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HuBMAP Portal Offers Free Access to Single-Cell Data from Healthy Human Tissues

SEQadmin2 — Tue, 03 Nov 2026 19:54:25 GMT

Researchers and data scientists from dozens of universities, hospitals, and other institutions across the United States have created an online portal that offers free, open access to one of the most comprehensive atlases of the cells, tissues and organ systems of healthy human bodies. Described in Nature Communications, the federally funded portal is designed to help both human researchers and artificial intelligence efforts understand health and disease.

The portal serves as the online home for data from the Human BioMolecular Atlas Program (HuBMAP). This eight-year effort involved more than 600 consortium members at over 60 institutions. According to the researchers, the atlas draws on more than 9,000 datasets from over 500 healthy donors across dozens of organs and tissues, a combination of scope and scale that no other single resource currently offers.

Web-based tools in the HuBMAP Data Portal let researchers worldwide quickly analyze visual, spatial, and biomolecular data from the collection. Users can browse data by organ system, search for gene expression patterns and molecular signatures across tissues, and explore single-cell data across diverse cell populations.

“The HuBMAP program has generated an enormously rich resource for deepening our understanding of human health and disease,” said senior author Nils Gehlenborg from the Blavatnik Institute at Harvard Medical School. “The portal gives users access to the data and also provides tools they can use to explore and analyze the complex relationships between the trillions of cells that make up each healthy human body in a way that’s never been possible before,” he added.

HuBMAP adds to a growing set of biomedical atlases that capture the cellular and biochemical status of people with various diseases. The researchers said a healthy comparison is needed to build a complete picture of human health and disease. “To understand disease, you first need to know what healthy looks like,” said first author Morgan Turner.

According to Turner, the portal combines more than 25 types of molecular and imaging data from dozens of healthy organs and tissues. The data follow community-driven FAIR data and metadata standards, so they can be compared across labs, organs and donors. “Those standards are also what power the portal’s visualization and analysis tools and make the data ready for AI,” Turner said.

A key part of the effort was ensuring that HuBMAP samples, captured with a wide range of tools and technologies, could be compared in a scientifically sound way. The portal team worked with other consortium members to create community-driven standards for contributors to follow when sharing data. They then built standardized processing pipelines so that molecular and imaging data from many labs and technologies are processed uniformly and can be compared.

The portal also lets researchers judge whether the atlas holds information relevant to their questions without first downloading the massive datasets or using heavy computing resources. For more advanced work, users can download data directly and use the portal’s openly available metadata and programming interfaces. Protected data, such as sequencing data that could identify donors, require an approval process.

16S/ITS Profiling vs. Shotgun Metagenomics: What Changes in Your Workflow?

SEQadmin2 — Fri, 30 Oct 2026 14:46:00 GMT

NGS has revolutionized clinical microbiology by offering new ways to identify and surveil pathogens without prior knowledge of the target. One popular approach is 16S rRNA and ITS amplicon sequencing, which targets specific microbial markers. But studies are increasingly incorporating shotgun metagenomics, a technique that sequences all DNA from a sample without targeting specific microbial markers. This approach allows more detailed taxonomic classification, detection of antimicrobial resistance genes, and reconstruction of microbial genomes (metagenome-assembled genomes, MAGs).

Relying on the whole DNA of a sample, however, also makes shotgun sequencing more sensitive to host DNA, environmental contamination, and analytical choices. So, if you are transitioning from amplicon to shotgun sequencing, there are a few key considerations to keep in mind. In this article, you will learn about:

• The challenges of extracting microbial DNA and conserving DNA length.
• How to manage host background and control contamination.
• How to choose library preparation methods, sequencing technologies, and sequencing depth.
• When to use read-based classification or genome assembly, and how to assess the results.

The workflow challenges of shotgun metagenomics

Many of the workflow challenges of shotgun metagenomics stem from one key difference: “while targeted 16S/ITS amplification largely bypasses human DNA, in shotgun metagenomics host DNA competes directly for sequencing capacity,” explained Armaan Mehra, Product Manager at Novogene Europe.

Thus, in low-microbial-biomass specimens, “most reads may therefore provide little microbial information,” Dr. Mehra said. Insufficient microbial coverage can limit genome reconstruction and increase costs if deep sequencing is needed. He added that, with little microbial material in the sample, reagent and environmental contamination can be harder to distinguish from the true biological signal.

Careful choices around nucleic acid extraction, host depletion/targeted enrichment, sequencing, and analysis are therefore key.

DNA extraction for metagenomics: balancing lysis and DNA integrity

Effective microbial lysis allows for an accurate representation of a microbial community, as some microorganisms, including Gram-positive bacteria, are harder to lyse than others. Using harsher lysis buffers or mechanical disruption methods can help, but these methods also risk shearing DNA. This is a particular challenge of working with shotgun metagenomics, as “preserving longer DNA fragments is a priority for genome assembly and understanding genomic context,” explained Anna Maria Niewiadomska, Associate Director for Micro & Infectious Diseases at Oxford Nanopore Technologies (ONT). Lysis must therefore be balanced with DNA integrity.

According to Dr. Mehra, these are some important things to take into account to achieve effective nucleic acid extraction while ensuring DNA quality is preserved:

• Keep sample collection and storage consistent, and avoid unnecessary freeze–thaw cycles.
• Match extraction protocol to the sample matrix and downstream objective. For stool and other complex matrices, an appropriate bead-beating step can improve recovery of difficult-to-lyse organisms. Depending on the target organisms, enzymatic or chemical lysis may also help, like cell-wall-disrupting enzymes for fungal-rich samples.
• Include extraction blanks to detect contamination and whole-cell mock communities (mixtures of known microbes) to check whether the method recovers organisms as expected.
• Don’t judge extraction only by total DNA yield: much of that DNA could be human. Also check microbial recovery, fragment length, host-DNA proportion, and inhibitors that could interfere with downstream steps.

Managing host DNA in shotgun metagenomics

During nucleic acid extraction, host DNA is also liberated from cells. For instance, in different respiratory samples, 94.1%, 99.2%, and 99.7% of the reads were human. Reducing host reads can be tackled at different stages of the workflow, from extraction to sequencing itself.

Host DNA depletion

Dr. Niewiadomska explained that “Host depletion through selective host-cell lysis is a valuable technique for host-rich samples, although this can also affect the microbial composition recovered and thus should be carefully considered.” Dr. Mehra added that selective host-cell lysis can work well when intact microbial cells are the target but may also lose extracellular or cell-free microbial DNA. This distinction is crucial in plasma, cerebrospinal fluid (CSF), and other specimens where cell-free pathogen DNA can be informative.

