https://www.seqanswers.com/ vBulletin Forums en Thu, 17 Sep 2026 06:56:52 GMT vBulletin 60 https://www.seqanswers.com/images/misc/rss.png https://www.seqanswers.com/ https://www.seqanswers.com/forum/news/327682-stanford-team-turns-scientific-papers-into-interactive-ai-agents Wed, 16 Sep 2026 18:23:23 GMT Scholarly journals have served as the written record of scientific advancement since 1665, offering static text for people to read. A team of... Scholarly journals have served as the written record of scientific advancement since 1665, offering static text for people to read. A team of Stanford Medicine researchers, led by Jiacheng Miao and James Zou, aims to change that dynamic with a new tool called Paper2Agent, which converts research papers including text, figures and data into an interactive AI agent capable of discussing the paper and interacting with agents built from other papers. Paper2Agent is described in Nature.

Zou traced the idea to how knowledge has always been recorded. “For essentially all of human history, the way that we represent knowledge is in the form of these very passive artifacts,” he said. “In old times people carved knowledge into stones, and now we type knowledge into words on pages—but in some sense pages aren’t that much better.” He described the project as “an opportunity to fundamentally reimagine what knowledge looks like. Instead of having only passive artifacts, why don’t we convert each static record into an active embodiment of knowledge?”—something closer, he said, to a virtual author capable of explaining and extending its own findings.

Turning a paper into an agent starts with a group of AI “worker agents” that examine a published paper along with its code and data, then attempt to reproduce the original research from scratch inside a virtual environment. That reenactment lets the agents capture details a reader would otherwise have to dig out manually, from reagents to experimental setup and execution. The resulting knowledge is stored using a model context protocol, or MCP. “An MCP lets AI essentially represent a paper PDF in a form that’s easy for agents to access, almost like a filing system,” Zou said, with each section of the paper organized into its own folder. Humans still play a role in the process, since manuscripts don’t capture failed experiments or the judgment calls behind experimental setups; authors supply that missing context through conversational exchanges with the agent.

The team demonstrated what happens when paper agents talk to each other by converting two unrelated papers, one on predicting how genetic mutations affect the genome, the other a genome-wide association study of ADHD risk. Working together, the two agents flagged a molecular variant near a gene called MPHOSPH9 linked to increased ADHD risk, a connection Zou said had not previously been reported. “In the past, if there are two research groups that publish two different papers, those two research groups have to somehow find each other,” he said. The eventual goal, he added, is something closer to manuscript speed dating at scale, with millions of paper agents surfacing common ground on their own.

Zou emphasized that attribution remains essential to the system: “It’s still important to attribute the final discoveries and reference them back to original papers and original human authors.” He also said the conditions under which agents collaborate need close guidance to keep their work safe and ethical. The team has so far created more than 100 paper agents, with a longer-term goal of giving most manuscripts one. “Millions of papers are published every year,” Zou said. “There’s enormous potential here.”
]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327682-stanford-team-turns-scientific-papers-into-interactive-ai-agents https://www.seqanswers.com/forum/news/327679-study-confirms-dna-zipper-model-of-strand-pairing Wed, 09 Sep 2026 20:14:31 GMT Like charges normally repel one another, yet DNA molecules must pair up inside living cells to carry out essential biological processes, including... Like charges normally repel one another, yet DNA molecules must pair up inside living cells to carry out essential biological processes, including genetic recombination, gene silencing and the development of cancer. Using high-powered atomic force microscopy, a team led by researchers from the University of York observed short DNA fragments matching up with exact precision, groove for groove. Advanced computer simulations revealed that positively charged metal ions act as tiny molecular bridges, nestling inside the grooves to lock the two strands together. The study was published in Nucleic Acids Research.

“This discovery could help researchers identify regions of the genome specially involved in DNA pairing,” explained study co-leader Professor Agnes Noy. “These regions may become particularly important when mutations disrupt normal cellular processes and contribute to cancer.”

The findings confirm a 20-year-old theory known as the “DNA zipper” model, originally proposed by Professor Alexey Kornyshev from Imperial College London, which suggested that surrounding salt ions create alternating charge patterns, allowing DNA molecules to line up like interlocking spiral staircases. To test this, the team used atomic force microscopy to scan DNA samples and build topographical maps, while detailed computer models tracked the movement of individual atoms and ions. They discovered that double-charged metal ions act like two charged arms, holding both DNA strands simultaneously across the gap.

Thomas Catley, co-lead author from the University of Sheffield, said: “It was incredible to be able to directly visualize the long-hypothesized mechanism for the first time. The advanced imaging techniques at our disposal are allowing us to uncover these key DNA interactions which have implications in many key cellular processes.”

The team also discovered that DNA pairing is not uniform. Certain DNA sequences form much stronger contacts than others, creating specific hotspots where two helices are particularly likely to align. Beyond cancer research, these programmable interactions could eventually help engineers design custom DNA structures for future biotechnology applications.
]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327679-study-confirms-dna-zipper-model-of-strand-pairing https://www.seqanswers.com/forum/news/327677-berkeley-team-builds-genomic-language-model-to-pinpoint-key-genetic-variants Wed, 09 Sep 2026 19:33:22 GMT More than two decades after scientists first sequenced the human genome’s 3 billion DNA letters, most of that code’s meaning remains unclear. Only 1... More than two decades after scientists first sequenced the human genome’s 3 billion DNA letters, most of that code’s meaning remains unclear. Only 1 to 2% of human DNA codes for proteins; the rest is a mix of leftover junk DNA and regulatory elements that control gene expression. These non-coding regions of the genome could hold the key to understanding a variety of inherited traits, but first, scientists have to understand how variants in this DNA contribute to the multitude of traits that make each of us unique.

To address this, researchers at the University of California, Berkeley have built a genomic language AI model, called GPN-Star, that outperforms competing models at identifying genetic variants most likely to influence inherited traits, including disease risk, while using far less computing power to train. The study was published in Nature.

“Our model excels in making predictions about the pathogenicity of genetic variants, and identifying functional versus non-functional elements in the genome,” said Yun Song, the study’s senior author. “We hope our work will help drive biological discovery,” Song said. “We believe our predictions will help to prioritize the experiments that could have the greatest impact on human health.”

Genomic language models work a little like chatbots trained on DNA instead of text, learning to recognize patterns that reveal a genome’s functional elements. Most such models, including the massive Evo 2 model, train on unaligned genomes across all domains of life, an approach demanding enough that Evo 2 took 2,000 processors and months to build. GPN-Star instead trains on whole-genome alignments, comparing hundreds of species’ genomes to a single reference to flag conserved or changed code, work that can take just days on a handful of processors. “We tried to help the model learn by curating data that’s more likely to harbor functional elements,” Song said.

