BOL: Related items

Machine Learning for Genomics

Jit — Thu, 09 Sep 2021 11:26:32 -0500

Module 1: Statistics for genomics (2-8 August 2021)

A simple intro to statistical distributions
hypothesis testing
linear models.

reading: http://compgenomr.github.io/book/stats.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/compgen2021_stats.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week1/

Module 2: Unsupervised learning for genomics (9-15 August 2021)

Understanding basic intuition behind machine learning approaches.
Using unsupervised learning to cluster and visualise data points
Dimension reduction techniques for visualisation and as input to clustering methods

reading: http://compgenomr.github.io/book/unsupervisedLearning.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/compgen2021_unsupervisedLearning.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week2/

Module 3: Supervised learning for genomics (16-22 August 2021)

Understanding and using supervised learning methods for predictive purposes
How to measure prediction performance
Understand and use cross-validation and related concepts

reading: http://compgenomr.github.io/book/supervisedLearning.html

slides: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/compgen2021_supervisedLearning.pdf

exercises+code: https://github.com/BIMSBbioinfo/compgen2021/tree/main/week3/

https://github.com/BIMSBbioinfo/compgen2021

Address of the bookmark: https://github.com/BIMSBbioinfo/compgen2021

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

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.

Computational Genomics: Applied Comparative Genomics

Jit — Wed, 29 Nov 2017 05:11:30 -0600

The primary goal of the course is for students to be grounded in theory and leave the course empowered to conduct independent genomic analyses. We will study the leading computational and quantitative approaches for comparing and analyzing genomes starting from raw sequencing data. The course will focus on human genomics and human medical applications, but the techniques will be broadly applicable across the tree of life. The topics will include genome assembly & comparative genomics, variant identification & analysis, gene expression & regulation, personal genome analysis, and cancer genomics. The grading will be based on assignments, a midterm exam, class presentations, and a significant class project. There are no formal course prerequisites, although the course will require familiarity with UNIX scripting and/or programming to complete the assignments and course project.

Address of the bookmark: https://github.com/schatzlab/appliedgenomics

Project Associate-I | Project Associate-II | Senior Project Associate @ IGIB

Thu, 05 Aug 2021 16:11:32 -0500

Experience in Next Generation Sequencing (NGS) application and interest in Genomics/ Clinical / Translational Applications. OR Good computational programming skills and deep interest in working on interface of Genomics and Clinical application.

Project Scientist-I
Experimental / Computation analysis experience in highthroughput genomics/ clinical application.

Project Manager
Experience in handling large biological projects involving high-throughput genomics/ clinical application.

Scientific Administrative Assistant
Lab Work.

More at https://vinodscaria.genomes.in/positionsopen

Joao Pedro de Magalhaes Lab

Fri, 26 Sep 2014 19:08:34 -0500

Ageing has a profound impact on human society and modern medicine, yet it remains a major puzzle of biology. The goal of my work is to help understand the genetic, cellular, and molecular mechanisms of ageing. In the long term, I would like my work to help ameliorate age-related diseases and preserve health. No other biomedical field has so much potential to improve human health as research on the basic mechanisms of ageing. Please see our lab website for further details about our work and publications.

Functional and Comparative Genomics

http://jp.senescence.info/
http://www.senescence.info/
http://www.liv.ac.uk/integrative-biology/staff/joao-de-magalhaes/

From Genes to Algorithms: The AI Revolution in Bioinformatics

Jitendra Narayan — Wed, 16 Sep 2026 02:23:40 -0500

Imagine waking up to a message from your doctor saying your genetic profile has been analyzed and a small change in your DNA has been found that could raise your risk for a certain disease. Instead of being alarmed, you learn that the condition was caught early and your genetic information can help doctors find treatments that might work better for you. Not long ago, this would have seemed like science fiction. But thanks to the rapid progress of artificial intelligence (AI) and bioinformatics, it is becoming more realistic. Bioinformatics brings together biology, computer science, mathematics, and statistics to collect, manage, and analyze biological data. As new technologies produce huge amounts of genomic, transcriptomic, proteomic, and clinical data, traditional methods often cannot keep up. AI is now playing a bigger role in bioinformatics, helping researchers find patterns, make predictions, and understand biological systems in ways that were once out of reach.