Target enrichment: when to narrow your search

Instead of reducing host DNA, you can enrich your target DNA. Choosing between targeted and untargeted shotgun metagenomics is a “matter of breadth versus sensitivity,” noted Dr. Mehra. As he explained, untargeted sequencing is useful when the causative organism is unknown or unexpected organisms are important. Targeted enrichment becomes attractive when the organisms of interest are reasonably well defined but present at very low abundance relative to host DNA, for instance, when working with metagenomic surveillance.

There are different approaches to targeted shotgun metagenomics, including multiplex PCR and hybridization capture. “Multiplex PCR can provide very high sensitivity for a defined panel but is constrained by primer design and sequence variation,” said Dr. Mehra. Hybridization capture, where DNA or RNA probes bind and capture sequences of interest, can instead “target broader pathogen groups or genomic regions while retaining more sequence diversity,” making it useful for genome recovery, antimicrobial resistance (AMR) analysis, and surveillance. Other methods like CRISPR-based enrichment are also emerging.

Enrichment during sequencing with ONT adaptive sampling

For nanopore sequencing, enrichment can also happen during sequencing itself through what is known as adaptive sampling. “Thanks to the real-time sequencing capability of ONT, MinKNOW can decide whether to continue sequencing individual DNA molecules or not based on supplied reference information,” noted Dr. Niewiadomska. This can be especially useful when additional sample preprocessing or physical enrichment is undesirable, Dr. Niewiadomska added.

Planning sequencing depth and choosing a technology

Compared with amplicon profiling, where most reads target microbial markers, shotgun profiling may require substantially more sequencing to obtain enough microbial reads. Thus, when planning sequencing depth, “the amount of usable microbial sequence data can be more informative than total read numbers,” explained Dr. Niewiadomska. This should be considered after host removal.

Dr. Mehra added that the required depth also depends on the endpoint. A 2026 benchmark using complex mixtures of DNA from cultured bacteria found useful reference-based strain-level information at approximately 0.5–1 Gb. In contrast, de novo MAG reconstruction may require substantially deeper sequencing, depending on community complexity, microbial abundance, and analytical objectives.

Choosing short-read, long-read, or hybrid sequencing

In terms of the sequencing technology of choice, “short reads remain cost-efficient and provide deep multiplexed coverage,” which is especially important when working with large sample cohorts, noted Dr. Mehra. Their limitation, however, comes down to genomic context. Dr. Niewiadomska pointed out that, “Long reads can be valuable for assembling more complete microbial genomes, resolving plasmids and other mobile genetic elements, and understanding the genomic context of antimicrobial resistance genes.”

Some researchers also opt for a combination of both. Hybrid sequencing can be useful when both high-confidence base calls and long-range linkage are needed, “particularly for reference-quality genomes or complex plasmids,” explained Dr. Mehra.

Shotgun metagenomics analysis: read classification and genome assembly

Bioinformatic analysis can be one of the most time-consuming parts of shotgun metagenomics due to the volume and complexity of the data generated, especially when reconstructing MAGs. Genome assembly requires piecing reads together into longer sequences, followed by binning and quality assessment.

When to use read-based classification

But if your goal is to identify which microorganisms are present, read-based classification can be a good option. “Read-based classification is particularly useful when speed, sensitivity, or low-abundance detection matters, because organisms can be identified directly from sequence reads without waiting for successful genome assembly,” explained Dr. Mehra. This is the case for pathogen detection or surveillance workflows, where requiring successful assembly could exclude organisms present at low abundance.

Dr. Mehra noted that established tools include Kraken2 with Bracken for classification and abundance refinement, and MetaPhlAn 4 or mOTUs for marker-based community profiling. However, “for a clinically relevant low-abundance signal, read count alone is insufficient,” Dr. Mehra added. Confidence increases when reads map uniquely across multiple regions of a reference genome, show appropriate breadth and depth of coverage, and are absent from negative controls.

Read-based classification and MAG reconstruction can also be combined to get the best of both. As Mehra explained, “read classification can identify organisms too rare to assemble, while MAGs provide genomic context for strain variation, functional genes, and mobile elements.” In a recent pilot study using nanopore sequencing to investigate bloodstream infections, initial taxonomic assignments were obtained within approximately one hour of sequencing, but runs continued for up to 72 hours to improve genome coverage, assembly continuity, and reconstruction of plasmid-associated sequences. The authors noted that extended sequencing primarily enhanced genomic completeness and contextual analyses but did not alter initial species identification or major AMR predictions.

Assessing MAG quality beyond completeness and contamination

When reconstructing genomes, both Dr. Mehra and Dr. Niewiadomska agreed that MAG quality needs to be assessed using several complementary metrics. “These include completeness, contamination, fragmentation, and assembly continuity,” noted Dr. Niewiadomska. Data show that passing thresholds such as >90% estimated completeness and <5% contamination does not automatically make a MAG biologically reliable. As Dr. Mehra noted, complementary checks could include GUNC for taxonomic chimerism; genome size; contig count; N50 and read-mapping coverage for fragmentation and structural consistency; and GTDB-Tk and ANI comparisons for taxonomic context.

About the author: Nuria M. Wentzien got her Ph.D. from the University of Granada in 2023. After finishing her Ph.D., she discovered that science communication brings together her two favorite worlds: science and writing. Her career reflects her passion for learning and exploring new topics, from a Bachelor’s degree in Biochemistry (with a strong focus on human sciences) to a Ph.D. in sustainable agriculture. She brings this same curiosity to every project, making readers feel as inspired by science as she does. Now dedicated to helping life-science companies communicate rigorous science in an engaging way, she is the co-founder of Helixa Communications.

Genome Sequencing Reveals Large, Repeat-Rich DNA in Trapdoor Spider Named for Stephen Colbert

SEQadmin2 — Wed, 28 Oct 2026 17:57:53 GMT

Researchers at the University of California, Davis, have sequenced the genome of Aptostichus stephencolberti, a trapdoor spider about the size of a quarter, and found that it is roughly 50% larger than the human genome. The spider is the first member of its family to have its genome sequenced, according to corresponding author Jason Bond. The study appears in the Journal of Heredity.

“At 4.6 gigabases, or 4.6 billion DNA base pairs, it’s not the largest genome among the spiders, but it’s significant,” said Bond.

The genome is also complex, with 52 chromosomes compared with 23 in humans. Most of its size, 77%, comes from repetitive sequences copied many times across the genome rather than from extra genes. Like all spiders, this one has no telomere repeats, the sequences that protect chromosome ends. That absence may account for why spider chromosomes evolve faster than those of vertebrates.