The team trained GPN-Star on alignments anchored to humans and five other species, including human-anchored versions built at primate, mammal, and vertebrate timescales. “We found that models trained at different evolutionary time scales were actually optimized for interpreting different kinds of genetic variants,” said co-first author Chengzhong Ye. Models trained on longer timescales were better at judging rare, slow-evolving protein variants, while the primate-specific model better predicted variants tied to complex traits like schizophrenia risk. “For complex traits, we were surprised and pleased to see that training a model that’s specific to primate genomes—which are more relevant to recent human evolution—really helped us make better predictions,” Song said.

Because GPN-Star needs so little computing power to train, the researchers hope other teams can easily adapt and build on their work. “We’re making great progress,” added co-first author Gonzalo Benegas. “But the more people that can work with these models, the better they will get.”]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327677-berkeley-team-builds-genomic-language-model-to-pinpoint-key-genetic-variants https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327676-registration-closing-soon-2nd-berlin-winter-school-in-rna-seq-data-analysis-november-2–5-2026 Wed, 09 Sep 2026 09:38:47 GMT Hi everyone, We are organizing the 2nd Berlin Winter School in RNA-Seq Data Analysis, a four-day, hands-on course for biologists and data analysts...
We are organizing the 2nd Berlin Winter School in RNA-Seq Data Analysis, a four-day, hands-on course for biologists and data analysts with little or no previous experience in NGS bioinformatics.

• Date: November 2–5, 2026
• Time: 9:00 am–5:00 pm
• Location: Berlin, Germany
• Language: English
• Registration fee: EUR 1,449 excluding 19% VAT

Participants will work with real bulk RNA-Seq data and cover:

• Linux and command-line basics for bioinformatics
• FASTQ quality control and preprocessing
• Read alignment, split-read mapping and pseudo-alignment
• SAM/BAM files, mapping statistics and visualization
• Gene, transcript and exon quantification
• Differential gene expression with DESeq2
• Differential splicing and isoform expression
• Diagnostic graphics and statistics in R
• Comparison and critical evaluation of different analysis approaches

A basic understanding of molecular biology is sufficient. The course does not cover single-cell RNA-Seq analysis.

Course materials, catering, a conference dinner, a guided city tour, high-performance workstations and a downloadable analysis environment are included. No laptop is required.

Registration will close soon or as soon as all available places have been filled. Places are allocated on a first-come, first-served basis.

More information and registration]]> Bioinformatics ecSeq Bioinformatics https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327676-registration-closing-soon-2nd-berlin-winter-school-in-rna-seq-data-analysis-november-2–5-2026 https://www.seqanswers.com/forum/applications-forums/sample-prep-library-generation/327675-struggling-with-small-insert-sizes-using-neb-s-fs-ultraexpress-dna-library-kit Mon, 07 Sep 2026 22:27:54 GMT Sample Prep / Library Generation kbkubow https://www.seqanswers.com/forum/applications-forums/sample-prep-library-generation/327675-struggling-with-small-insert-sizes-using-neb-s-fs-ultraexpress-dna-library-kit https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327674-cell-and-gene-therapy-design-estimands-operations-and-ai-–-livestream-seminar Sat, 05 Sep 2026 01:39:47 GMT Hi everyone Instats is excited to offer a 1-day seminar, Cell and Gene Therapy: Design, Estimands, Operations, and AI...
Instats is excited to offer a 1-day seminar, Cell and Gene Therapy: Design, Estimands, Operations, and AI, livestreaming October 6 and sponsored by the American Statistical Association’s Cell and Gene Therapy Scientific Working Group. As cell and gene therapies rapidly reshape biomedical innovation, this seminar provides PhD students, academic researchers, and applied scientists with a coherent framework for evaluating complex interventions across translational research, clinical development, and regulatory evidence generation. Drawing on expertise associated with the American Statistical Association’s Cell and Gene Therapy Scientific Working Group, the workshop examines core design and analysis challenges, including single-arm and randomized trials, dose finding, historical borrowing, estimands, operational considerations, and the production of credible, publishable evidence in settings with limited samples and heterogeneous populations. The seminar also considers the growing role of artificial intelligence in cell and gene therapy development, helping participants understand how emerging computational tools may influence discovery, decision support, evidence synthesis, reproducibility, and interpretation in this fast-moving field.

Sign up today to secure your spot, and feel free to share this opportunity with colleagues and students who might benefit!


Best wishes

Michael Zyphur
Professor and Director
Instats | instats.org]]> Bioinformatics Instats https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327674-cell-and-gene-therapy-design-estimands-operations-and-ai-–-livestream-seminar https://www.seqanswers.com/forum/news/327673-study-links-five-chronic-fatigue-disorders-to-shared-biological-architecture Thu, 03 Sep 2026 18:22:53 GMT Researchers at the University of East Anglia have identified shared biological mechanisms that may help explain chronic exhaustion in conditions... Researchers at the University of East Anglia have identified shared biological mechanisms that may help explain chronic exhaustion in conditions including long Covid and PTSD. A new paper published in the Journal of Translational Medicine reveals biological similarities between five major illnesses long viewed as separate disorders: chronic fatigue syndrome (also known as ME), long Covid, post-traumatic stress disorder, rheumatoid arthritis, and multiple sclerosis. The findings suggest these conditions may share common underlying mechanisms, despite being triggered by different events ranging from viral infections to psychological trauma or autoimmune responses.

According to lead researcher Dmitry Pshezhetskiy, “Until now, illnesses including long Covid, PTSD, ME/CFS, multiple sclerosis, and rheumatoid arthritis were viewed as seemingly unrelated and triggered by completely different events.” He noted that patients across all five conditions “frequently report remarkably similar symptoms—overwhelming fatigue, brain fog, poor concentration, disturbed sleep, autonomic dysfunction and a dramatic reduction in everyday functioning.” As he put it: “What we discovered is something approaching a biological unifying theory of fatigue.”

Rather than analyzing DNA sequences directly, researchers used a computational method that examines the three-dimensional architecture of the genome—how DNA folds inside living cells, bringing regions that sit far apart in the linear sequence into contact at points where gene activity is controlled. Published genomic data for long Covid, PTSD, rheumatoid arthritis, and multiple sclerosis, drawn from existing genome-wide association studies, were combined computationally with 3D genomic data from an earlier ME/CFS patient study, without collecting new patient samples. Applied to the five conditions, the analysis found that genetic changes with little in common connected into the same regulatory circuitry.