Modern sequencing technologies have turned biology into a data-heavy science. The human genome has about three billion DNA base pairs, and sequencing can produce massive datasets quickly. Bioinformatics helps organize and interpret this information, allowing scientists to compare genomes, find genes, spot genetic differences, study gene expression, and understand diseases at the molecular level. Now, AI is changing how these datasets are analyzed. Machine learning and deep learning can find complex patterns in biological data and make predictions using large datasets. Rather than following only set rules, AI systems can learn from data and help researchers solve tough biological problems.

A key example of AI in bioinformatics is predicting protein structures. Proteins are vital for many functions in living things, and their three-dimensional shapes are closely tied to what they do. For a long time, figuring out these structures in the lab was slow and difficult. AI-based tools like AlphaFold have shown that deep learning can predict protein structures with impressive accuracy. These advances help speed up biological research and give scientists useful predictions to support their experiments. This shift means bioinformatics is moving from just analyzing biological data to predicting how biology works and designing new solutions.

AI is also expected to greatly influence drug discovery. Traditionally, developing a new drug is a long and costly process that involves finding biological targets, searching for possible drug molecules, testing how well they work, studying their properties, and running many experiments. AI can help researchers analyze large chemical and biological datasets, predict how molecules might interact with targets, find promising compounds, and estimate properties of drug candidates. Generative AI can even help design new molecules to test in the lab. This does not mean lab experiments will go away, but AI can help researchers focus on the best options and possibly speed up some parts of drug development.

Personalized medicine is another area where AI-driven bioinformatics could make a big difference. Each person has a unique mix of genetic, molecular, environmental, and lifestyle factors, so people with the same disease might respond differently to the same treatment. Bioinformatics lets researchers study individual biological data, and AI can combine many types of information, like genetic data, gene expression, protein levels, medical images, clinical records, and drug responses. In the future, this could help doctors look beyond just the disease and focus on the specific biology of each patient. This would support more personalized ways to diagnose, assess risk, and choose treatments.

As AI becomes more common, the job of bioinformaticians will likely change. Instead of spending most of their time on repetitive computer tasks, they may focus more on creating research questions, understanding AI results, checking predictions, and linking computer findings to lab experiments. Future professionals will need to know about molecular biology, genetics, statistics, programming, machine learning, data science, databases, and computational biology. But technical skills alone will not be enough. Being able to ask good scientific questions and carefully judge computer results will be even more important, since AI can make predictions, but scientists must decide if those predictions make sense and can be proven in the lab.

Even with its great potential, using AI in bioinformatics comes with big challenges. AI systems rely on the quality and variety of the data they are trained on. Biological and clinical datasets can have missing information, technical errors, inconsistent measurements, and may not represent all groups equally. A model that works well on one dataset might not work as well on another group or in a different setting. That is why data quality, proper validation, transparency, reproducibility, and careful model development are crucial for the future of AI in bioinformatics. Researchers also need to think about genetic privacy, data ownership, security, and using personal biological information responsibly. These concerns are especially important when AI predictions are used in healthcare.

The lab of the future may look very different from today’s. Researchers might use AI to review scientific papers, find research questions, analyze large datasets, come up with ideas, and predict which experiments will be most helpful. Automated lab systems could then run these experiments, create new data, and send the results back to computer models. This would create a cycle of making guesses, predicting, experimenting, collecting data, and learning. This approach could speed up scientific discovery by helping researchers explore questions more efficiently. Bioinformatics could become a key link between computer predictions and real lab experiments.

In the end, the future of bioinformatics with AI will not be about replacing people with machines. It will be about humans and AI working together. Computers can handle huge amounts of biological data and spot patterns that a single person could not. But humans bring scientific understanding, creativity, critical thinking, ethical judgment, and the ability to see the bigger picture. AI might find a pattern in millions of genomes, but scientists need to figure out why it matters. An AI model could suggest a new drug, but researchers must check if it is safe and works well. A computer might find a genetic change, but doctors have to decide what it means for a real patient.