“It lends itself to broadening our understanding of spider genome composition and consequently how genomes have evolved across the animal Tree of Life,” Bond said.

The spider got its name in 2008. Comedian Stephen Colbert found out that Bond had named a trapdoor spider after rock musician Neil Young and, on his “Who’s Not Honoring Me Now?” segment of “The Colbert Report,” demanded a spider of his own. Bond agreed.

A. stephencolberti lives only in coastal dunes in Central California, from the San Francisco Bay to Monterey, where beachgoers often come near it without knowing. Females spend their whole lives in silk-lined burrows sealed by a hinged, camouflaged door, rushing out to grab prey that come too close. The spider’s venom poses no danger to humans. Bond noted that only 30 to 50 of the more than 50,000 known spider species have venom known to be toxic to people.

The sequencing is part of the California Conservation Genomics project, a state-funded effort to build a genomic dataset for conservation science. Trapdoor spiders seldom travel far, and their dune habitat is fragmented, so genomic data can help identify distinct populations that may need protection. Rising sea levels threaten their narrow range.

Single-Cell Sequencing of 5 Million Immune Cells Links Genetic Variants to Disease

SEQadmin2 — Wed, 28 Oct 2026 17:51:03 GMT

A team at the Garvan Institute of Medical Research, working with the Murdoch Children’s Research Institute, UNSW and other partners, has published three studies in Nature that tie genetic risk regions for 16 diseases to possible causes in specific immune cells. Together, the papers identify nearly 1,500 genes that may contribute to conditions including cancers and immune and neurological disorders.

The studies are the first major output of TenK10K, a project that aims to map genetic variation and gene activity in immune cells from 10,000 people. Reading a person’s DNA has become far easier over the past three decades. Working out how sequence differences lead to disease, however, requires large amounts of single-cell data, which have been difficult to generate and analyze until now.

“In the first phase of our TenK10K project, we’ve sequenced the genomes of nearly 2,000 Australians and used the latest single-cell technologies to measure gene activity in more than 5 million of their immune cells,” says Professor Joseph Powell, senior co-author on all three papers. “That let us trace tens of millions of genetic variants to the specific cell types they act in and link them to 16 diseases and 44 blood characteristics, to identify likely mechanisms that are driving that disease.”

Each paper looks at a different layer of gene regulation. The first study examines how common and rare variants affect gene activity in individual immune cell types. Across 21,000 genes, the team found more than 180,000 effects on gene expression. Nearly 5,000 of the affected genes, about a quarter, had not been linked to a genetic effect on their activity in the largest previous whole-blood study. Comparing the results with genetic studies of 16 diseases revealed 893 genes tied to diseases or blood traits whose activity had not previously been connected to genetic variation. Many of these effects appeared only in specific cell types, which may be why whole-blood studies missed them.

The second study focuses on tandem repeats. These repetitive DNA sequences help regulate genes and take up more of the genome than all protein-coding genes, but they have long been too difficult to analyze accurately. Using new methods for analyzing whole-genome sequencing data, the researchers found more than 69,000 tandem repeat variants associated with changes in gene expression. They also found over 200 genes where repeats may help explain genetic links to immune and blood traits.

The third study maps chromatin accessibility, meaning how open or tightly packed DNA is in a cell, which controls which genes can be switched on or off. The team identified more than 240,000 variants linked to chromatin accessibility and connected more than 28,000 of these regions to the genes they help regulate. Looking at chromatin accessibility explained the likely role of up to 23% more disease-linked genetic regions than looking at gene activity alone.

Powell gave one example of what the data can show. “For example, we found a genetic variant that increases the expression of a gene called OSM in natural killer immune cells and increases the risk of developing irritable bowel disease. This uncovered a specific immune pathway that could potentially be targeted with future treatments.”

“We’re excited to see how this resource can be leveraged by other scientists and organizations to guide new therapies and deliver better care for patients. And this is just the start. In the next phase, we’ll be significantly expanding the number of people and disease areas analyzed in TenK10K and increasing the genetic diversity of participants so that our findings can be representative of more people around the world,” says Powell.

Ancient DNA Analysis (ADNA01) – Live Online Course

PR Stats — Wed, 28 Oct 2026 14:57:52 GMT

Ancient DNA Analysis (ADNA01) – Live Online Course

Dates: 26–30 October 2026
Duration: 5 days, 25 hours
Instructor: Dr Nikolay Oskolkov
Format: Live online, with recordings available
Registration fee: £400

PR Stats is pleased to announce a practical course on the computational analysis of ancient DNA, covering bioinformatics workflows, ancient DNA authentication, metagenomics and population genomic analysis.

Ancient DNA provides powerful insights into evolutionary history, population dynamics, migration, domestication and gene flow. However, degraded DNA, low sequencing coverage and contamination present significant challenges for genomic analysis.

This workshop provides hands-on training in processing ancient genomic datasets, from raw sequencing reads through quality control and authentication to population structure, ancestry inference and evolutionary interpretation.

Topics covered include:

Quality control, adapter trimming and read processing using fastp
Alignment of ancient DNA sequencing reads using BWA and Bowtie2
Taxonomic profiling of ancient metagenomic datasets using Kraken and sourmash
Contamination assessment and ancient DNA authentication
DNA damage analysis using mapDamage and PMDtools
Probabilistic variant calling using ANGSD
Population structure analysis using PCA, t-SNE and UMAP
Ancestry inference using ADMIXTURE and STRUCTURE
F-statistics (F2, F3 and F4) and D-statistics for investigating admixture and gene flow
Building complete ancient DNA analysis workflows

Who should attend?

Bioinformaticians, evolutionary biologists, population geneticists, archaeogeneticists, microbiologists, postgraduate students and researchers working with ancient genomic or metagenomic datasets.

The course combines theoretical instruction with practical exercises using real-world datasets.

Course format:

Live, instructor-led online training
Hands-on computational exercises
All sessions recorded, with 30 days of access
Course materials and datasets provided
30 days of post-course instructor email support

Full course details and registration:

https://prstats.org/course/ancient-dna-analysis-adna01/

Please feel free to share this announcement with colleagues, laboratories, research groups, institutions and wider genomics and bioinformatics networks.

LIVE ONLINE COURSE: Ancient Microbiome and Pathogen Genomics (AMPG01)

PR Stats — Wed, 28 Oct 2026 14:34:19 GMT

Dates: 11–15 January 2027
Duration: 25 hours (5 days)
Instructor: Dr Nikolay Oskolkov
Format: Live online, with recordings available
Registration fee: £400

PR Stats is pleased to announce a practical, instructor-led course on the computational analysis of ancient microbiomes and pathogen genomes.

