Script are moved to http://bioinformaticsonline.com/snippets/view/34633/clump-finding-problem-solved-with-perl

Address of the bookmark: https://github.com/sc932/ALE

To find repeats in a genome from 2 to 9 length using a Perl script, you can use the RepeatMasker tool with the "--length" option[0]. Here's a step-by-step guide:

Qualifications : Ph. D.

Desirable : Experience on DNA Markers, plant genome mapping and bioinformatics

No. of Post : 03

Department : Genetics

Salary : Rs. 60,000/-
How to apply

The applicants are requested to register their names on the day of interview in the First Floor, Biotech Centre, Centre for Genetic Manipulation of Crop Plants, Department of Genetics before the stipulated time for the interview. Only the registered eligible candidates will be interviewed on the day in the Committee Room. Applicants are requested to bring all related documents, in original and a set of photocopy, for verification. Date and time of the interview : 25.06.2015 at 10:30 AM.

Click Here for Job Details http://www.du.ac.in/du/index.php?mact=News,cntnt01,detail,0&cntnt01articleid=5492&cntnt01returnid=83

• 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/

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.

Applications are invited from individuals who have high motivation to do research for the below mentioned position,

1. Research Associate - 1 No.
in the DBT sponsored project under the supervision of Dr. D. Prabaharan, Principal Investigator, National Facility for Marine Cyanobacteria, Dept. of Marine Biotechnology, Bharathidasan University, Tiruchirappalli-24.

Title of the Project: “Establishment of National Repository for Micro algae & Cyanobacteria” funded by Department of Biotechnology, Govt. of India

Qualification

1. Research Associate – 1 No. Rs. 36,000/38,000/40,000 per month for I, II and III year + 20% HRA

Essential : Doctoral degree in relevant subject from recognized University/ Institutes
Desirable: Research experience in molecular biology and bioinformatics

Interested candidates can send their complete CV in plain paper with a passport size photograph, with details of Marks secured in all subjects from plus two stage (with proof, full postal address, sex, date of birth, community etc., along with additional qualification or experiences and two address of references whom could be contacted (One of whom should be PG teacher/guide). Application should reach the Principal Investigator on or before 30.06.2015 by Email (nfmcbic@yahoo.com)/Registered post/ Speed post, with subject subscribed as “Application for Research Associate /Technical Assistant /Lab attendant”. Qualifying candidates will be short listed and communicated with date of interview. No TA and DA will be given for attending the interview. Addressfor Communication Dr. D. Prabaharan Principal Investigator National Facility for Marine Cyanobacteria Bharathidasan University Tiruchirappalli-620024, Tamil Nadu.

Advertisement: http://www.bdu.ac.in/adv/NFMC_Project_Positions.pdf

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?)

Current research interests include next-generation DNA sequencing, structural variation, genome rearrangements in cancer and evolution, and network analysis of somatic mutations in cancer. Earlier research included topics in comparative genomics, multiple sequence alignment, and motif finding.

More athttp://compbio.cs.brown.edu/