SEQanswers - Forums

SEQanswers - Forums https://www.seqanswers.com/ vBulletin Forums en Wed, 14 Oct 2026 06:58:33 GMT vBulletin 60 https://www.seqanswers.com/images/misc/rss.png SEQanswers - Forums https://www.seqanswers.com/ Larger Datasets Reveal Additional Switches That Control Gene Expression https://www.seqanswers.com/forum/news/327712-larger-datasets-reveal-additional-switches-that-control-gene-expression 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... 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.”
]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327712-larger-datasets-reveal-additional-switches-that-control-gene-expression Computational Tool Captures How Chromatin Folds at Scale https://www.seqanswers.com/forum/news/327710-computational-tool-captures-how-chromatin-folds-at-scale 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... 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.
]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327710-computational-tool-captures-how-chromatin-folds-at-scale Turning NGS Data into Confident Pathogen Detection https://www.seqanswers.com/forum/site-news/webinar-series/327707-turning-ngs-data-into-confident-pathogen-detection Thu, 01 Oct 2026 13:37:21 GMT Next-generation sequencing (NGS) is changing how clinical and public health laboratories detect pathogens, track antimicrobial resistance, and...

Next-generation sequencing (NGS) is changing how clinical and public health laboratories detect pathogens, track antimicrobial resistance, and respond to outbreaks. However, transforming raw sequencing reads into actionable results is a challenge. Scientists in clinical and public health laboratories need a clear analysis process, an understanding of the quality metrics behind each call, and defined criteria for deciding when a detection counts as positive. Without these, sequencing data can be hard to interpret and turn into a validated clinical workflow.

In this webinar brought to you by BugSeq, Jose Alexander from AdventHealth will explain the basics of NGS analysis and show how to build a sequencing workflow in a clinical lab, including how to set thresholds for reporting positive detections. Jennifer Laplante from the New York State Department of Health will share how her team approaches viral sequencing in a public health setting. To close, Sherif Nour will demo BugSeq's fully automated, cloud-based bioinformatics platform, which analyzes DNA and RNA sequencing data within hours, and show where it fits in an efficient lab workflow.

Topics to be covered:

• The basic principles and key metrics behind NGS data analysis evolutionary insights to clinical applications
• How to set up a lab process that takes NGS data from sequencing through analysis
• How to set threshold criteria for calling positive detections with confidence
• How viral sequencing supports public health surveillance
• How automated, cloud-based bioinformatics can make clinical and public health sequencing workflows faster and simpler

Who should attend:

• Clinical microbiologists and medical microbiologists
• Clinical and public health laboratory directors and managers
• Public health laboratory scientists and epidemiologists working on pathogen surveillance
• Medical technologists and clinical laboratory scientists who are moving into sequencing
• Bioinformaticians who support clinical or public health sequencing workflows
• Infection prevention and antimicrobial stewardship professionals who use sequencing data
• Labs that are evaluating or starting to adopt NGS for pathogen detection

Register Here

]]> Webinar Series SEQadmin2 https://www.seqanswers.com/forum/site-news/webinar-series/327707-turning-ngs-data-into-confident-pathogen-detection Decoding the Genomics of Neurodegeneration with Large-Scale Long-Read Sequencing https://www.seqanswers.com/forum/site-news/webinar-series/327704-decoding-the-genomics-of-neurodegeneration-with-large-scale-long-read-sequencing Thu, 01 Oct 2026 13:06:10 GMT

Much of the genetic basis of Alzheimer's disease and related dementias (AD/ADRD) is still unexplained, and some of the answers may sit in parts of the genome that standard sequencing cannot read. Short-read sequencing often fails to resolve large structural variants, repeat expansions, and complex genomic regions, so variants that could drive disease go undetected. To close this gap, researchers at the NIH Center for Alzheimer's and Related Dementias (CARD) have built one of the largest long-read whole-genome sequencing resources in human brain tissue.