“We expected to find at least some overlap in genes across the conditions. But we actually found the opposite,” Pshezhetskiy said. “But when we analyzed how those genes interact in complex biological networks, a completely different picture emerged. Suddenly, the diseases appeared deeply connected. This is not something you can see by reading the genetic sequence alone, which is why these conditions may have looked unrelated for so long.”

The study found genes linked to each illness fed into the same major biological systems, including immune and inflammatory signaling, mitochondrial energy production, metabolic regulation, stress-response mechanisms and neuroendocrine signaling. The team also identified “hub genes” sitting at the busiest points within these shared networks, including genes involved in immune regulation and mitochondrial energy production—candidates the authors say require further work to confirm. One part of the analysis focused on ME/CFS flagged LAG3, a gene associated with T-cell exhaustion.

“This work adds to a growing body of evidence suggesting that persistent immune dysfunction may play a far larger role in chronic fatigue-related illnesses than previously recognized,” Pshezhetskiy said. ME/CFS and long Covid are currently diagnosed largely through symptoms, with no universally accepted laboratory test available, leaving many patients facing years of uncertainty. Earlier work using this same genomic approach had already produced a blood-based ME/CFS test promising high diagnostic accuracy, though further validation is needed before clinical use; the new findings raise the prospect of shared biological signatures across several conditions, not only ME/CFS.

“Rather than viewing long Covid, ME/CFS, PTSD, rheumatoid arthritis, and multiple sclerosis as entirely separate disorders, we now think they may be different manifestations of disturbed biological networks operating throughout the body,” Pshezhetskiy said. “This study offers a framework for understanding how different triggers can converge to cause the exact same profound clinical exhaustion.”
]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327673-study-links-five-chronic-fatigue-disorders-to-shared-biological-architecture https://www.seqanswers.com/forum/news/327672-newly-discovered-chimeric-proteins-found-to-play-roles-throughout-the-body Wed, 02 Sep 2026 20:32:08 GMT Scientists have long understood that each of the roughly 20,000 genes in the human body carries the instructions for a single kind of protein.... Scientists have long understood that each of the roughly 20,000 genes in the human body carries the instructions for a single kind of protein. Researchers at Harvard Medical School have now found that instructions from separate genes—even genes on different chromosomes—can combine into chimeric mRNAs that produce previously unknown, functional proteins. The findings, published in Nature, point to potentially thousands of new chimeric proteins and show that at least some carry out important jobs in the body.

“Nobody knows these exist. Medicine doesn’t know they exist, the pharmaceutical industry doesn’t know they exist,” said senior author Ruaidhrí Jackson. “We’ve discovered an entirely new gene regulation system that could expand the known genome and proteome dramatically.” Jackson’s team doesn’t yet know how widespread the phenomenon is, but says chimeric proteins may contribute to poorly understood disease processes and could point to new drug targets.

Chimeric mRNA in humans had previously been documented mainly in cancer, where broken DNA scatters and genes fuse abnormally; sequencing results occasionally hinted it might also occur in healthy tissue, but standard techniques struggled to detect it reliably.

Using a newer method called direct RNA sequencing, the team compiled a list of over 30,000 chimeric mRNAs observed at least once in mammalian cells—the “dark genome” library, as Jackson calls it—and has so far profiled how almost 400 of these are regulated by inflammatory signals. The researchers found that healthy chromosomes can loop together in mouse cells during an immune response, bringing normally distant genes into proximity so the newly adjacent genes transcribe a single chimeric mRNA built from pieces of each one, producing a hybrid protein. “We thought we had a blueprint of every mRNA that is made in the body, and now we’re saying that was just page one,” Jackson said. “There are all these other combinations that can occur.” Detecting a chimeric mRNA, though, doesn't necessarily mean it creates a functional protein.

To test that, the team focused on one mouse chimera combining the genes for GSDMD and TMEM106A. GSDMD alone produces a protein that opens cell membranes during pyroptosis, in which immune cells burst open to trigger a large inflammatory response. They confirmed GSDMD-TMEM106A occurred naturally in two lab mouse strains and a wild-derived strain, then built a genetic tool to stop production of the chimeric protein alone, without disrupting normal GSDMD and TMEM106A.

Mice without the chimeric protein mounted a slower immune response and, when infected with Salmonella, were less able to fight off the bacteria. “They can’t control the bacterial infection,” Jackson said. “Nearly 50 percent of the immune process has been reduced without our chimeric protein, even though the normal GSDMD is still present. GSDMD needs our GSDMD-TMEM106A to function fully.” Given an endotoxin that causes sepsis, an extreme and potentially deadly immune response, 70 percent of mice without the chimeric protein survived a dose that would normally be lethal, because their immune response was milder than normal.

Working with Moderna, the researchers engineered an mRNA to boost GSDMD-TMEM106A production. Mice with elevated chimera levels did not survive even a mild endotoxin dose, but mice that both lacked the GSDMD gene and had elevated chimera levels had no response to the endotoxin at all—showing the chimera speeds up pyroptosis but cannot open cell membranes without GSDMD itself. Together, the results confirmed the chimeric protein is vital to the mouse inflammatory response.

“GSDMD-TMEM106A was initially discovered based on a single read in one of our samples. We were bracing ourselves for this to turn out to be nothing,” said co-first author Harry Kane. “It was very exciting when we found that GSDMD-TMEM106A was not only a real protein but also functional—it could modulate release of inflammatory molecules from cells.”

Jackson, Kane and their colleagues are now studying the molecular cues behind chimeric mRNA formation and evaluating other chimeric mRNAs for potential uses in medicine. “We want to find out which ones are operative in currently incurable diseases,” Jackson said. “We think we can find new players that have been completely overlooked by medicine, by pharma, by biomedical science in general.”]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327672-newly-discovered-chimeric-proteins-found-to-play-roles-throughout-the-body https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327671-rna-seq-analysis-online-training-practical-ngs-transcriptomics-data-analysis-final-call Wed, 02 Sep 2026 14:58:27 GMT RNA-Seq Analysis (RNAA02) – Practical NGS & Transcriptomics Data Analysis

Only 3 places left!

RNA-Seq Analysis (RNAA02)

Delivered by Dr. Frances Turner, a bioinformatician at the University of Edinburgh with extensive experience supporting researchers in the analysis and interpretation of high-throughput sequencing data, including whole-genome, transcriptomic, and metagenomic datasets.

Learn how to analyse bulk RNA-Seq data, taking sequencing data from experimental design and initial quality control through alignment, gene-expression quantification, differential expression, visualisation, and functional interpretation.