Advances in ancient DNA sequencing have made it possible to reconstruct microbial communities and investigate pathogen evolution from archaeological and environmental samples. However, ancient microbial DNA presents significant analytical challenges, including degradation, low sequencing coverage, contamination and incomplete reference databases.

This workshop provides hands-on training in bioinformatics workflows for analysing ancient microbial and pathogen DNA, from raw sequencing reads to biological interpretation.

Topics covered include:

Quality control, adapter trimming and read processing using fastp and cutadapt
Taxonomic profiling using Kraken and sourmash
Construction and optimisation of microbial reference databases
Ancient DNA authentication using mapDamage and PMDtools
Contamination detection and removal using decontam and Recentrifuge
Reference database decontamination using GENEX
Metagenomic assembly and authentication of reconstructed microbial genomes
Practical ancient metagenomic analysis using the aMeta workflow
Microbial population genomics and community structure analysis using PCA and UMAP

Who should attend?

Bioinformaticians, archaeogeneticists, microbiologists, evolutionary biologists, infectious disease researchers and postgraduate students interested in ancient DNA, metagenomics and pathogen evolution.

The course combines theoretical instruction with practical exercises using real-world datasets.

All sessions will be recorded, allowing participants across different time zones to follow the course. Recordings will remain accessible for 30 days.

Full course details and registration:

https://prstats.org/course/ancient-m...nomics-ampg01/

Please feel free to share this announcement with colleagues, laboratories, research groups, institutions and wider genomics and bioinformatics networks.

Live Online GWAS Workshop for Evolutionary Biology | 9–13 November 2026 | PR Stats

PR Stats — Wed, 28 Oct 2026 12:57:38 GMT

LIVE ONLINE COURSE: Genome-Wide Association Studies (GWAS) for Evolutionary Biology (GWAS01)

Only 5 places left!

Dates: 9–13 November 2026
Duration: 25 hours (5 days)
Instructor: Dr Nikolay Oskolkov
Format: Live online, with recordings available
Registration fee: £400

PR Stats is pleased to announce an upcoming practical workshop on Genome-Wide Association Studies (GWAS), designed for researchers working in evolutionary biology, population genetics, genomics, bioinformatics, and related disciplines.

The course provides hands-on training in GWAS methodologies, taking participants from genomic data processing and quality control through association testing, population structure analysis, meta-analysis, and polygenic prediction.

Topics covered include:

Genotype phasing and imputation using PHASEIT2 and IMPUTE2
Genotype quality control and filtering
Association testing using PLINK and SNPTEST
GWAS meta-analysis using GWAMA and METAL
Next-generation sequencing workflows with GATK
Population structure analysis and PCA
Manhattan plots and LocusZoom visualisation
Polygenic risk scores and missing heritability

The course combines theoretical foundations with practical exercises and real-world genomic datasets.

All sessions will be recorded, allowing participants in different time zones to follow the course. Recordings will remain accessible for 30 days after the course.

Full course details and registration:
https://prstats.org/course/genome-wi...iology-gwas01/

Please feel free to share this announcement with colleagues, research groups, laboratories, students, and anyone working in evolutionary genomics or related fields.

Online Workshop: A Practical Introduction to NGS Data Analysis and Variant Calling | November 23–25, 2026

ecSeq Bioinformatics — Mon, 26 Oct 2026 10:20:35 GMT

Are you new to NGS data analysis and want to learn how to process and interpret sequencing data yourself?

We are organizing a 3-day, hands-on online workshop on NGS Data Analysis and Variant Calling, taking place November 23 - 25, 2026.

The course is designed for biologists, PhD students, postdocs, and researchers with little or no prior experience in bioinformatics. What will you learn?

Working with real Illumina sequencing data, participants will gain practical experience in:

Linux basics: Essential command-line tools for bioinformatics
Quality control: FASTQ files, FastQC, adapter trimming and read preprocessing
Read mapping: Aligning sequencing reads using BWA-MEM, Bowtie2 and other tools
Alignment analysis: Working with SAM/BAM files, samtools and mapping statistics
Data visualization: Exploring aligned reads in genome browsers
Variant calling: Identifying, filtering and interpreting DNA variants using VCF files

Course format

The workshop combines lectures with extensive hands-on exercises. Participants work directly on high-performance cloud computers through their web browsers.

No previous Linux or programming experience is required. No software installation is necessary.

Experienced bioinformaticians provide live support throughout the practical exercises. Workshop details

Dates: November 23 - 25, 2026
Time: 09:00 - 17:00 CET (UTC+1)
Location: Online, live and interactive
Language: English
Registration fee: 879 EUR (excluding VAT)
Participants: Maximum 25
Registration deadline: November 22, 2026

The fee includes access to our cloud computing infrastructure, printed course materials, downloadable training resources and a certificate of participation. Registration and full programme

Workshop Website

The workshop is organized by ecSeq Bioinformatics GmbH, a German bioinformatics company specializing in NGS data analysis, consulting and training.

We would be happy to welcome you to the course. Please feel free to share this announcement with colleagues or students who might be interested.

weird Q30 drop in the recent nextseq run

allenyang2003 — Fri, 23 Oct 2026 15:53:24 GMT

This is the first time I noticed the Q30 drop in the middle of the cycles, but goes up at the end of the run. Does anyone have experience with this? is this the primer dimer or adapter dimer issue?
Thanks in advance!

The library is bulk RNA libary. So the diversity should not be the problem.

Exeter-Led Study Maps What an Average Human Genome Looks Like

SEQadmin2 — Thu, 22 Oct 2026 19:13:38 GMT

A new study from the University of Exeter has characterized what counts as an “average” human genome, offering a reference point for normal genetic variation and genetic disease risk. Published in Genome Medicine, the research drew on nearly one million whole genome sequences from two major population biobanks, UK Biobank and the U.S.-based All of Us program, funded by the Medical Research Council, Wellcome and the NIHR Biomedical Research Centre: Exeter.

The analysis points to an uncomfortable conclusion: based on genetics alone, virtually anyone, whatever their current health, could be flagged as at risk for multiple conditions. Each genome, the team found, contains roughly five million variants, about 10,000 of which alter gene sequences—ranging from traits like lactose intolerance to drug response, though most such changes are biologically neutral. Everyone also carries one to three rare variants previously linked to serious genetic disease, plus around 50 more of uncertain clinical significance.

