BOL: Related items

DIYA: a bacterial annotation pipeline for any genomics lab

Jit — Fri, 30 Jun 2017 08:48:26 -0500

DIY Genomics is an open source bioinformatics consortium intended to bring a collection of tools and libraries into the hands of small scale genomics labs for the process of sequence assembly and annotation. Projects include DIYA, MGAP, CRISPR, and DIYGV

http://gmod.org/wiki/Diya

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

ShendureLab

Thu, 28 Dec 2017 09:57:50 -0600

The mission of our lab is to develop and apply new technologies and methods for genetics, genomics and molecular biology. Most of our work exploits next-generation DNA sequencing which is effectively emerging as a broadly enabling microscope for the measurement of biological phenomena. Our ongoing work generally falls into six areas. These are listed below as links to representative publications in each area.

Developing New Molecular Methods

Genomic Approaches to Developmental Biology

Massively Parallel Functional Genomics

Translating Genomics to the Clinic

Genetic Basis of Human Disease

Genome Sequencing Technologies

http://krishna.gs.washington.edu/index.html
http://www.gs.washington.edu/faculty/shendure.htm

DEPARTMENT OF GENETICS, GENOMICS AND BIOINFORMATICS, National Biotechnology Development Agency, Nigeria

BioStar — Wed, 05 Sep 2018 10:48:25 -0500

The Genetics, Genomics & Bioinformatics Department (GBBD) at NABDA is unique, encompassing all facets of modern genetics and bioinformatics research. Trans-disciplinary research being conducted in our laboratories would lead to cures for human diseases; improvements to crop and livestock quality and yield; creation of new technologies with applications to medicine; agriculture; environment; and industry.

Our capacity building activities covers both general and specialized topics in translational genetics, and is designed to better acquaint scientists and clinicians with the tools and technologies of genetics and genomics.

OUR RESEARCH ACTIVITIES INCLUDE:

Biomedical Genetics: investigating genetic and environmental factors contributing to phenotypes with relevance to human health and disease.
Computation and Bioinformatics: develop new approaches for the management, analysis, and modelling of large, complex data sets.
Population and Quantitative Genetics: study of how genetic processes evolve to generate genetic variation in populations of organisms, and the effects on the patterning of variation within and between populations and specie, and
Genetic Engineering and Biotechnology: focuses on the research and innovation for industrial enzymes, biologics and biosimilars production.

https://www.h3abionet.org/nabda

Address of the bookmark: http://www.nabda.gov.ng/departments/genetics-genomics-and-bioinformatics

PLANT COMPUTATIONAL GENOMICS LAB – JILL WEGRZYN

Thu, 20 Aug 2020 19:49:12 -0500

Our research focuses on the computational analysis of genomic and transcriptomic sequences from non-model plant species. We do this by developing approaches to examine gene finding, gene expression, transcriptome assembly, and conserved element identification, through machine learning and computational statistics. We use these novel methods to address questions related to genome biology and population genomics.

We also develop web-based applications that integrate data across domains to facilitate the forest geneticist or ecologist’s ability to analyze, share, and visualize their data. Such integration requires the implementation of semantic technologies and ontologies to connect genotype, phenotype, and environmental data.

http://plantcompgenomics.com/

Scientist C - Non-Medical (IT Expert- Computer Professional/Genomics/Bioinformatic) at NIMR

Mon, 01 Feb 2021 13:54:06 -0600

Applications are invited upto 12th February 2021 in the prescribed format (available on the websites of ICMR-NIMR) through link http://onlineapply.nimr.org.in/ up to 05:00 PM on 12th February 2021 for the following post on contract basis at NIMR, Sector-8, Dwarka, New Delhi.

Scientist C - Non-Medical (IT Expert- Computer Professional/Genomics/Bioinformatic)No. of posts: 01 (UR)

Salary (Fixed): Rs.51,000/- + HRA

Essential Qualification: Candidate should possess 1st class master degree in relevant subjects from a recognized university with 4 years experience
OR
2nd class M.Sc + Ph.D degree in relevant subjects from a recognized university with 4 years experience.Desirable Qualification: Candidates should possess a PhD degree in any field of science.
Preference will be given to those who have published scientific papers in international journals and who have a track record of working in infectious diseases.

The candidate must know the following for further consideration: (a) data processing and analysis using statistical softwares, (d) programming, (e) presentation of complex data from excel files and related skills.
Understanding of GIS and malaria will be an advantage. Experience and interest in functional genomics and genomic sequencing will be important.

Age Limit: 40 YearsDuration: 30.09.2021

More at http://onlineapply.nimr.org.in/

Genomics India Conference 2024 !