RNA-Seq is one of the most widely used next-generation sequencing approaches for investigating gene expression. However, producing biologically meaningful results requires careful decisions at every stage of the analysis, from experimental design and read QC to alignment, quantification, statistical modelling, and interpretation. This course provides hands-on training in a complete RNA-Seq workflow, giving participants the practical skills needed to analyse their own sequencing datasets confidently. What you'll gain

• A strong understanding of RNA-Seq experimental design
• Practical experience assessing raw sequencing data and performing quality control
• Skills in aligning RNA-Seq reads to a reference genome and assessing alignment quality
• Understanding of gene-expression quantification and common analytical pitfalls
• Practical experience performing differential expression analysis with DESeq2
• Skills using PCA to explore sample relationships and variation
• Experience analysing complex experimental designs, covariates, and continuous variables
• Skills visualising differential expression using approaches including volcano plots and MA plots
• Understanding of functional analysis of differential expression using FGSEA
• A reproducible workflow for taking RNA-Seq data from raw reads through to biological interpretation

Course format

• 4-day live, instructor-led online course
• 3.5 hours per day
• Hands-on practical exercises throughout
• All required software provided through a browser-accessible virtual machine
• Code, datasets, and presentation materials provided
• All sessions recorded
• 30 days of recording access and post-course email support
• Opportunity to discuss your own RNA-Seq projects and analysis questions

Who is this course for?

• Bioinformaticians and computational biologists
• Researchers working with bulk RNA-Seq and NGS data
• Genomics and transcriptomics researchers
• Molecular and cell biologists
• PhD students and postgraduate researchers
• Researchers who are planning an RNA-Seq experiment or need to analyse existing sequencing data

Participants should have basic experience with R, RStudio, and Linux, together with a basic understanding of transcriptomics and molecular biology. Why take this course?


RNA-Seq workflows involve considerably more than simply running differential-expression software. Decisions made during experimental design, QC, alignment, quantification, filtering, statistical modelling, and visualisation can all influence the final biological conclusions.

This course takes participants through the complete bulk RNA-Seq analysis workflow, helping you understand not only how to run the analysis but also how to identify potential problems and make appropriate analytical decisions.

Whether you're comparing treatments, conditions, genotypes, tissues, developmental stages, or other biological groups, you'll gain practical experience using DESeq2 and associated bioinformatics tools to identify, visualise, and functionally interpret differential gene expression. Course dates


21–24 September 2026
18:00–21:30 UK time
14 hours | Live online | £350 Learn more & enrol


PR Stats course page – RNA-Seq Analysis (RNAA02) Questions?


Email: oliver@prstats.org]]> Bioinformatics PR Stats https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327671-rna-seq-analysis-online-training-practical-ngs-transcriptomics-data-analysis-final-call https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327669-online-course-introduction-to-computational-biology Wed, 02 Sep 2026 07:34:29 GMT Dear all, We are pleased to announce Introduction to Computational Biology, a five-day online hands-on training course taking place 28...


We are pleased to announce Introduction to Computational Biology, a five-day online hands-on training course taking place 28 September–2 October.



Course website: https://www.physalia-courses.org/cou...ional-biology/



The course provides an introduction to modern computational approaches used in genomics, transcriptomics, and multi-omics research, combining short lectures with practical, project-based learning using real biological datasets.





Topics covered include:

-Long-read sequencing & genome assembly
-Metagenomics & pangenomics
-Single-cell RNA sequencing
-Metabolomics & multi-omics integration





Throughout the course, participants will work in small groups on guided mini-projects, developing practical skills in data analysis, biological interpretation, and scientific communication.

The course is aimed at PhD students, early-career researchers, wet-lab biologists, researchers transitioning into computational biology, and industry professionals working with genomic or multi-omics data.



For the full list of our courses and workshops, please visit: https://www.physalia-courses.org/courses-workshops/



Please feel free to share this announcement with colleagues who may be interested.

Best regards,



Carlo





--------------------

Carlo Pecoraro, Ph.D


Physalia-courses DIRECTOR

info@physalia-courses.org]]> Bioinformatics Physalia-courses https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327669-online-course-introduction-to-computational-biology https://www.seqanswers.com/articles/327667-how-immunogenomics-decodes-immunity’s-genetic-blueprint Tue, 01 Sep 2026 13:41:00 GMT The immune system’s power comes from its genetic diversity, allowing myriad threats to be neutralized through first recognizing foreign antigens....



The immune system’s power comes from its genetic diversity, allowing myriad threats to be neutralized through first recognizing foreign antigens. That diversity is also what makes the immune system so difficult to study. Recent advances in sequencing technology and computational biology, however, are giving researchers new tools to understand immune responses and immune-related diseases in greater detail.

This convergence of genetics, immunology, and computation has become a discipline of its own called immunogenomics. With this exploration of the immune system, researchers can start to determine how an individual might respond to specific treatments and dangers such as pathogens or cancer cells. “Large-scale genetic studies have identified many disease-associated variants in immune-related regions, while immune-repertoire sequencing has enabled researchers to examine the diversity and clonal expansion of T- and B-cell receptors,” says Shi-qi An, Director of Science Communications at Novogene Europe.

Genetic variation of the immune system

There are multiple genetic aspects of the immune system that researchers can interrogate to gain greater insights into health and disease.

There are several gene families central to the immune system’s ability to protect the body from pathogens and other foreign substances, including human leukocyte antigen (HLA), killer immunoglobulin-like receptor (KIR), immunoglobulin (IG), and T-cell receptor (TCR) families.¹ Of these, HLA is the most polymorphic and gene-dense region of the human genome.² Exploring variation in these gene regions and beyond can illuminate underlying genetic contributors to disease, provide an understanding of cellular dynamics during infection or other disease processes, and assess self versus non-self recognition.

T-cell and B-cell receptors, meanwhile, bind foreign antigens and differ from one lymphocyte to the next. That diversity arises through V(D)J recombination, which reshuffles variable (V), diversity (D), and joining (J) gene segments to allow interaction with a vast range of antigens.³ Receptor repertoire sequencing can help researchers identify and track unique variants associated with various diseases, potentially leading to the discovery of useful predictive, diagnostic, or monitoring biomarkers.

Sequencing as the key immunogenomics tool

Advances in whole-genome sequencing availability have driven much of recent progress in immunogenomics. Scientists can use either short-read or long-read approaches, depending on which aspect of immune variation they are studying. “Short-read sequencing remains well suited to high-throughput studies, large cohorts, quantitative gene-expression analysis, and deep measurement of immune-repertoire diversity. It is supported by mature workflows and can provide a cost-effective option when many samples or large numbers of cells must be analyzed,” says An.
When full sequence content is important, researchers may prefer long-read sequencing to obtain reads that span full genes or receptor sequences and illuminate structural variants and differing isoforms. However, short- and long-read sequencing can be complementary. “Short reads may provide scalable depth and quantification, while long reads resolve regions or molecular structures that are difficult to reconstruct unambiguously from fragmented sequences alone,” adds An.