In this webinar, brought to you by Oxford Nanopore, Kimberley Billingsley, the head of the Translational Neurogenomics Group at CARD, will present discoveries of AD/ADRD-relevant mutations made possible by long-read sequencing. She will cover how haplotype-resolved methylation at key loci such as APOE points to new layers of epigenetic regulation and how the team extended this work to long-read RNA sequencing of brain tissue, which enables isoform-level transcriptomic analysis alongside the genomic and epigenetic data.

Topics to be covered:

How a large-scale long-read whole-genome sequencing resource was built across multiple postmortem brain cohorts
How long reads reveal structural variants and repeat expansions linked to Alzheimer's disease and related dementias
What haplotype-specific methylation at loci such as APOE shows about epigenetic regulation that short reads can't reach
How long-read RNA sequencing adds isoform-resolved transcriptomic data to genomic and epigenetic analyses of the brain
An introduction to Oxford Nanopore by Walaa Mohamed

Register Here

]]> Webinar Series SEQadmin2 https://www.seqanswers.com/forum/site-news/webinar-series/327704-decoding-the-genomics-of-neurodegeneration-with-large-scale-long-read-sequencing Spatial Transcriptomics and Metabolite Mapping Combined on a Single Tissue Slide https://www.seqanswers.com/forum/news/327703-spatial-transcriptomics-and-metabolite-mapping-combined-on-a-single-tissue-slide Tue, 29 Sep 2026 17:51:30 GMT Spatial transcriptomics (ST) shows which genes are active and where within a tissue, while spatial metabolomics (SM) maps the location of... Spatial transcriptomics (ST) shows which genes are active and where within a tissue, while spatial metabolomics (SM) maps the location of metabolites, the small molecules cells produce and use. A new open, low-cost imaging-based ST platform can now integrate SM on the same slide, addressing two problems that have limited the field: high cost and incompatibility between the two methods.

The platform, called OpenFISH, was described in a study led by Duan Lihui at the Institute of Genetics and Developmental Biology of the Chinese Academy of Sciences, published in Neuron. A biological system can be thought of as a multistory building, with information flowing from genome to transcripts to proteins to metabolites and finally to phenotype, and every layer interacting with the others. Measuring only one layer can miss the full picture and the connections between them. Existing ST technologies can detect molecular features within tissue at single-cell resolution, but they remain expensive and hard for many labs to use, and pairing imaging-based ST with the untargeted SM method MALDI-MSI on the same tissue section adds a further challenge.

To bring costs down, the researchers used a modular probe design along with a simple gene-coding system that removes the need for a microfluidic system, and they streamlined the experimental procedure so wet-lab time runs no more than 13 hours. A standard 20x widefield fluorescence microscope is enough to capture clear signals in situ. Together, these changes cut OpenFISH's total cost by about 95 percent compared with leading commercial platforms.

The team tested OpenFISH in two neuroscience applications: tracking cell-type-associated transposable element (TE) elevation during inflammation, and examining neuronal cell lamination distortion after knocking out the Reln gene. Reproducible TE upregulation pointed to TEs as active drivers or modulators of neuroinflammatory pathways, and beyond the known anatomical effects of Reln knockout, the researchers also found a decrease in D1-type inhibitory striatal neurons.

Because ST and SM are usually run on separate, adjacent tissue sections, differences between those sections can complicate interpretation. To combine both methods on one section, the researchers modified conductive glass slides for MALDI-MSI, using polyacrylamide gel embedding, protein digestion, and lipid removal so that OpenFISH signals remained detectable even after the harsh laser processing MALDI-MSI requires. Ion feature signals and transcript detection were barely affected by this integration, and the combined pipeline revealed cell-type-associated metabolites in mouse brain cells, with the merged modalities improving anatomical depiction.

