job Profile:
Candidate having experience in computer aided drug design and or biochemical testing of natural or synthetic compounds.

How to apply:
Eligible and interested candidates should need to appear for walk-in interview on 21.11.2014 at 1700 hrs at the above mentioned address.

Contact
Pondicherry University
Dr. S. Mohane Coumar, Assistant Professor & Project Investigator, Centre for Bioinformatics, Pondicherry University, Puducherry 605 014
Email:registrar@pondiuni.edu.in
Phone: 0413-2655175

More at http://www.pondiuni.edu.in/sites/default/files/JRF-bioinfor-041114.pdf

Address of the bookmark: http://disco.omicsbio.org/

Applications are invited for the AiCADD fellowship (MHRD, Govt. of India) of University of Kerala.

The terms and conditions of the fellowship is given below:

Ø The AiCADD PhD Fellowship scheme will be available for the students registered for full-time research or intending to register and pursue full time research at SIUCEB in frontier areas of bioinformatics, computational biology, systems biology and closely allied areas with focus on Ayur-Informatics.

Ø The fellowships will be widely announced and open to students irrespective of geographical consideration.

Ø Candidates availing of this fellowship shall not be in receipt of any other fellowships concurrently.

Ø Researchers will be selected on the basis of research aptitude test and personal interview.

Ø Each selected student will be eligible for a monthly fellowship of Rs. 10,000/- for the 1st and 2nd year and Rs. 12,000/- for the 3rd year.

Ø Candidates must register for PhD within one year of joining, failing which the fellowship will have to be remitted back.

Ø Candidates receiving the fellowship shall submit bi-annual reports of progress and the continuation of the fellowship will be based on the evaluation of the same.

Ø Candidates are also required to take up academic duties including teaching upto a maximum of 6 hours per week, as directed by AiCADD Principal Investigator.

Interested candidates may please forward their application along with resume on or before 15th November 2014 in the following address. Principal Investigator, AiCADD Centre, Dept. of Computational Biology and Bioinformatics, University of Kerala, Thiruvananthapuram - 695581.

More at https://sites.google.com/site/centreforbioinformatics/announcements

This bookmark is created to provide NGS online books links.

Address of the bookmark: http://en.wikibooks.org/wiki/Next_Generation_Sequencing_%28NGS%29/Print_version

The work in our lab has focussed on computational approaches to speed-up the finishing process. Specifically, we have explored the use of optical mapping and mate-pair data to augment assemblies and direct finishing experiments. The tools developed in our lab have been used in several finishing projects, producing complete genomes (and near-complete ones) with surprisingly little computational and experimental effort (Nagarajan et al., in submission). The executables (as well as source code) for these tools are freely available here:

• Scaffolding using Optical Restriction Mapping
  Optical Maps are global, ordered maps of restriction site locations in a genome. This information can be quite useful in scaffolding contigs from a shotgun assembly to guide the finishing process. A set of programs to exploit optical maps for assembly can be found here: SOMA v2.0 (63 MB tar.gz file). This version of SOMA contains several improvements to programs in v1.0 as well as new scripts for working with multiple maps, contig graphs and scaffolds. 
  
  
• Augmenting assemblies with mate-pair data
  Mate-pair information can be valuable in augmenting short-read assemblies and reconstructing the genome as larger scaffolds. AMOS-Hybrid is a pipeline written in the AMOS framework (open-source assembly tools) to merge arbitrary mated reads into an existing assembly and merge contigs and create scaffolds where possible. Source code and executables for AMOS-Hybrid are available here: AMOS-Hybrid v1.0 (142 MB tar.gz file). 
  
  
• Assembly and sequence-composition guided finishing
  Contigs from a shotgun assembly are typically linked together in a graph structure that can serve to guide finishing and in some case close gaps in-silico. Also, in many cases, sequence composition of contigs can provide clues to fill gaps in scaffolds. A set of scripts to automate some of these tasks can be found here: Finishing Scripts v1.0 (63 MB tar.gz file). 

http://www.cbcb.umd.edu/finishing/

Address of the bookmark: http://www.cbcb.umd.edu/finishing/

Qualification: Highly motivated M.Sc. (Bioinformatics/ Biotechnology/ Life Sciences/ Botany/ Agriculture) students are encouraged to apply. Prior experience in Bioinformatics/Plant tissue culture work is preferable. Preferences would be given to DBT/ CSIR / UGC NET qualified students.

Application Procedure: A detailed CV indicating name, date of birth, address, contact number, e-mail address, educational qualifications, NET qualified or not, research experiences if any, should be e-mailed to This email address is being protected from spambots. You need JavaScript enabled to view it. on or before 24th December 2014.

Important Note: Only short listed candidates will be called for interview at Akbar Bhawan, Chanakyapuri, New Delhi. No TA/DA will be paid for attending the interview. SAU Selection Committee reserves the rights to relax any of the qualifications in case the candidate is found otherwise well qualified. The above- mentioned post is temporary and will be initially offered for a period of one year, which can be extended to one more year.

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