Pay scale: Pay Band of Rs. 37400-67000 with AGP of Rs. 9000.

i. Educational Qualification: Good academic record with a Ph.D. Degree in the concerned/allied/relevant disciplines.

ii. A Master's Degree with at least 55% marks (or an equivalent grade in a point scale wherever grading system is followed).

iii. A minimum of eight years of experience of teaching and/or research in an academic/research position equivalent to that of Assistant Professor in a University, College or Accredited Research Institution/industry excluding the period of Ph.D. research with evidence of published work and a minimum of 5 publications as books and/or research/policy papers.

iv. Contribution to educational innovation, design of new curricula and courses, and technology - mediated teaching learning process with evidence of having guided doctoral candidates and research students.

v. A minimum score as stipulated in the Academic Performance Indicator (API) based Performance Based Appraisal System (PBAS), set out in UGC Regulation.

Download application form from website: http://www.allduniv.ac.in/

Send your application to the Registrar, University of Allahabad, Allahabad-211002 (U.P.) on or before 30th April 2014

For more details: http://www.allduniv.ac.in/images/adv/backlog/advt-details.pdf OR http://www.allduniv.ac.in/images/news/extension-notice.pdf

Last Apply Date: 30 May 2014

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.

That is the challenge researchers face when studying how bacteria adapt to different environments. A bacterial gene may appear to be linked to a particular habitat, when the real reason is simply that closely related bacteria happen to live there.

In their 2025 Genome Biology paper, Bujdoš, Walter, and O’Toole introduce aurora, a machine-learning tool designed to tackle this problem.

Aurora identifies potentially mislabeled or unusual bacterial strains before performing genome-wide association studies (GWAS). By cleaning up the dataset and accounting for bacterial evolutionary relationships, it can help researchers find genetic features that are more genuinely connected to habitat adaptation.

The researchers tested aurora using simulated and real bacterial datasets and found that it could recover important genetic associations even when datasets contained misleading labels.

The bigger lesson is simple: better biological discoveries often begin with better data.

Aurora gives researchers a new way to separate real genetic clues from misleading ones—and could help us better understand how bacteria adapt, survive, and evolve in the environments they call home.

Based on Bujdoš et al., “aurora: a machine learning GWAS tool for analyzing microbial habitat adaptation,” Genome Biology (2025).[Read the original paper](https://link.springer.com/article/10.1186/s13059-025-03524-7?)

1. Cancer Genomics

2. Microbial Genetics and Metagenomics

3. Human Infective Diseases

4. Computational Drug Design

Interested candidates may apply to Dr. Ranjith N. Kumavath, Assistant Professor & Head, Department of Genomic Science, School of Biological Sciences, Central University of Kerala, Padannakad (PO), Nileshwar, Kasaragod-671328,Kerala. Email: RNkumavath@gmail.com

Research Directions

Stony corals: evolution of novelty and photosymbiosis

Symbiotic acoels: cell type evolution and photosymbiosis

Animal genomes: structural evolution and gene regulation

https://sgel.biodiv.tw/home

The tutorial is divided in three main parts:

• In the Sequence part, you will see how to look efficiently for a particular protein sequence, how to blast it against the database of your choice to find homologues, how to perform a multiple alignment of the homologues you've selected and how to edit this alignment.
• The Structure part is about molecular visualization, homology modeling and structural domain prediction.
• In the Function part, you will be introduced to you 3 useful servers to investigate the function of a protein. i.e. finding interactors, co-expressed genes, see a phylogenetic profile, easily access papers citing your gene etc ...

During all the three parts, we will use the S. cerevisiae VPS36 protein as an example.

Address of the bookmark: http://www.mrc-lmb.cam.ac.uk/rlw/text/bioinfo_tuto/introduction.html

Applications are invited for the position of Senior Research Fellow for the following time-bound sponsored project as per the details given below:

1. BTIS project on, “Bioinformatics Center-National Infrastructural Facility in the Area of Immunology” funded by DBT

Senior Research Fellow (P) (One Position only)

Dr. Debasisa Mohanty
Staff Scientist-VI
deb@nii.res.in

Qualifications: M.Sc in Biological Sciences or Biotechnology with at least 04 years of Research experience in Bioinformatics or computational Biology after the master’s degree is essential.

Emoluments: The selected candidates will draw consolidated emoluments as per Institute Rules, depending upon qualifications & experience

Rs. 18,000/- per month consolidated plus 30% HRA if Leading to Ph.D/NET/GATE Qualified otherwise Rs. 14,000/- per month + 30% HRA.

Job description: The candidate should be well versed in programming in PERL/C++/HTML/CGI, web server and portal development, computational analysis of
protein structure & function, molecular dynamics simulations and use of high performance computing systems.

GENERAL TERMS AND CONDITIONS:-

1. The candidates selected for the above posts will be on contract for one year or duration of the project whichever is shorter, at a time.
2. No hostel/ housing facility will be provided.
3. Number of posts may vary and shall be need based. Advertisement is no commitment.
4. Applicants may clearly mention the category they belong to i.e. SC/ST/OBC/PH and attach documentary proof of the same.
5. No TA/DA will be paid for attending the interview, if called for.
6. Apart from sending application in the prescribed format given below, candidates should send complete Curriculum Vitae along with the names of three referees. Curriculum Vitae should contain details of the experimental expertise.

HOW TO APPLY Interested candidates may apply directly, STRICTLY IN THE PRESCRIBED FORMAT GIVEN BELOW, through e-mail, to the Investigator of the project, clearly indicating the name of the project along with their complete C.V., e-mail id, fax numbers, telephone numbers. Only Short listed candidates will be called for interview and they required to submit attested copies of all their certificates and a Demand Draft of Rs 100/- drawn on Canara Bank or Indian Bank payable at Delhi/New Delhi in favour of the Director, NII (SC / ST and PH candidates are exempted subject to submission of documentary proof), at the time of interview.

LAST DATE OF RECEIPT OF APPLICATIONS: 06th June, 2014

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www1.nii.res.in/sites/default/files/projectappointment-Dr.Mohanty-6June2014.pdf

Candidates should have a 1st class MSc/MTech/BTech degree in Bioinformatics. Please send complete CV, quoting Application for RMETH-JRF-2014, by email to Dr. Dinesh Gupta: dinesh@icgeb.res.in

Closing date for applications: 6 August 2014

More at http://www.icgeb.org/tl_files/Vacancies/JRF.pdf

sort a space-delimited file based on its first column, then the second if the first is the same, and so on:
sort input.txt

sort a huge file (GNU sort ONLY):
sort -S 1500M -t $HOME/tmp input.txt > sorted.txt

sort starting from the third column, skipping the first two columns:
sort +2 input.txt

sort the second column as numbers, descending order; if identical, sort the 3rd as strings, ascending order:
sort -k2,2nr -k3,3 input.txt

sort starting from the 4th character at column 2, as numbers:
sort -k2.4n input.txt

More Linxu sort command information

If you have any sort commands you'd like to share, please add them to our comments section below. For more help, you can also type:

man sort

or

sort --help

on your Unix/Linux system.