The team also calculated polygenic risk scores—which combine many genetic factors into an overall disease-risk estimate—across 28 common conditions, including diabetes, cardiovascular disease and schizophrenia, and found most participants fell into the high genetic risk category for about three of them. Since the researchers didn’t assess participants’ actual health status, many of those flagged this way may have no disease at all, underscoring that such results can reflect ordinary genetic variation rather than a looming diagnosis, with other factors playing a major role in whether disease ever develops.

Senior author Professor Caroline Wright said the findings fill a gap in how genomic results get interpreted: “Whole genome sequencing has become such an important tool in diagnosing genetic disease, and we’re increasingly moving toward genetic screening for predicting disease. Yet most people are unaware of just how much genetic variation is present in every genome. Actually, variation is the norm.”

Both biobanks skew toward participants of Western European ancestry, and the researchers note a continued need for more diverse population data. Among participants recruited alongside their parents, the team also found around 63 new, or “de novo,” variants in each genome that weren’t present in either parent, arising mostly from mutations in fathers’ sperm and, to a lesser extent, mothers’ eggs. The number of these new variants rose with paternal age, by about 1.2 additional mutations per year —a finding the researchers say carries implications for reproductive decision-making.

Wright added that context remains essential when acting on genetic test results: “Importantly, we found that everyone carries a small number of genetic variants linked to disease, although many of these will not actually cause disease in that individual. It is therefore crucial to consider context when interpreting findings from genetic testing, including symptoms or family history of disease as well as other clinical and environmental risk factors. Our findings also provide important context for professionals who are interpreting genetic testing results or commissioning genetic screening programs.”

Long-Read Sequencing Uncovers Cancer Mutations Conventional Testing Missed

SEQadmin2 — Thu, 22 Oct 2026 18:48:44 GMT

The University of Michigan’s Oncology Sequencing Program, known as MiOncoSeq, analyzes DNA and RNA from patients’ tumors and healthy tissue to find genetic alterations that can inform treatment. Two new studies from the team, one published in Cancer Discovery and another in the New England Journal of Medicine, show how adding long-read nanopore sequencing to that process can surface genetic changes that conventional sequencing methods overlook entirely.

Long-read sequencing makes it possible to see how complex genetic changes are arranged relative to each other, something conventional methods often can’t resolve. As Arul Chinnaiyan, senior author on both papers, put it: “Having a list of mutations doesn’t always explain a tumor. We need to understand how these mutations are arranged, which conventional sequencing often cannot resolve.” Since most genes exist in two copies, one from each parent, this arrangement matters: nanopore sequencing can reveal whether one or both copies of a cancer-protective gene have been disabled through complex rearrangements or chemical changes, or whether several mutations have stacked up on a single copy of a cancer-promoting gene in ways that boost its activity or fuel drug resistance.

In one part of the Cancer Discovery study, the team screened 768 patients with prostate, breast or ovarian cancer and found 46 tumors showing signs of genomic instability that conventional sequencing couldn’t explain; long-read sequencing resolved 32 of those by uncovering hidden alterations or chemical modifications in protective genes. A broader screen of 4,496 tumor samples from 3,688 MiOncoSeq patients used nanopore sequencing in select cases to map how mutations were arranged in genes including NOTCH1, PDGFRB, KIT and PIK3CA, work the team says can clarify why some patients develop drug resistance and inform treatment decisions. The approach also caught an inherited risk a commercial multigene panel had missed: a woman with a strong family history of breast and ovarian cancer, who had tested negative on that panel, was found through nanopore sequencing of a blood sample to carry a disease-causing insertion in BRCA2, a DNA-repair gene—a finding the researchers hope could help guide genetic counseling for relatives in similar unexplained cases.

The second study traced a rare tumor that appeared beside the spine of a 3-year-old boy 26 months after he received gene therapy for Krabbe disease, an inherited disorder affecting the protective coating of nerve cells. The therapy, delivered after a stem cell transplant, used a modified virus to supply a working copy of the gene missing in Krabbe disease. Nanopore sequencing showed that a fragment of that viral DNA had inserted into the GLI1 gene, carrying genetic switches that drove abnormally high GLI1 activity capable of promoting tumor growth. At the time of the report, the child had no tumor-related symptoms and was being monitored with repeat imaging. “Gene therapy holds great promise, and understanding complications like this can help guide safer design,” Chinnaiyan said. “It is also important to monitor patients over the long term after they receive treatment.”

Three last-minute places available: 2nd Berlin Winter School in RNA-Seq Data Analysis, November 2–5, 2026

ecSeq Bioinformatics — Mon, 19 Oct 2026 12:40:27 GMT

Three participants have just cancelled! 3 places are now available for this fully booked workshop. Register now to secure one of these spot!

More information and registration

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

Mayo Clinic-Led Study Detects DNA Mutations Across Noncancerous Tissues

SEQadmin2 — Thu, 15 Oct 2026 18:52:55 GMT

A Mayo Clinic-led study has shown how to detect a broad range of DNA mutations in noncancerous human tissues and across the genome. The analysis, drawing on more than 90 trillion DNA letters of sequencing data, could help scientists study when these changes arise, from early development through aging, and how they contribute to cancer and other diseases.

Published in Cell, the study is the flagship paper in a package of roughly a dozen publications from the NIH Common Fund’s Somatic Mosaicism across Human Tissues, or SMaHT, Network. Researchers evaluated approaches for detecting somatic mutations ranging from single-letter DNA changes to large deletions, insertions and rearrangements.

“We often think of the human genome as a single, fixed sequence, but every person is a mosaic of cells that acquire genetic changes over time,” explained Alexej Abyzov, corresponding author of the Cell study. “As we have increasingly recognized in recent years, these mutations are relevant not only to cancer but also to a broad range of age-related diseases.”

Unlike inherited genetic changes, somatic mutations arise after conception and accumulate over a lifetime. As cells divide, they can pass those changes to new cells, creating genetic differences within the same person known as somatic mosaicism. Some somatic mutations can alter how cells behave and contribute to cancer, aging, and neurodevelopmental, cardiovascular and autoimmune diseases.

These mutations can be difficult to detect when they are rare or occur in complex, repetitive regions of the genome, so the SMaHT Network compared short- and long-read whole-genome sequencing, duplex sequencing, single-cell sequencing, and computational approaches. Long-read sequencing produced one of the clearest findings, detecting about twice as many large DNA changes as short-read sequencing and finding insertions that shorter reads often missed. A more complete genome reference also raised precision in one benchmark from 79% to 89%, and single-cell sequencing of 102 lung and colon cells revealed cell-to-cell differences that helped trace their history back to early development.