Fri, 27 Oct 2023 05:48:11 -0500

Genomics India Conference is back and this time we are coming to Shiv
Nadar Intitution of Eminenece, Delhi NCR. GIC 2024 will be held from 1st
to 3rd of February 2024 and we are happy to send you an early invitation
for India's premier genomics conference.

GIC2024 focuses on "Advances In Genomics From AI-ML To Targeted
Therapies". GIC2024 encourages researchers to present original
contributions for poster presentations.

Note: Early bird registration closes on 1st December 2023.

Kindly, register at GIC 2024 Earlybird registartion

https://genomicsindia.co.in/

Rennison Lab !

Sat, 26 Oct 2024 15:10:32 -0500

Welcome to the Rennison lab in the School of Biological Sciences at the University of California San Diego. We are a group interested in the evolution and maintenance of biodiversity. We study the processes related to biodiversity using methods from the fields of evolution, ecology, population genomics, and theory.

More at https://rennisonlab.com/

NVIDIA and Arc Institute Unveil Evo 2: A Breakthrough AI for DNA Design

BioStar — Fri, 21 Feb 2025 10:39:47 -0600

NVIDIA and the Arc Institute have introduced Evo 2, a groundbreaking AI model designed to understand, predict, and generate DNA sequences. This marks a major advancement in computational biology, offering scientists an unprecedented tool to decode the genetic blueprint of life and even design entirely new biological systems.

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.

Ley Lab !

Fri, 11 Sep 2026 13:21:10 -0500

Research Program aims to elucidate how gut microbial species relate to genotypic differences between human individuals, to identify bacterial and archaeal species that share an evolutionary history with humans, and to determine the molecular basis of long-term host-microbial relationships. We approach these aims through a combination of population-level observations and laboratory-based molecular-level investigations. We link inter-microbial and microbial-host interactions at the molecular scale to patterns at population and evolutionary scales.

More at https://leylab.com/

AI Uncovers Hidden Secrets in Bacterial DNA, Opening New Frontiers in Genomic Research

Jitendra Narayan — Fri, 25 Sep 2026 22:38:42 -0500

Scientists are now using artificial intelligence in order to examine sections of bacterial DNA that have not been looked at before. This method is showing potential RNA interactions and previously unknown genetic systems which could alter our understanding of microbes. A new study presents Minerva, a genome language model, demonstrating that AI can assist researchers in identifying biological patterns that traditional techniques might fail to detect.

The study, which was made available as a preprint on bioRxiv on 23 September 2026, focuses on the non-coding sections of DNA that are still largely unknown. Although these areas do not produce proteins, they can contain important signals and instructions which have an effect on cell function. The research presents a novel approach to investigating how bacteria handle and control their genetic information.

AI maps previously unexplored genomic regions

The team developed Minerva in order to identify possible interactions between different regions in microbial genomes; rather than depending on similarities with known sequences, Minerva predicts these relationships directly from the DNA by using patterns learned by a genome language model.

On 150 bacterial genomes, Minerva identified a large number of interactions that were not included in the existing annotations. The researchers stated that 84.3 per cent of the predicted intergenic base-pairing interactions were not present in the current annotations, which demonstrates that AI can be of help in generating new ideas in biology.

Unusual RNA structures and viral genetic systems identified

The study also examined a bacterial RNA family known as TwoAYGGAY in Pseudomonas; the model anticipated longer RNA structures and identified associations with repeated DNA sequences, thus providing new insights into how these non-coding elements are organised and how they have evolved.

In a separate section of the study, the researchers examined reverse transcriptase systems associated with bacteriophages, which are viruses that infect bacteria. They identified RNA arrays that maintain their structure but have different sequences and were linked to Unknown Group 27 reverse transcriptases. The findings indicate that these RNAs could function as templates for the production of complementary DNA that is capable of forming hairpin shapes.

The researchers also observed that Minerva was able to detect patterns associated with protein-coding areas, even though it had not been trained to do so. This indicates that genome language models may pick up on biological signals that go beyond what they were intended to identify.

Implications for future genomic research

The fact that artificial intelligence is becoming increasingly important in the field of microbial genomics is shown by the fact that models such as Minerva are able to predict interactions and identify patterns in areas which have not been extensively studied, thus helping researchers to decide what to study next and enabling them to gain a better understanding of biological systems that are still not well understood.

Yet the predictions do not reveal the exact function of each element identified. In order to verify which of the predicted interactions actually take place in living cells and the way in which they affect the microbes, experiments will be necessary. Although the study has undergone peer review, it does nonetheless offer a promising illustration of how machine learning can complement traditional genomics and assist scientists in moving from the identification of known genes to the exploration of the complex relationships that shape microbial life.

More at https://www.biorxiv.org/content/10.64898/2026.09.22.753630v2.full.pdf