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.

This is where KiNext comes into the picture. Introduced in a 2024 study published in BMC Bioinformatics, KiNext is a computational workflow designed to identify and classify protein kinases from predicted protein sequences. Instead of relying on a single search method, it brings together several approaches, including Hidden Markov Models, sequence alignment, phylogenetic analysis, and structural comparison.

The search begins with a simple question: does a protein contain the characteristics of a kinase? Protein kinases can change considerably during evolution, but important regions of their sequences often retain recognizable patterns. KiNext uses Hidden Markov Models, or HMMs, to detect these patterns. An HMM does not require a protein to be an exact match to a known kinase. Instead, it looks for a statistical sequence signature associated with kinase proteins, making it possible to detect more distant candidates.

Once potential kinases are identified, KiNext takes the analysis further. It distinguishes conventional eukaryotic protein kinases from atypical protein kinases and then attempts to classify them into different kinase groups and families. This distinction is important because simply identifying a protein as a kinase does not tell the complete story. Different kinase families can have very different evolutionary histories and biological functions.

The next stage brings evolution into the picture. KiNext can align kinase sequences and construct phylogenetic trees, allowing researchers to examine how newly identified proteins are related to previously characterized kinases. When sequence evidence alone is difficult to interpret, structural information can provide another clue. The workflow can incorporate AlphaFold-predicted structures and Foldseek-based structural comparisons to investigate whether an unusual protein resembles known kinase structures.

The researchers tested KiNext using two very different organisms: the Pacific oyster, Crassostrea gigas, and the green alga Ostreococcus tauri. In C. gigas, KiNext recovered previously reported kinases while identifying additional candidates. Structural analysis provided further evidence for many of the newly detected proteins. In O. tauri, the workflow similarly recovered most previously reported kinases and identified additional candidates while refining some of their classifications.

What makes KiNext particularly interesting is not just its ability to find kinases, but how the entire analysis is organized. The workflow uses Nextflow, allowing the different computational steps to be connected into a reproducible pipeline. Containers can also help manage software dependencies, making it easier to run the workflow across different computing environments.

This reproducibility becomes increasingly important as the number of available genomes continues to grow. A researcher studying one organism may be able to perform an analysis manually, but repeating the same process across hundreds or thousands of genomes quickly becomes impractical. A standardized workflow provides a way to perform the analysis consistently while keeping track of how the results were generated.

At its core, KiNext demonstrates a broader change taking place in modern genomics. Sequencing a genome provides an enormous amount of information, but the real scientific challenge begins afterward: understanding what all those sequences mean. Protein kinases are only one part of this larger puzzle, yet they are particularly important because they act as molecular switches throughout the cell.

By combining sequence profiles, evolutionary analysis, and structural evidence within a reproducible computational framework, KiNext provides researchers with a systematic way to uncover these molecular switches. Its real value lies not only in finding more kinases, but in making the process scalable, repeatable, and easier to apply to new genomes.

As genome sequencing continues to expand across the tree of life, tools such as KiNext can help turn enormous collections of protein sequences into meaningful biological stories—one kinase at a time.

Read more about it @

https://link.springer.com/article/10.1186/s12859-024-05953-w

Please add ur links/bookmarks in comment section.

Address of the bookmark: http://physicsdatabase.com/free-math-books/

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/23/14/1713.full

http://www.bccs.uni.no/units/cbu/research/rehmsmeier/

- Budapest Reference Connectome: View a parametrizable connectome (brain graph).
- AmphoraNet: The webserver implementation of the AMPHORA2 workflow for phylogenetic analysis of metagenomic shotgun sequencing data.
- AmphoraVizu: Chart visualization for metagenomics analysis tools AMPHORA2 and AmphoraNet.
- SCARF: Free online association rule mining tool.

More at: http://pitgroup.org

Source : https://www.coursera.org/course/ads1

Address of the bookmark: https://www.coursera.org/course/ads1

Dr. Rawal and his US based collaborators have recently secured grant for development of vaccine against an infectious disease agent. For this project, applications are invited for the posts of Post Doctoral Fellow/Research Scientist (One Position) for the following time-bound sponsored projects as per the details given below:

PI: Dr. Kamal Rawal, Biotechnology Department, JIIT, Noida.

Essential Qualification(s) for Post Doctoral Fellow/ Research Scientist:

We are seeking an individual with expertise in analyzing literature information, text mining, network biology, data integration, and modeling. Competitive candidates would also have programming experience in scripting languages with perl, C, C++, and R programming. This position requires a PhD in Computational Biology, Bioinformatics, Biostatistics, Physics or related fields, and evidence of scientific productivity through publications in international journals. Motivation to gain an in-depth understanding of biological phenomena is required. Applications should include a current CV and names of at least three references. Application packages and inquiries regarding this position can be sent to Dr. Kamal Rawal (bioinfocvatgmaildotcom and kamaldotrawalatgmaildotcom). Screening of applications will commence immediately and the position will remain open until filled. Candidates having master’s degree with extensive experience in IT industry or research can also be considered for this post.

Salary: Rs 50000 per month.

Duration: 2 years or upto the project duration.

Number of position: 1

Candidate may also fill the following form:

https://docs.google.com/…/1FAIpQLSdZoZ21ZoNRStEeL5…/viewform

http://tinyurl.com/bioinfocv2017

In this tutorial series article, I will explore features and packages of python which are widely used in the big data, NGS, and bioinformatics. I will also walk through a real biological example which shows NGS data processing with the help of python packages and programming.

Python has a couple of points to recommend it to biologists and scientists specifically:

• It's widely used in the scientific community
• It has a couple of very well designed libraries for doing complex scientific computing (although we won't encounter them in this book)
• It lend itself well to being integrated with other, existing tools
• It has features which make it easy to manipulate strings of characters (for example, strings of DNA bases and protein amino acid residues, which we as biologists are particularly fond of)

In general, following are some of the important features of python which makes it a perfect fit for rapid application development.

• Python is interpreted language so the program does not need to be compiled. Interpreter parses the program code and generates the output.
• Python is dynamically typed, so the variables types are defined automatically.
• Python is strongly typed. So the developers need to cast the type manually.
• Less code and more use makes it more acceptable.
• Python is portable, extendable and scalable.

There are two major Python versions, Python 2 and Python 3. Python 2 and 3 are quite different. This tutorial uses Python 3, because it more semantically correct and supports newer features.

I will post tutorial on daily basis on this page. Check the sub-pages on right side.