• 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/

Details will be available at: http://nrcorchids.nic.in/Employments/Vacancy%20-%20JRF.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.

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?)

PO Box: 21

DHARAMSHALA, DISTRICT KANGRA, HIMACHAL PRADESH – 176215

EMPLOYMENT NOTICE NO.: 02 / 2015

APPOINTMENT TO VARIOUS TEACHING, NON-TEACHING AND OTHER ACADEMIC STAFF POSITIONS

Applications in the prescribed form are invited from the eligible candidates for the following Teaching, Non-Teaching and other Academic Staff positions to be filled up on regular basis: Details of teaching positions:

15. School of Life Sciences

Computational Biology & Bioinformatics

1 (ST - 1) 2 (UR - 2)

Last Date of receipt of applications: 22ND JUNE, 2015

Advertisement:

http://www.cuhimachal.ac.in/download/2015/may-2015/emp-notice-eng/1.%20Employment%20Notice%20No.%2002-2015%20dated%2019.05.2015_for%20Website.pdf

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

Minimum qualifications: M.Sc. with minimum 55% for general and OBD Category (55% for SC/St/PH) in the above-mentioned subject areas (viz. Biotechnology, Life Sciences, Microbiology, Botany, Plant Sciences, Chemistry, Zoology, Animal Sciences, Fishery Sciences and any other relevant branches).

Preference will be given to those holding valid CSIR-UGC NET JRF. DBT-JRF, ICAR-JRF, ICMR-JRF and DST-INSPIRE Fellowship while NET/SLET/SET qualified and GATE qualified candidates (90 or above percentile) are also encouraged to apply. Reservations of seats: 15% for SC, 7.5% for ST, 27% for OBC (noncreamy layer) and 3% for Physically Handicapped as per statutory norms.

Selection Procedure: If the number of JRF and INSPIRE qualified candidates is more, selection will be based on interview of the JRF and INSPIRE qualified candidates only. The selected candidates may be registered for Ph.D. in any of the recognized Universities in India.

Application Procedure: Application should be sent in the prescribed application form (available on the IBSD website). The candidate should send the completed and signed form along with self attested copies of all supporting certificates and marksheets along with an application fee of Rs.300/- (For GEN/OBC/PH) & Rs.150/- for (SC/ST), for which a Demand Draft in favour of ‘Institute of Bioresources and Sustainable Development, payable at Imphal, Manipur, should be attached with the application form. Candidates are advised to provide their email ID and mobile number as they would be contacted electronically by the Institute. Duly filled applications (with ‘Application for IBSD PhD Programme’ super scribed on the envelope) should be sent to ‘The Director, Institute of Bioresources and Sustainable Development, Takyelpat, Imphal-795001, Manipur so as to reach on or before 6th of July, 2015. Applications send by email with scan copy of required enclosures will also be accepted and can be sent to director.ibsd@nic.in. However, in such instances, the application will be processed only after the receipt of the mailed hard copies.

Advertisement: http://ibsd.gov.in/jobs/phd_2015/IBSD_JRF_2015.pdf

Application Form : http://ibsd.gov.in/jobs/phd_2015/APPLICATION_FORM.pdf

Is this available, or still under trial mode? 

https://www.nanoporetech.com/

https://www.nanoporetech.com/technology/the-minion-device-a-miniaturised-sensing-system/the-minion-device-a-miniaturised-sensing-system

Bioinformatics Textbook Track

Find more about Rosalind puzzle at http://rosalind.info/problems/list-view/?location=bioinformatics-textbook-track

I will provide solution of all the Rosalind problem with Perl for community.

Check out the right sidebar for more links ...

Our main advantages over other similar tools are:

• GeneAnalytics is very simple and intuitive to use.
• GeneAnalytics is based on our proprietary databases – GeneCards, MalaCards, PathCards and LifeMap Discovery, each of them integrates information from a very large number of resources.
• GeneAnalytics supplies links for extensive background information on each of the matched results.

I invite you to try it out for free at geneanalytics.genecards.org, and would be happy to hear your comments and thoughts on how we can improve.

Yours,

Shani Ben-Ari Fuchs

LifeMap Sciences Team