Applied to 5xFAD mice, a model for Alzheimer's disease, the pipeline showed that microglia had the strongest changes, with multiple cell-type-associated metabolites elevated compared with healthy controls. The researchers describe the work as introducing a cost-effective ST tool that pairs with SM platforms to help untangle complex molecular interactions in biological systems.
]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327703-spatial-transcriptomics-and-metabolite-mapping-combined-on-a-single-tissue-slide New Genomics Technologies Take Aim at Long-Standing Limits https://www.seqanswers.com/articles/327696-new-genomics-technologies-take-aim-at-long-standing-limits Mon, 28 Sep 2026 18:27:00 GMT Researchers using sequencing and genomics tools often have to make trade-offs. They can choose between speed or scale, short reads or long-range...

Researchers using sequencing and genomics tools often have to make trade-offs. They can choose between speed or scale, short reads or long-range information, or targeted panels or a view of the whole transcriptome. New technologies that have been released this year are built to address those tough choices.

We asked six companies the same four questions to learn about their latest products. The new technologies bring a lot to the table, including rethinking sequencing throughput and flexibility, bringing long-range insights to short-read data, extending whole-transcriptome analysis to spatial and archived samples, and mapping base-editing outcomes directly in cells.

Read on to learn more from the experts.

Atera
10x Genomics
Kamila Belhocine, Ph.D., Senior Director, Spatial Biology, 10x Genomics

1. What is the new innovation?

Atera is a new in situ spatial biology platform engineered to deliver whole-transcriptome spatial analysis without compromising on throughput, plex, or performance.

A single Atera instrument can process over 800 1 cm² whole transcriptome samples in both fresh-frozen and FFPE tissue. That’s nearly a billion cells and trillions of transcripts in a single year. Atera instruments also have a 212 nm per-pixel resolution with analytical software that can provide sub-30 nm localization, compatibility with standard microscopy slides, support custom panels, and are powered by onboard GPUs and optimized algorithms that can analyze petabytes of data in real time.

2. What challenge is it solving?

Historically, scientists have used spatial biology tools to analyze molecules, cells, and tissues in their native context, but it came with a tradeoff. Legacy technologies didn’t allow for the kind of throughput to make population-scale insights possible in a realistic period of time.

Because of this technological jump, Atera removes constraints that have previously sequestered spatial tools into niche discovery and enables analysis of full clinical trial cohorts as opposed to subsets of sample populations. For example, an Atera instrument can analyze up to 3,000 core biopsy samples in a year. That kind of throughput is critical for scientists, as generating meaningful insights at scale requires large, high-quality datasets—especially to build complex, foundational AI models.

3. How do you see scientists using this technology?

Ultimately, Atera is designed for researchers who need spatial insights at population-level scale.

We’ve seen spatial tools utilized in fields such as oncology, immunology, neurodegenerative disease, and developmental biology. Spatial insights, especially at scale, can help scientists uncover important biomarkers, accelerate drug target identification and validation, explore core mechanisms of both healthy and diseased tissue, and even inform treatment strategy, patient stratification, and potentially predict treatment response. Atera’s compatibility with formalin-fixed paraffin-embedding (FFPE) is critical, as many archival samples are preserved in that format but are difficult to work with.

Academic and medical institutions continue to utilize our technology to drive foundational discovery, such as Brigham and Women’s Hospital, which aims to use Atera to accelerate target discovery in inflammatory and fibrotic diseases, or The Broad Institute, which will be using the platform to search for predictive biomarkers and train AI models.

Another one of our partners, The German Cancer Research Center (DKFZ), used Atera to study a colorectal tumor sample and found something exceptional: tumors that were previously thought to have low immune infiltration were found to harbor active and diverse immune cell populations.

For biotech and pharmaceutical partners, a core goal is to uncover biology to help accelerate the drug pipeline, whether for target identification and validation, biomarker discovery, mechanism of action studies, or toxicology.

Some of the other organizations and research consortiums we’ve partnered with have a specific goal: to power AI models with massive amounts of high-quality data, such as Human Cell Atlas, PharosAI, and Bioptimus.