Dr. Abyzov also co-led a companion SMaHT paper published in The American Journal of Human Genetics, in which deep whole-genome sequencing accurately detected rare, single-letter DNA changes present at levels below 1% in a tissue sample. Analyzing DNA from different organs helped detect mutations that arose early in development and spread to multiple tissues; combining lung and colon data uncovered 13 additional shared mutations, improving detection by 145% for these very rare mutations compared with analyzing either tissue alone.

SMaHT aims to catalog somatic mutations across 10 to 15 tissue types from 150 donors while improving the tools used to detect them. NIH is planning a second stage focused on understanding how these mutations influence biological processes and disease. Dr. Abyzov says the research could eventually help identify people at higher cancer risk and track precancerous changes in tissues before they develop symptoms.

Multiomic Liquid Biopsy: What DNA/RNA Panels Reveal That DNA Alone Can’t

SEQadmin2 — Wed, 14 Oct 2026 17:55:00 GMT

Tumors contain complex mixtures of cell populations that evolve in response to treatment and host-specific factors. Tissue biopsies provide only static snapshots of a tumor’s state. But what if we could follow that evolution over time using a minimally invasive technique?

Liquid biopsies can detect tumor-derived material circulating in the bloodstream, providing a way to study treatment response, detect emerging resistance, and discover new biomarkers. Circulating tumor DNA (ctDNA), a fraction of cell-free DNA (cfDNA), is among the most widely studied analytes. But researchers are increasingly interested in adding other omic layers. Cell-free RNA (cfRNA), which includes RNA carried in extracellular vesicles, is one of them, and it helps researchers connect genotype with gene expression and activity within the tumor microenvironment.

However, cfDNA and cfRNA are often present at low concentrations, exhibit distinct stability profiles, and differ in their biological and clinical interpretation. The advantages of combining cfDNA and cfRNA in liquid biopsy can be hampered by the added complexity of the workflow.

In this article, we will cover:

The complementary value of analyzing DNA, RNA, and proteins in liquid biopsy samples
The technical challenges of combining multiple liquid biopsy analytes
How to balance analyte-specific requirements with streamlined workflows
What the future of multiomic liquid biopsy could look like

What RNA adds to DNA-based liquid biopsy

Analyzing ctDNA within the cfDNA pool provides useful information about “tumor-specific genomic alterations, such as mutations, copy-number changes, and emerging resistance-associated variants,” said Dr. Khatereh Motamedchaboki, Sr. Global Manager in Business Development, Life Science Mass Spectrometry, Omics at Thermo Fisher Scientific. This makes ctDNA a powerful tool to monitor tumor evolution and clonal selection during therapy.

But detecting a genomic alteration does not mean it is actively expressed. Adding cfRNA to liquid biopsy panels helps connect these alterations to gene expression and provides a more dynamic view of the tumor functional state, as Dr. Jonathan Shaffer, Head of NGS R&D at QIAGEN, explained.

Gene expression changes also provide information about cellular identity that driver mutations cannot. Dr. Emily Leproust, CEO and Co-founder of Twist Bioscience, highlighted lineage plasticity, in which tumor cells alter their gene expression programs to escape treatment, as seen in neuroendocrine transformation in EGFR-driven lung cancer or prostate cancer.

Combining cfDNA and cfRNA can be very useful to detect cancer-driving gene fusions and abnormal splicing events that produce oncogenic proteins, as Dr. Leproust and Dr. Shaffer highlighted. Fusion breakpoints can occur within large introns and vary significantly between individuals, making them difficult to predict based on DNA sequence alone. In contrast, as Dr. Leproust explained, the skipped transcript provides one clear, consistent signal, as seen with MET exon 14 skipping.

By bypassing these large, repetitive introns, cfRNA “compresses the target space and reduces the sequencing reads needed to detect complex structural variants,” said Dr. Shaffer. He added that each mutated DNA copy can generate 20–2,000 mRNA copies, and thus adding cfRNA could increase assay sensitivity.

However, to enjoy the benefits of working with cfDNA and cfRNA for liquid biopsy, labs need to manage a more complex workflow that preserves both analytes and delivers results within a sufficiently short turnaround time.

Combining cfDNA and cfRNA: key workflow challenges

The integration of cfDNA and cfRNA in liquid biopsy means working with analytes that show very different stability profiles. On top of that, sample material is often limited, and tumor-derived signals can be scarce, occurring at variant allele frequency (VAFs) as low as 0.1%. Labs need to take the specific characteristics of each analyte into account while combining them in a workflow that remains efficient.

Sample collection and handling to preserve both cfDNA and cfRNA

One of the main workflow challenges starts right after sample collection. As Dr. Markus Sprenger-Haussels, Vice President R&D, PreAnalytiX – A QIAGEN and BD Joint Venture, explained, stabilizing samples and processing them as soon as possible are key to preserving the analytes being studied. He stressed that delayed processing of unstabilized blood samples leads to leukocyte lysis and release of genomic DNA into the plasma, increasing background DNA, reducing the relative fraction of ctDNA, and thus affecting assay sensitivity and interpretation.

For cfRNA, the challenge is even more complex, as beyond degradation, “ongoing cellular and biological processes after blood collection can alter the expression profile of the sample,” according to Dr. Sprenger-Haussels.

Both Dr. Leproust and Dr. Sprenger-Haussels also emphasized the importance of standardized handling from collection through plasma storage, including:

Using collection tubes and stabilization procedures validated for the intended analytes.
Controlling collection-to-processing times and transport temperatures.
Minimizing residual cells and platelets during plasma separation.
Aliquoting plasma before freezing and avoiding repeated freeze–thaw cycles.

When prompt processing is not possible, validated stabilization options can extend the handling window. PAXgene Blood ccfDNA Tubes (RUO*) allow whole blood to be stored for up to 10 days at temperatures up to 25°C before plasma separation for cfDNA analysis. Emerging data also suggests that plasma from these tubes can support cfRNA extraction and downstream analysis with appropriate extraction and analytical workflows.

cfDNA and cfRNA extraction: together or not?

After plasma preparation, the main decision comes down to extracting cfDNA and cfRNA together or separately. Dr. Sprenger-Haussels explained that co-extraction saves sample volume and simplifies the workflow, making it advantageous when working with limited material.

However, extraction conditions optimized for one analyte can reduce the recovery of the other or bias fragment sizes, as cfDNA and cfRNA show distinct fragment and structural profiles. The workflow should also balance recovery with inhibitor elution.