With either sequencing method, interrogating samples at the single-cell level is bringing novel insights to the immunogenomics field. Single-cell sequencing connects an individual immune cell’s identity, its transcriptional or functional state, and its paired receptor sequence. “Single-cell sequencing is extremely powerful, as it allows us to see the exact immune repertoire expressed by each immune cell in the sample,” says Andrea O’Hara, Senior Product Manager of Multiomics and Synthesis Solutions at Genewiz. “Because each cell is barcoded individually, we can see the exact TCR or BCR sequence of every cell, while utilizing short-read sequencing for the read out. If we opt for targeted sequencing approaches, long-read sequencing is ideal so we can read the full end-to-end V(D)J sequence and chain pairing among the cell population,” she adds.

The resolution gained from single-cell sequencing is especially valuable for detecting rare populations, reconstructing developmental relationships, and understanding heterogeneous responses to infection, vaccination, or immunotherapy, according to An.

Immunogenomics applications

Immunogenomics shapes research and practice across precision medicine, cancer immunotherapy, vaccine development, infectious disease, transplantation, and autoimmune disease. “Immunogenomics allows us to unlock information regarding how our immune system functions when we are healthy, when we are sick, and how we may respond to different treatments, which can ultimately impact our individual standard of care and our widespread use of personalized medicine,” says O’Hara.

In infectious disease and vaccine research, scientists use immunogenomics to examine the immune response following vaccination or pathogen exposure.⁴ For example, single-cell V(D)J sequencing to analyze T-cell repertoires of patients with COVID-19 highlighted decreased T-cell receptor clone diversity in cases of disease. The researchers found certain VJ pairs that were increased or decreased in patients compared to healthy controls, leading to a greater understanding of the immune response against SARS-CoV-2.⁵

Autoimmune disease results from a breakdown of immune tolerance, which creates self-reactive T and B cells. Immunogenomic approaches can link immune cell populations to disease-associated variants and help identify abnormal clones, which helps researchers pinpoint what is going awry in the immune response. These diseases are often complex, and single-cell sequencing makes it possible to identify paired TCR and BCR chains alongside a cell’s gene expression profile.⁶

In transplantation, HLA typing and immune repertoire monitoring help assess donor-recipient compatibility, track immune reconstitution, and monitor for signs of rejection. Genotyping key NK receptors from donor tissue can also shed light on relapse risk after transplantation, as was shown in a recent long-read KIR genotyping study that linked donor KIR and HLA polymorphisms to post-transplant relapse in T-cell malignancies.⁷

A standout application of immunogenomics is within the field of oncology. “Immunogenomics can be used to learn more about the cancer itself by capturing the complex heterogeneity of the tumor microenvironment and identification of non-responsive cancer cells for additional targeting,” notes O’Hara. “It can also be used to assess predictive biomarkers to guide potential treatments or even as a means of identification and creation of new immunotherapies to target tumor-specific mutations to activate an immune response for personalized medicine.” For example, because higher tumor mutational burden (TMB) has been associated with improved response to immune checkpoint blockade in some settings, TMB has become one of several biomarkers used to guide treatment decisions.⁸

Future perspectives

With advancing technologies, researchers will continue to gain novel insights through immunogenomics. Already, the addition of spatial information is complementing single-cell data, allowing scientists to analyze immune cells within tissues.⁹ In the future, combining robust sequencing data with protein measurements, longitudinal sampling, and more will broaden the scientific community’s appreciation of the immune response during health, disease, and treatment.

The future of immunogenomics

“As we continue to learn more about the interplay of specific biomarkers, we will continue to develop more advanced and target drugs. Personalized medicine is already driving a number of therapeutics in oncology today but I think we will see a continued shift toward targeted therapies as we streamline the downstream targeted therapy development.”–Andrea O’Hara, Senior Product Manager of Multiomics and Synthesis Solutions at Genewiz

“Over the next five to ten years, immunogenomics is likely to move beyond describing immune-cell populations and receptor repertoires toward connecting clonotype, antigen specificity, functional state, tissue location and clinical outcome … Lower sequencing costs, greater multiplexing, and laboratory automation should also make larger, more diverse cohorts more practical. AI and machine learning will support multimodal data integration, cell-state classification, repertoire pattern discovery, and biomarker development, although strong study design and biological validation will remain essential.” –Shi-qi An, Director of Science Communications at Novogene Europe

References

1. Wang S, et al. A Scalable Framework for Comprehensive Typing of Polymorphic Immune Genes from Long‐Read Data. Advanced Science. 2026;13(22):e21531.

2. Robinson J, et al. The IPD and IMGT/HLA database: allele variant databases. Nucleic Acids Research. 2014;43(D1):423-D431.

3. Hou D, et al. High-Throughput Sequencing-Based Immune Repertoire Study during Infectious Disease. Frontiers in Immunology. 2016;7:336.

4. He J, et al. Research progress on application of single-cell TCR/BCR sequencing technology to the tumor immune microenvironment, autoimmune diseases, and infectious diseases. Frontiers in Immunology. 2022;13:969808.

5. Wang P, Jin X, Zhou W, et al. Comprehensive analysis of TCR repertoire in COVID-19 using single cell sequencing. Genomics. 2020;113(2):456-462.

6. Wang S, et al. Decoding autoimmune disease with single-cell immune repertoire and transcriptome sequencing: mechanisms and therapeutic opportunities. Frontiers in Immunology. 2026;17:1827384.

7. Morita M, et al. Long-read KIR genotyping reveals donor KIR/HLA polymorphisms linked to posttransplant relapse in T-cell malignancies. Blood Advances. 2026;10(13):4563-4572.