The future of bioinformatics is closely tied to the future of AI. As biological data grows and AI models get better, bioinformatics may shift from mainly analyzing data to predicting biological processes, designing new molecules, speeding up experiments, and supporting personalized healthcare. For students and researchers, this is an exciting chance to work where biology and technology meet. The most successful people will not just be those who use the latest AI tools, but those who understand biology well enough to ask good questions and judge the answers carefully. The future will not be about AI versus humans, but about people and AI working together to understand life in ways we never could before. The next big discovery may come from this partnership between a biological question, a computer model, a lab experiment, and human curiosity.

The Centre for Bioinformatics (MCB) Lab

Sun, 14 Jul 2013 12:41:20 -0500

The Centre for Bioinformatics (MCB) is a diverse collection of professors, postdoctoral fellows, and students, who share a common interest in Bioinformatics.

Research Area

We are interested in the development of the statistics and computational methods for the analysis of this data in breast cancer.
We have worked on probabilistic models for subcellular localization, protein-protein interactions, and problems related to chemical genomics.
We are interested in the development of bioinformatics/biostatistical methodology in the analysis of epigenetic/epigenomic data.
We are interested in integrative bioinformatics approaches to learn the gene, gene products, interactions, and regulatory mechanisms involved in mental retardation.

Link @ http://www.mcgill.ca/mcb/

Jayaram Lab

Sun, 14 Jul 2013 14:04:37 -0500

Responsible (a) for developing Chemgenome, Bhageerath & Sanjeevini methods & softwares for genome annotation, protein tertiary structure prediction & computer aided drug design respectively, (b) for setting up a multi-teraflop supercomputing facility for Bioinformatics & Computational Biology at IIT Delhi, and (c) for making the hardware and software freely accessible at (www.scfbio-iitd.res.in) to the global scientific user community.

Faculty facilitator/Founder Director for two start-up companies (Leadinvent incubated at IIT, Delhi from 2006-2009 & Novoinformatics, under incubation at IIT Delhi since 2011).

Research Interest
Genome Analysis, Protein Structure Prediction and Drug Design.

Link @ http://www.scfbio-iitd.res.in/

Genomics for Bioinformatician

Jitendra Narayan — Sat, 20 Jul 2013 07:03:00 -0500

Genomics is the study of the genomes of organisms. The field includes intensive efforts to determine the entire DNA sequence of organisms and fine-scale genetic mapping efforts. The field also includes studies of intragenomic phenomena such as heterosis, epistasis, pleiotropy and other interactions between loci and alleles within the genome. In contrast, the investigation of the roles and functions of single genes is a primary focus of molecular biology or genetics and is a common topic of modern medical and biological research. Research of single genes does not fall into the definition of genomics unless the aim of this genetic, pathway, and functional information analysis is to elucidate its effect on, place in, and response to the entire genome's networks.

Genomics was established by Fred Sanger when he first sequenced the complete genomes of a virus and a mitochondrion. His group established techniques of sequencing, genome mapping, data storage, and bioinformatic analyses in the 1970-1980s. A major branch of genomics is still concerned with sequencing the genomes of various organisms, but the knowledge of full genomes has created the possibility for the field of functional genomics, mainly concerned with patterns of gene expression during various conditions. The most important tools here are microarrays and bioinformatics. Study of the full set of proteins in a cell type or tissue, and the changes during various conditions, is called proteomics. A related concept is materiomics, which is defined as the study of the material properties of biological materials (e.g. hierarchical protein structures and materials, mineralized biological tissues, etc.) and their effect on the macroscopic function and failure in their biological context, linking processes, structure and properties at multiple scales through a materials science approach. The actual term 'genomics' is thought to have been coined by Dr. Tom Roderick, a geneticist at the Jackson Laboratory (Bar Harbor, ME) over beer at a meeting held in Maryland on the mapping of the human genome in 1986.

The outcome of almost two years of intense discussions with literally hundreds of scientists and members of the public, has three major areas of focus: Genomics to Biology, Genomics to Health, and Genomics to Society.

Genomics to Biology:
The human genome sequence provides foundational information that now will allow development of a comprehensive catalog of all of the genome's components, determination of the function of all human genes, and deciphering of how genes and proteins work together in pathways and networks.