QIAGEN approaches co-extraction with the QIAamp ccfDNA/RNA Kit, a two-stage silica-column workflow that can co-isolate cfDNA and cfRNA from 1–4 mL of plasma or serum, helping conserve limited sample. Protein precipitation and successive silica-column wash steps reduce contaminant carryover, and the final eluate is concentrated with a MinElute cleanup.

Still, Dr. Sprenger-Haussels recommends monitoring DNA and RNA separately. Key controls include:

Recovery: DNA and RNA spike-ins added before extraction, spanning relevant fragment sizes.
Contamination: a full-process negative control and a characterized positive sample.
cfDNA: quantify with a sensitive method such as qPCR or dPCR, check fragment sizes and high-molecular-weight DNA contamination by capillary electrophoresis, and assess amplifiability.
cfRNA: check reverse-transcription performance and detection of both short and longer targets. Include a no-RT control to identify DNA-derived signal.
Downstream suitability: monitor sequencing performance; include low-frequency variant, fusion or expression controls where relevant.

When sample volume is less limited, or when cfDNA and cfRNA concentrations need to be adjusted independently for downstream analysis, Dr. Sprenger-Haussels recommends sequential recovery using dedicated purification technologies, such as the QIAamp MinElute ccfDNA Kit and the exoRNeasy Kit for extracellular-vesicle-associated RNA.

The decision ultimately comes down to the biological question, the available sample volume, the specific DNA and RNA populations to study, and the downstream assay requirements.

Library preparation: balancing sensitivity and workflow efficiency

At the library preparation stage, labs again face a choice: prepare cfDNA and cfRNA libraries together or separately.

Dr. Leproust explained that separate library preparation offers better control over each analyte’s particularities, which can be especially useful when working with very low-input samples or when end repair and adapter ligation are needed to capture short cfDNA fragments. Some applications that benefit from independent library preparation are those where sensitivity is key, such as minimal residual disease (MRD) or low-VAF detection, or when adding cfRNA to an already optimized cfDNA workflow.

The trade-off is extra processing and sample requirements. To limit this, Dr. Leproust recommends workflows optimized for each analyte, so laboratories can meet DNA/RNA sensitivity needs with less input and preserve material for other omic analysis. “Twist’s cfDNA Library Preparation Kit with TrueAmp™ Polymerase Mix maximizes molecular conversion efficiency and high-fidelity amplification, supporting reliable variant detection even in sample inputs down to 1 ng,” she explained.

cfDNA library prep can be run in parallel with Twist’s RNA Library Prep Kit. According to Dr. Leproust, they take about two and five hours, respectively, meaning both libraries can be ready within a day.

Combined library preparation to simplify workflows

An alternative is to combine DNA and RNA library preparation in a single workflow. Dr. Shaffer reported that the QIAseq Multimodal DNA/RNA Library Kit can be used with cfDNA and cfRNA, as long as cfRNA is adequately preserved during preanalytical processing. Combining both analytes in the same workflow reduces sample splitting, transfers, and handling steps and can produce sequencing-ready libraries in about six hours.

The QIAGEN kit also adds Unique Molecular Indices (UMIs) to both DNA and RNA, helping distinguish biological signals from technical noise. This approach can help conserve limited sample material and simplify the workflow, but as Dr. Shaffer highlighted, labs still need to validate DNA and RNA recovery and sensitivity for their specific application.

The future of multiomic liquid biopsy beyond DNA and RNA

Combining DNA and RNA in liquid biopsies helps connect tumor genetics with gene expression, but as Dr. Motamedchaboki noted, “genomic and transcriptomic alterations do not always correlate with functional cellular states or therapeutic response.”

Thus, researchers are increasingly exploring additional omic layers, including proteomics, protein post-translational modifications like glycoproteomics and phosphoproteomics, as well as metabolomics and lipidomics, to characterize dysregulated signaling pathways, adaptive resistance mechanisms, and clinically relevant patient subtypes. Extracellular vesicles are attracting attention in this field as “they carry molecular information that reflects active biological processes within tumor cells and their microenvironment,” added Dr. Motamedchaboki.

But detecting circulating proteins and lipids of interest in plasma can be difficult, as they show low abundance against a much larger background. Advances in mass spectrometry are improving sensitivity and throughput. This is achieved through advances like the Thermo Scientific Orbitrap Astral Zoom Mass Spectrometer, which uses ion pre-accumulation and a dedicated low-input detector mode. Also, the targeted affinity-based methods like Olink Assays can be combined with mass spectrometry to obtain broader protein coverage when working with very low-abundance targets.

Dr. Motamedchaboki shared with us her visions on the future of omics in liquid biopsies: “In the future, as these technologies mature and become more broadly accessible, clinicians may be able to leverage an integrated clinical multiomics workflow that combines genomic, transcriptomic, proteomic, phosphoproteomic, lipidomic, and metabolomic data.” This more comprehensive view will help improve patient stratification, more accurately predict therapeutic response, and support more dynamic treatment decisions in precision oncology.

* Unless otherwise indicated, QIAGEN and PreAnalytix products mentioned are for research use only. Not for use in diagnostic procedures. Products and product claims may differ from country to country based on regulations, clearances and approvals and may not be available in certain country. Please visit www.preanalytix.com, www.qiagen.com or contact your local supplier or country representative for further details.

About the author: Nuria M. Wentzien got her Ph.D. from the University of Granada in 2023. After finishing her Ph.D., she discovered that science communication brings together her two favorite worlds: science and writing. Her career reflects her passion for learning and exploring new topics, from a Bachelor's degree in Biochemistry (with a strong focus on human sciences) to a Ph.D. in sustainable agriculture. She brings this same curiosity to every project, making readers feel as inspired by science as she does. Now dedicated to helping life-science companies communicate rigorous science in an engaging way, she is the co-founder of Helixa Communications.

Study Finds Chromatin Motion Holds Steady Until Cells Face Stress

SEQadmin2 — Wed, 14 Oct 2026 13:39:42 GMT

Living human cells appear to hold the local movement of their DNA at a remarkably steady level through most of the cell cycle, only dialing it up when something goes wrong, according to a new study from a team led by Kazuhiro Maeshima at Japan’s National Institute of Genetics.

Genomic DNA is wrapped around core histone proteins to form nucleosomes, the basic units of chromatin, and this dynamic chromatin behavior, tied to chromatin accessibility, plays a critical role in genome functions such as transcription and DNA replication and repair. During interphase, the period between cell divisions, DNA is replicated and the nucleus expands, all while cells continue these essential functions. How DNA’s physical behavior holds up through these large-scale changes had remained unclear. In an earlier study using synchronized human cells, Professor Maeshima’s team found that local chromatin motion stayed nearly constant across the G1, S, and G2 phases. The new study asked whether the same holds true under more natural conditions, in cells growing asynchronously without drug-induced synchronization.