8. Chan TA, et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Annals of Oncology. 2018;30(1):44-56.

9. Ståhl PL, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353(6294):78-82.

About the author: Niki Spahich, Ph.D., earned her Ph.D. in genetics and genomics from Duke University, where she studied Haemophilus influenzae membrane proteins that contribute to respiratory infections. She later explored Staphylococcus aureus metabolism during her postdoctoral fellowship in the Department of Microbiology and Immunology at the University of North Carolina - Chapel Hill. In 2016, Niki co-founded Science Riot, a nonprofit dedicated to providing entertaining and engaging science outreach events, and in 2020, she launched The Scientist Speaks, a popular podcast series for researchers at the bench. Niki has led custom content creation for marketers seeking to reach life scientists since 2019.]]> Articles SEQadmin2 https://www.seqanswers.com/articles/327667-how-immunogenomics-decodes-immunity’s-genetic-blueprint https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327661-accessible-bioinformatics-for-windows-users-–-livestream-seminar Tue, 25 Aug 2026 05:53:58 GMT Hi everyone Instats is excited to offer a 5-day seminar, Accessible Bioinformatics for Windows Users...
Instats is excited to offer a 5-day seminar, Accessible Bioinformatics for Windows Users, livestreaming October 2nd - 6th and led by Andres Zhou Tsang from Adelaide University. Because many bioinformatics tools are designed for Linux while Windows remains the most common operating system on personal and institutional computers, this workshop provides a practical pathway for researchers who need to run Linux-based bioinformatics workflows without investing in a dedicated Linux machine or relying on cloud services. Participants will learn to install and configure a Windows-based bioinformatics environment using Windows Subsystem for Linux (WSL), while working with tools such as conda, Notepad++, R, Python, IGV, and FastQC to process, visualize, and assess sequencing data. Through hands-on sessions covering FASTA formats, basecalling, genome assembly, sequence alignment, RNA-seq, gene annotation, and differential expression analysis, this seminar equips Windows users with accessible, practical skills for conducting common bioinformatics analyses.

Sign up today to secure your spot, and feel free to share this opportunity with colleagues and students who might benefit!


Best wishes

Michael Zyphur
Professor and Director
Instats | instats.org​]]> Bioinformatics Instats https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327661-accessible-bioinformatics-for-windows-users-–-livestream-seminar https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327660-single-cell-data-analysis-3-0-–-livestream-seminar Tue, 25 Aug 2026 05:40:31 GMT Hi everyone Instats is excited to offer a 1-day seminar, Single Cell Data Analysis 3.0 (https://instats.org/seminar/single-cell-data-analysis-30),...
Instats is excited to offer a 1-day seminar, Single Cell Data Analysis 3.0, livestreaming September 9th and led by Nikolay Oskolkov from Lund University. As single-cell methods become central to biological and health research, mastering computational approaches for single-cell RNA sequencing data is essential for investigating cellular heterogeneity and advancing discovery in areas such as cancer research, developmental biology, and immunology. In this workshop, you’ll gain hands-on experience with state-of-the-art workflows for preprocessing, quality control, normalization, dimensionality reduction, clustering, visualization, differential expression analysis, and dataset integration using key tools in both R and Python. Nikolay Oskolkov’s practical approach emphasizes widely used frameworks such as Seurat and Scanpy, helping PhD students, academic researchers, and professionals develop the skills needed to move from raw single-cell data to meaningful biological interpretation.

Sign up today to secure your spot, and feel free to share this opportunity with colleagues and students who might benefit!


Best wishes

Michael Zyphur
Professor and Director
Instats | instats.org​​]]> Bioinformatics Instats https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327660-single-cell-data-analysis-3-0-–-livestream-seminar https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327658-machine-learning-for-computational-biology-–-livestream-seminar Tue, 25 Aug 2026 05:24:32 GMT Hi everyone Instats is excited to offer a 1-day seminar, Machine Learning for Computational Biology...
Instats is excited to offer a 1-day seminar, Machine Learning for Computational Biology, livestreaming September 8th and led by Nikolay Oskolkov from Lund University. As biological data continue to grow in scale and complexity, machine learning has become essential for analyzing and interpreting data in biostatistics, genetics, bioinformatics, ecology, genomics, and related fields. This workshop introduces key machine learning approaches for computational biology, including neural networks, random forests, k-means clustering, Gaussian Mixture Models, Markov Chain Monte Carlo methods, and autoencoders, while emphasizing practical implementation in R and Python. Through hands-on coding sessions and real-world case studies, participants will learn to build algorithms from scratch, troubleshoot and optimize models, and apply machine learning methods confidently to their own biological research questions.

Sign up today to secure your spot, and feel free to share this opportunity with colleagues and students who might benefit!



Best wishes

Michael Zyphur
Professor and Director
Instats | instats.org]]> Bioinformatics Instats https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327658-machine-learning-for-computational-biology-–-livestream-seminar https://www.seqanswers.com/forum/news/327657-giant-sunflower-genomes-advance-the-case-for-climate-resilient-perennial-crops Mon, 24 Aug 2026 18:32:10 GMT Researchers at the Land Institute and the HudsonAlpha Institute for Biotechnology have produced the first chromosome-scale, haplotype-phased genome... Researchers at the Land Institute and the HudsonAlpha Institute for Biotechnology have produced the first chromosome-scale, haplotype-phased genome assemblies for two wild North American prairie plants: Silphium integrifolium and Silphium perfoliatum. The work, published in Nature Communications, lays the genomic groundwork needed to domesticate these deep-rooted giant sunflower species into perennial crops.

The stakes are notable given how narrow the world’s food supply already is: more than half of all human calories come from just five crops—rice, wheat, corn, sugarcane, and barley. Perennial species like Silphium, which regrow for multiple years after a single planting, could add diversity and productivity to that supply while also benefiting the ecosystems they grow in.

According to senior author David Van Tassel, wild perennials “could help improve food security by contributing sustainability-enhancing traits such as deep roots,” and that genomic-informed breeding “will help generate new crop varieties more quickly than in the past,” even in “a stubborn genus that previously resisted genome assembly.”

The genomes themselves are enormous—7.6 gigabases for S. integrifolium and 7.5 for S. perfoliatum—with individual chromosomes among the largest recorded in plants, organized by an unusual helical DNA structure the team identified. Mapped genetic markers for traits such as drought tolerance, heat adaptation, and seed yield should let breeders domesticate Silphium faster while retaining its wild resilience. S. integrifolium in particular stands out as a potential oilseed crop, with seeds yielding 11.8 to 25.3 percent edible oil and containing squalene, used in vaccines and cosmetics. Ecologically, Silphium roots can reach 4.5 meters deep, helping stabilize soil, build organic carbon, and improve water infiltration and drought resilience.

First author Renan Souza explained that the urgency behind the work comes from “the growing instability of climate patterns and disruptions in global food supplies,” adding that the genomic framework “will help speed up the improvement of not only Silphium, but also other wild species with agricultural potential.”

The team also developed a DNA fingerprinting method breeders can use to select for traits like disease resistance and seed head size, and to identify genetic diversity hotspots. Next steps include scaling fingerprinting to thousands of plants annually and further studying the chromosomes’ unusual structure.]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327657-giant-sunflower-genomes-advance-the-case-for-climate-resilient-perennial-crops https://www.seqanswers.com/forum/news/327655-genomic-map-reveals-how-animal-chromosomes-evolve-along-fixed-paths Thu, 20 Aug 2026 19:17:09 GMT A study (https://www.science.org/doi/10.1126/sciadv.adz5561) published in Science Advances by researchers at the University of Vienna maps how genome... A study published in Science Advances by researchers at the University of Vienna maps how genome architecture has been reshuffled across the animal kingdom, showing that animal genomes evolve along a limited set of irreversible “evolutionary highways.” The findings offer a new basis for conservation of animal biodiversity.