4. What has been the response from scientists?

Initial responses from researchers reflect genuine excitement for the breadth of new insights this approach makes possible. We’re already getting feedback that access to this kind of throughput is rapidly expanding the scope of new projects. It’s something we’re incredibly proud of. Our partners who have used Atera in early access include Allen Institute and DKFZ.

Another exciting aspect to Atera is that we have a full roadmap planned to incorporate even more features to simplify its workflow, incorporate protein multiomics and variant calling, and more. Plus, we’re investing heavily into a software ecosystem that allows any scientist to effectively interpret output without needing a background in bioinformatics.

BaseMap™
Broken String Biosciences
Felix Dobbs, Ph.D., Co-founder and Chief Operations Officer, Broken String Biosciences

1. What is the new innovation?

BaseMap™ is a new cellular approach for characterizing adenine base editing activity, designed to show exactly where deamination events are occurring. It can identify intended on-target editing, bystander deamination, and off-target activity directly in cells, providing a more complete picture of editing outcomes within their cellular context.

The important distinction is that BaseMap does not rely on a proxy system to predict where editing might occur. It measures editing-associated events directly in the cells being studied, providing nucleotide-level information about the location and nature of those events.

2. What challenge is it solving?

Base editing has created a new characterization challenge. Unlike conventional nuclease-based genome editing, base editors are designed to make targeted nucleotide changes without creating a programmed double-strand break. This means that understanding their activity requires looking beyond conventional DNA break mapping alone.

Researchers need to understand whether the intended base has been edited, whether neighboring bases have also been modified through bystander deamination, and whether unintended activity is occurring elsewhere in the genome.

Existing approaches can provide important pieces of this picture, but there is a gap in the ability to directly characterize these different outcomes in a cellular system. This is increasingly important as base editing moves toward therapeutic development, with recent FDA draft guidance highlighting the importance of assessing off-target editing and genome integrity in cells representative of the intended target tissue.

3. How do you see scientists using this technology?

BaseMap™ is provided as a kit, enabling researchers to run the workflow in-house using their own cells rather than sending samples to an external service. This is particularly important for base editing, where cellular context can influence editing outcomes and where access to relevant cell models may be critical.

There is currently no comparable commercially available approach that enables researchers to directly characterize on-target deamination, bystander products, and off-target activity within a cellular system. BaseMap is designed to fill that gap.

We see immediate applications in the development and optimization of base editing systems, where researchers can compare editors, guide RNAs, and experimental conditions and understand the wider editing profile associated with each. For therapeutic developers, this can support candidate selection and genome integrity assessment as programs progress toward the clinic. More broadly, it provides researchers with a new way to investigate how base editors behave across different genomic and cellular contexts.

4. What has been the response from scientists?

The response we have seen reflects a growing need for more complete characterization of base editing outcomes, particularly as the field moves from demonstrating editing efficiency toward understanding specificity and safety.

One of the practical advantages is that researchers can investigate on-target activity, bystander products, and off-target activity within a cellular context rather than building their understanding from separate proxy approaches. This gives them a more connected view of what is happening following editing and can help inform decisions around editor, guide, and condition selection.

Importantly, the aim is not simply to generate more data. It is to provide information that helps researchers distinguish between candidates and make better-informed decisions earlier in development, before significant time and resources have been committed to a particular editing strategy.

TruPath™ Genome
Illumina
James Han, Vice President of Bioinformatics, Illumina

1. What is the new innovation?

TruPath™ Genome’s innovative proximity read mapping provides long-range insights on short-read sequencing. TruPath Genome is Illumina’s simplest genomic workflow to date, featuring the ease of on-flow cell library preparation. Patterned flow cell technology and advanced informatics seamlessly add long-distance insights for comprehensive variant detection. DRAGEN™ algorithms incorporate proximity information from TruPath Genome flow cells to improve read alignment and variant calling.