To find out, the researchers combined single-nucleosome imaging with Fucci probes, fluorescent markers that change color as a cell progresses through the cycle, letting them identify each living cell’s cycle stage without chemical synchronization. Using HeLa and HCT116 cells expressing H2B-HaloTag to mark bulk DNA, they tracked individual nucleosomes and matched that movement to each cell’s Fucci signal. Across asynchronously growing cells, average local nucleosome motion remained nearly constant from G1 through S and into G2, even as the genome doubled in content and the nucleus grew substantially in volume.

The team also examined nucleosomes carrying H3.3, a histone variant enriched in active chromatin, or euchromatin. These nucleosomes moved more than bulk H2B-marked nucleosomes overall, but that elevated level of motion likewise stayed constant across interphase, suggesting different chromatin environments each maintain their own characteristic baseline of movement as the cycle progresses.

“Our results show that local DNA motion is maintained at a surprisingly similar level even while DNA is being duplicated and the nucleus is growing,” said Yu Nagata, first author of the study published in Journal of Cell Science. “This near-constant motion may provide similar physical conditions for housekeeping genome functions throughout interphase.”

That stability wasn’t absolute, though. When the researchers inhibited transcription, nucleosome motion increased throughout interphase. Replication stress induced with hydroxyurea and DNA damage induced with cisplatin produced the same effect, each raising average nucleosome motion above its steady baseline.

“Cells seem to maintain local DNA motion near a steady level for routine housekeeping tasks, but they can change this motion when necessary,” Professor Maeshima added. “For example, increased DNA motion after DNA damage may help cells perform ad hoc tasks such as recruiting DNA repair factors.”

Larger Datasets Reveal Additional Switches That Control Gene Expression

SEQadmin2 — Tue, 13 Oct 2026 17:56:14 GMT

Most GWAS loci fall in regions that control gene expression rather than in the genes themselves, and linking those variants to eQTL and sQTL signals has typically hit a discovery limit after a few thousand samples. A new study published in Science suggests that limit was really a limit of dataset size.

The team analyzed 14,324 RNA sequencing samples, including 6,454 whole blood and 1,291 lung samples, from the NHLBI-sponsored Trans-Omics for Precision Medicine (TOPMed) program. After e/sQTL analysis, they mapped tens of thousands of signals at high resolution. Because TOPMed cohorts are more genetically diverse than most prior studies, roughly one in six signals found in whole blood came from variants that are rare or absent in people of European ancestry, and just 1.3% of those appear in the four large eQTL resources built mostly from European samples.

“The more samples you add, the more e/sQTL signals that you detect,” said co-study leader Steve Parker from the University of Michigan. “I think NHLBI has invested very smartly in TOPMed. Although you saturate discovery of main effects after a few thousand samples… the number of independent e/sQTL signals just keeps on growing and growing, which is really cool to see.”

Fine-mapping GWAS signals from the UK Biobank showed that more eQTL signals also meant more overlap with GWAS findings, and the extra signals weren’t redundant: most genes have several independent regulatory switches, and the second, third, and fourth are harder for smaller studies to detect. Of the 10,611 GWAS signals the team connected to a specific gene, roughly two-thirds involved one of these secondary switches.

“Genetic regulation of the expression levels of one part of a biological unit’s machinery, such as a regulator of a cellular stress response, can affect the expression levels of other parts of the same biological machinery,” explained co-author Laura Scott. Parker added that “genetic evidence of drug targets is a huge predictor of clinical success for therapeutics,” more than doubling the rate of success, making the link between genetic variation and target gene expression especially valuable.

Co-author Francois Aguet noted that “another powerful aspect of larger-scale studies is the ability to identify QTL for rarer variants, which tend to have larger effects. As further such studies are conducted across more tissues and cell types, we will likely find a lot more of these effects.”

The findings also point to the value of expanding beyond whole blood and improving ancestral diversity. “In TOPMed, we still have many more distinct signals to discover at most genes, even in whole blood, which will almost certainly provide still more links between GWAS loci and their likely mechanisms,” noted co-author Laura Raffield. “Also, many non-European ancestry populations are still underrepresented in our current dataset… important ancestry-differentiated signals may be missed, even in blood, and certainly in other tissues. These issues will continue to be addressed in future work from TOPMed.”

“Science moves forward with bigger and better data, always,” Parker said. “It’s the equivalent of getting a fancy new microscope except we’re not seeing things in higher resolution in a microscopy image, we’re seeing things in much higher resolution from a genetic association with gene expression perspective.”

Computational Tool Captures How Chromatin Folds at Scale

SEQadmin2 — Fri, 09 Oct 2026 19:14:25 GMT

Studying how chromatin changes requires capturing molecular movements and interactions that are difficult to resolve experimentally. Now, an international research team led by IRB Barcelona, the University of Cambridge, UT Southwestern Medical Center and the Howard Hughes Medical Institute has developed OpenCGChromatin, a tool that functions as a computational microscope, using simulations to explore how chromatin folds, how its components interact, and what forces hold its structures together.

“The challenge is to connect interactions between individual molecules with the behavior of much larger stretches of chromatin. This tool allows us to study both within the same framework and understand how small molecular changes can alter DNA packaging,” says Modesto Orozco, corresponding author on the study published in Nature Communications.

The tool combines a detailed representation of DNA and proteins with greater computational efficiency, allowing researchers to study chromatin systems more than ten times larger than those accessible to previous models with comparable resolution, including assemblies containing hundreds of nucleosomes. Its simulations reproduce observations from microscopy and biochemical experiments while also revealing movements of flexible histone regions that are difficult to resolve experimentally.

“What Kieran Russell, first author of this study, has achieved is remarkable. He has pushed the state of the art in chromatin modeling, allowing us to simulate systems at a scale and level of molecular detail that were previously out of reach. We can now connect the chemical makeup of chromatin to its organization across scales—from molecular interactions to gene-sized structures and biomolecular condensates—while remaining closely grounded in experiment. I am very excited because OpenCGChromatin opens up a completely new range of questions that we can now address computationally,” says Rosana Collepardo-Guevara e, who also co-led the study.

“The simulations help us understand why changing the spacing between nucleosomes, or adding chemical modifications to histones, can make chromatin behave differently,” adds David Farré-Gil, also an author of the paper.

OpenCGChromatin is available as open-source software, allowing other researchers to use it to investigate the physical principles underlying genome organization.