Since all animals split from a common ancestor more than 600 million years ago, their chromosomes have fused, split and rearranged repeatedly. Thousands of animal genomes have now been sequenced, but most are “draft” genomes that list an animal’s genes without showing how they’re arranged. Chromosome-scale assemblies, which place every gene in order along complete chromosomes, are harder to produce. Only recently have enough of them existed to allow a comparison across the animal tree of life.

The team analyzed more than 5,800 publicly available chromosome-scale genomes spanning 4,454 species across 19 animal phyla, the largest such comparison to date. They built a new framework called evolutionary genome topology that projects this diversity onto a single map. The approach showed that genomes don’t change at random. Instead, hundreds of present-day species carry evidence of traveling along, or diverging from, shared evolutionary highways at different times and rates. “For the first time, we can see thousands of genomes on a single map and trace the unique paths along which animals’ DNA evolved,” said Darrin Schultz, who led the work. “And if we fold the map up in a different way, we can compare how different groups of animals took different paths from each other after splitting onto different evolutionary paths.”

Underlying these patterns is a process the team calls “fusion-with-mixing”: when two chromosomes fuse, their genes intermingle in a way that can’t be undone, leaving a permanent, one-directional record. Differences in chromosome number across animal groups trace back to either the combination or separation of ancestral chromosomes, and in both cases fusion-with-mixing pushes lineages onto distinct evolutionary paths, placing major animal groups in separate regions of “genome-architecture space” and leaving a lasting imprint on genes that control development.

Because the framework compares genome architecture rather than sequence alone, it gives researchers a shared coordinate system for the growing number of chromosome-scale genomes, useful for prioritizing unusual lineages and testing links between chromosome change and gene regulation, development or biodiversity. Some groups, including mosquitoes, glass sponges and earthworms, occupy isolated regions of the map with no close parallel, and the approach can also simulate possible future directions of genome evolution. “Understanding these rules of evolution doesn’t just tell us about the past,” said study co-leader Oleg Simakov. “It also lets us ask where genome evolution might go next and enables us to identify key measures for the conservation of animal biodiversity.”]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327655-genomic-map-reveals-how-animal-chromosomes-evolve-along-fixed-paths https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327654-ancient-environmental-metagenomics-aemg01-–-live-online-bioinformatics-course Thu, 20 Aug 2026 12:42:39 GMT Ancient Environmental Metagenomics (AEMG01) – Live Online Bioinformatics Course ... Ancient Environmental Metagenomics (AEMG01) – Live Online Bioinformatics Course

https://prstats.org/course/ancient-e...nomics-aemg01/

Learn how to analyse ancient environmental DNA (aeDNA) and metagenomic sequencing data using modern bioinformatics workflows for quality control, taxonomic profiling, authentication, decontamination, assembly, and genomic analysis.

Ancient environmental DNA recovered from sediments, ice cores, archaeological deposits, and other environmental archives provides unique opportunities to reconstruct past ecosystems, biodiversity, and environmental change.

However, these datasets present substantial computational challenges, including highly fragmented DNA, low sequencing coverage, complex mixtures of organisms, incomplete reference databases, post-mortem DNA damage, and modern contamination.

This practical course provides hands-on training in the bioinformatics methods needed to move from raw ancient environmental sequencing reads to authenticated taxonomic and genomic results.

WHAT YOU'LL GAIN

• Understanding of ancient environmental DNA and metagenomic sequencing workflows
• Practical experience with quality control, adapter trimming, host removal, and read processing using fastp and cutadapt
• Taxonomic profiling using Kraken and sourmash
• Skills in constructing and optimising reference databases for mammals, plants, microbes, fungi, and invertebrates
• Methods for authenticating ancient environmental DNA and taxonomic assignments
• Experience using mapDamage and PMDtools to assess post-mortem DNA damage
• Strategies for detecting and removing contamination using decontam, negative controls, and Recentrifuge
• Experience with metagenome de novo assembly and authentication of assembled contigs
• Practical use of the aeMeta workflow for ancient environmental metagenomics
• Application of PCA and UMAP to population genomic and ancient environmental datasets

COURSE FORMAT

• 25 hours of training
• Live online
• Hands-on bioinformatics using real-world datasets
• Practical exercises throughout
• Strong focus on complete, research-ready workflows

WHO IS THIS COURSE FOR?

• Bioinformaticians and computational biologists
• Ancient DNA and palaeogenomics researchers
• Metagenomics and environmental DNA researchers
• Evolutionary biologists and population genomic researchers
• Ecologists and palaeoecologists
• Microbiologists
• PhD students and researchers working with ancient or environmental sequencing data

WHY TAKE THIS COURSE?

Ancient environmental metagenomic data require specialised bioinformatics approaches that go beyond conventional metagenomic analysis.

Researchers must distinguish authentic ancient DNA from modern contamination, account for post-mortem damage and fragmented sequences, construct appropriate reference databases, and validate taxonomic assignments before drawing biological conclusions.

This course provides practical experience across the complete computational workflow, giving participants the skills needed to process, authenticate, analyse, and interpret ancient environmental metagenomic datasets.

COURSE DETAILS

Course dates: 23–27 November 2026

Duration: 25 hours

Format: Live online

Registration: £400

LEARN MORE & ENROL

https://prstats.org/course/ancient-e...nomics-aemg01/

QUESTIONS?

Email: oliver@prstats.org
]]> Bioinformatics PR Stats https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327654-ancient-environmental-metagenomics-aemg01-–-live-online-bioinformatics-course https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327653-genome-wide-association-studies-gwas-live-online-course Thu, 20 Aug 2026 10:49:29 GMT Genome-Wide Association Studies (GWAS) for Evolutionary Biology (GWAS01) – Live Online Course ... Genome-Wide Association Studies (GWAS) for Evolutionary Biology (GWAS01) – Live Online Course

https://prstats.org/course/genome-wi...iology-gwas01/

Learn how to perform genome-wide association studies (GWAS), from genomic data processing and quality control through to association testing, population structure correction, meta-analysis, visualisation, and polygenic prediction.

Genome-wide association studies are a cornerstone of modern genetics and genomics, enabling researchers to identify genetic variants associated with complex traits and investigate the genetic architecture of phenotypic variation.

This practical course provides hands-on training in complete GWAS workflows using widely adopted bioinformatics and statistical tools, including PLINK, SNPTEST, GATK, GWAMA, METAL, and LocusZoom.