Genomics to Health:
Completion of the human genome sequence offers a unique opportunity to understand the role of genetic factors in health and disease, and to apply that understanding rapidly to prevention, diagnosis, and treatment. This opportunity will be realized through such genomics-based approaches as identification of genes and pathways and determining how they interact with environmental factors in health and disease, more precise prediction of disease susceptibility and drug response, early detection of illness, and development of entirely new therapeutic approaches.

Genomics to Society:
Just as the HGP has spawned new areas of research in basic biology and in health, it has created new opportunities in exploring the ethical, legal, and social implications (ELSI) of such work. These include defining policy options regarding the use of genomic information in both medical and non-medical settings and analysis of the impact of genomics on such concepts as race, ethnicity, kinship, individual and group identity, health, disease, and "normality" for traits and behaviors.

This vision for the future of genomics is not just about the NHGRI. It encompasses the whole field of genomics, including the work of all the other Institutes and Centers at the NIH and of a number of other federal agencies. All of the NIH Institutes are already taking full advantage of the sequence and will apply its data to the better understanding of both rare and common diseases, almost all of which have a genetic component. A recent example of the way that the HGP and the knowledge and new technologies it has spawned are already facilitating science is the extremely rapid sequencing by groups in Canada and at the Centers for Disease Control and Prevention (CDC) in Atlanta of the genome of the virus that causes Severe Acute Respiratory Syndrome (SARS). The sequencing of the SARS virus genome provides insight into this new and deadly disease at a speed never before possible in science. In turn, this should lead to the rapid development of diagnostic tests and, in time, vaccines and effective treatments.

Links for the addition material available on Net

Genomes and genomics:

Bioinformatics and Genomics:

Structural genomics tutorial:

Comparative Genomics Tutorial:

GENOME TUTORIAL:

Tools and resources for identifying protein families, domains and motifs

Bioinformatics Tools
Tips, Tutorials, and Terminology for Using Selected Resources in Genome Database Guide:

A Web-Based Comparative Genomics Tutorial for Investigating Microbial Genomes:

Free Online Tutorials Teach Anyone How to Use Genome Databases:

Circos to create concise, explanatory, unique and print-ready visualizations of your data:

Genomics and Comparative Genomics Learning Module:

Computational Challenges in Comparative Genomics

A Tutorial:

A Comparative Genomics Resource for Grains:

PLAZA: A Comparative Genomics Resource to Study Gene and Genome Evolution in Plants:

VISTA :

Software for Genomics

Artemis Artemis is a free genome viewer and annotation tool that allows visualization of sequence features and the results of analyses within the context of the sequence, and its six-frame translation.
Chromas It will display and prints chromatogram files from ABI automated DNA sequencers, and Staden SCF files which the analysis programs for ALF, Li-Cor and Visible Genetics OpenGene sequencers can create.
Glimmer A system for finding genes in microbial DNA, especially the genomes of bacteria and archaea.Glimmer (Gene Locator and Interpolated Markov Modeler) uses interpolated Markov models (IMMs) to identify the coding regions and distinguish them from noncoding DN
Glimmer HMM A fast and accurate gene finder based on a GHMM architecture, developed specifically for eukaryotes. It incorporates splice site models adapted from the GeneSplicer program and uses interpolated Markov models for evaluating the coding regions.
Glimmer M A gene finder derived from Glimmer, but developed specifically for eukaryotes. It is based on a dynamic programming algorithm that considers all combinations of possible exons for inclusion in a gene model and chooses the best of these combinations. The d
MUMmer MUMmer is a system for rapidly aligning entire genomes, whether in complete or draft form.
pDRAW pDRAW32 is being developed as a free time hobby project. It is far from finished, but as it has reached a point where it could be helpful for many labs, it is now available to the scientific community.
Sequin Sequin is a stand-alone software tool developed by the NCBI for submitting and updating entries to the GenBank, EMBL, or DDBJ sequence databases. It is capable of handling simple submissions that contain a single short mRNA sequence, and complex submissio
Staden The Staden Package consists of a series of tools for DNA sequence preparation (pregap4), assembly (gap4), editing (gap4) and DNA/protein sequence analysis (spin).

For more software @ http://bioinformaticsonline.com/bookmarks/view/926/list-of-popular-bioinformatics-softwaretools