WHAT YOU'LL GAIN

• Understanding of genetic variation and the principles of genome-wide association studies
• Practical experience with genotype quality control and filtering
• Skills in genotype phasing and imputation
• Association testing using PLINK and SNPTEST
• Genomic data processing and variant calling using GATK workflows
• Population structure analysis and correction using PCA
• GWAS meta-analysis using GWAMA and METAL
• Visualisation of association results using Manhattan plots and LocusZoom
• Construction and validation of polygenic risk scores
• Understanding of missing heritability and current approaches to complex trait genetics

COURSE FORMAT

• 5 days, 5 hours per day
• Live, instructor-led online training
• Hands-on genomic and bioinformatics analysis
• Practical exercises using real-world datasets
• Strong focus on complete, research-ready GWAS workflows

WHO IS THIS COURSE FOR?

• Bioinformaticians and computational biologists
• Population geneticists and evolutionary genomic researchers
• Researchers working with genotype and sequencing datasets
• Quantitative and statistical geneticists
• PhD students and researchers interested in GWAS and complex trait genetics

WHY TAKE THIS COURSE?

GWAS requires the integration of genomic data processing, rigorous quality control, population structure analysis, statistical association testing, and biological interpretation.

This course provides practical experience across the complete workflow, helping participants move from raw genomic data through variant processing and association testing to the interpretation and visualisation of genome-wide results.

COURSE DETAILS

Course dates: 9–13 November 2026

Duration: 25 hours

Format: Live online

Registration: £400

LEARN MORE & ENROL

https://prstats.org/course/genome-wi...iology-gwas01/

QUESTIONS?

Email: oliver@prstats.org]]> Bioinformatics PR Stats https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327653-genome-wide-association-studies-gwas-live-online-course https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327652-metagenomic-data-analysis-of-microbial-communities-mgmc01-–-live-online-course Thu, 20 Aug 2026 10:39:14 GMT Metagenomic Data Analysis of Microbial Communities (MGMC01) – Live Online Course ... Metagenomic Data Analysis of Microbial Communities (MGMC01) – Live Online Course

https://prstats.org/course/metagenom...nities-mgmc01/

Learn how to analyse shotgun metagenomic sequencing data using modern bioinformatics workflows for taxonomic profiling, functional analysis, and characterisation of complex microbial communities.

Shotgun metagenomics provides powerful approaches for investigating microbial communities directly from sequencing data, but analysing these datasets presents substantial computational and bioinformatic challenges.

This live online course provides practical, hands-on training in metagenomic data analysis, taking participants through the workflow from sequencing data processing and quality control to taxonomic and functional profiling and biological interpretation.

WHAT YOU'LL GAIN

• Understanding of shotgun metagenomic sequencing and analysis workflows
• Practical experience processing metagenomic sequencing datasets
• Skills in quality control and preparation of sequencing data
• Taxonomic profiling and characterisation of microbial communities
• Functional profiling of microbial genes and pathways
• Analysis and visualisation of microbial diversity and community composition
• Experience interpreting metagenomic outputs and biological patterns
• Confidence applying metagenomic workflows to your own sequencing datasets

COURSE FORMAT

• 5 days, 5 hours per day
• Live, instructor-led online training
• Hands-on bioinformatics and data analysis
• Practical exercises using metagenomic datasets
• Strong focus on applied, research-ready workflows

WHO IS THIS COURSE FOR?

• Bioinformaticians and computational biologists
• Metagenomics and microbiome researchers
• Genomics and NGS researchers
• Microbial ecologists and environmental microbiologists
• Researchers working with environmental or host-associated microbiomes
• PhD students and researchers wanting practical experience analysing shotgun metagenomic sequencing data

WHY TAKE THIS COURSE?

Metagenomic sequencing generates complex datasets requiring specialised computational approaches for quality control, taxonomic classification, functional profiling, diversity analysis, and downstream interpretation.

This course provides practical experience with the bioinformatics workflows needed to move from shotgun sequencing data to biologically meaningful descriptions of microbial community composition and functional potential.

COURSE DETAILS

Course dates: 26–30 October 2026

Duration: 5 days, 5 hours per day

Format: Live online

Registration: £400

LEARN MORE & ENROL

https://prstats.org/course/metagenom...nities-mgmc01/

QUESTIONS?

Email: oliver@prstats.org]]> Bioinformatics PR Stats https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327652-metagenomic-data-analysis-of-microbial-communities-mgmc01-–-live-online-course https://www.seqanswers.com/forum/news/327651-new-tool-estimates-cellular-age-from-gene-activity-patterns Tue, 18 Aug 2026 18:05:42 GMT Aging is a major risk factor for many diseases, but reliable methods for measuring how quickly cells age have been lacking. Now, researchers at... Aging is a major risk factor for many diseases, but reliable methods for measuring how quickly cells age have been lacking. Now, researchers at Karolinska Institutet and Stockholm University have developed a new tool called Pasta that estimates a cell’s biological age by analyzing its gene activity, and in a study published in Advanced Science, they also demonstrate how the tool can identify substances that influence cellular aging.

To build Pasta, the researchers analyzed gene activity data from more than 17,000 tissue samples taken from healthy people, then tested the model on several independent datasets. “Previous tools for measuring biological age often only work for a single tissue or type of data, which limited their usefulness. We built Pasta to be broadly applicable across many tissues, cell types and laboratory techniques, so that all research groups can apply it to the data they already have,” said first author Jérôme Salignon. “With this tool, we can track how cells change over time and gain insights into the mechanisms driving ageing,” Salignon added.

The researchers found that cells with high biological age often showed increased activity in genes linked to DNA damage and cellular stress, and the tool could distinguish between older, senescent cells and more youthful, stem cell-like cells. Pasta was then used to analyze more than three million gene profiles from public databases where cells had been exposed to thousands of drugs and genetic alterations, identifying substances and biological signaling pathways that appeared to increase or decrease cells’ biological age; some results were subsequently confirmed in laboratory experiments on human cells.

“Reliably determining the biological age of cells has long been a major challenge. We can now do this using gene expression data—a type of data that is already routinely generated in a great many research studies. I believe we are thus entering a new era in which biological age can be used as an experimental measure in many different types of studies. Pasta opens up entirely new possibilities for understanding the mechanisms behind ageing and for systematically searching for genes and substances that can influence it,” senior author Christian G. Riedel said.

Salignon added that the tool can help identify candidates for future treatments of age-related diseases and cancer. In laboratory experiments, the team validated two new candidates: pralatrexate, which accelerated cellular ageing, and piperlongumine, which made cells more youthful. He cautioned that the results are based on cell-based experiments, so further research is needed before they could be translated into patient treatments.
]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327651-new-tool-estimates-cellular-age-from-gene-activity-patterns