2. What challenge is it solving?

Traditional short-read sequencing methods struggle to capture difficult-to-map or “dark” regions of the genome. TruPath Genome innovates with flow cell configuration and informatics to accurately characterize these challenging regions. It extends Illumina’s high-quality reads to difficult-to-map regions, including variant phasing, and improved detection of structural variants and short tandem repeats.

3. How do you see scientists using this technology?

We believe genetic disease researchers will find TruPath Genome to be an unmatched workflow for variant detection and accuracy. It maximizes discovery potential and research dollars by giving comprehensive results.

4. What has been the response from scientists?

Early customers like University Medical Center Utrecht’s Marcel Nelen and the University of Exeter’s Emma Baple are already demonstrating TruPath Genome’s potential applications in rare disease and NIPD research.

Overall, customers are finding the technology detects even the hardest-to-call variants.

They appreciate that TruPath Genome reduces the need for additional or reflex assays. Unlike other methods that require off-instrument library preparation with trained laboratory personnel and ancillary equipment, the TruPath Genome workflow is on flow cell and only takes about 10 minutes of hands-on time. They also like that it operates on the sequencers they already use, so it delivers powerful new capabilities from the instruments they already have.

T7+
MGI Tech
Erin Zhang, Ph.D., Product Director, MGI Tech

1. What is the new innovation?

T7+ is our next-generation powerhouse sequencer, which addresses the trade‑off in large‑scale genomic sequencing. Users traditionally have to choose between speed and scale, which is a constraint that has been slowing down research for years. T7+ makes it possible for users to process many samples really quickly.

Several technological highlights help achieve this superior performance. The density of DNA Nanoballs (DNB) on the flow cell is ultra-high, allowing us to significantly boost the throughput without compromising the imaging speed. We have also developed a TDI camera with high-resolution and high-speed. Our proprietary RiCT (RCA in coiled tubing) technology allows for high-speed DNB preparation and can adjust reagent flow rates to achieve peak efficiency with smart sensors. In addition, an optimized base-calling algorithm makes the data generation four times faster.

2. What challenge is it solving?

To ensure that T7+ addresses real customer challenges, we worked closely with hundreds of labs to learn about their pain points. T7+ has addressed three major issues. First is the trade-off between speed and throughput. T7+ is able to generate more than 14 Tb data within 24h.

Next is the complexity of operating sequencers. We built a 7-in-1 highly integrated compact architecture: DNB making, DNB loading, sequencing, pure water bucket, waste bucket, data analysis, and data compression are all consolidated inside around a one‑meter‑footprint instrument. For the reagent cartridge, the modular design and RFID auto-identification largely minimize the manual handling operation and ensure error-free setup. Also, we designed the smart control center to be as easy as a smartphone.

Another challenge is cost. We placed heavy emphasis on cost optimization and made our technology more cost effective. With lower costs, researchers can generate more data with the same funding.

3. How do you see scientists using this technology?

T7+ has been used by both academic and industrial customers for a wide range of applications, spanning from fundamental biological research to translational clinical studies. Scientists have deployed it in diverse research projects, including population cohort study, spatial transcriptomics of brain injury repair, agricultural strain identification, and deciphering the evolution of the code of life through ancient human DNA samples.

Researchers at Fudan University used T7+ for PCR‑free whole‑genome sequencing in population cohorts. The lab leveraged the platform’s uniform read coverage to resolve challenging genomic regions such as homopolymers and tandem repeats, demonstrating how consistent, high-accuracy sequencing delivers high-quality variant calling of SNPs, Indels and SVs.

Moving from population genetics to clinical translational research, the platform has also proven valuable in oncology research. One customer in the U.S. has applied DNBSEQ™ sequencing technology to oncology applications using circulating tumor DNA (ctDNA). Their work highlighted how ctDNA is transitioning from a research tool to a vital clinical marker for minimal residual disease (MRD) and early cancer detection.

Outside academic labs, commercial service providers particularly value T7+’s ability to support pooling highly diverse library types in a single flow cell. Many service providers have pooled multiple library types spanning WGS, metagenomics, RNA, single-cell, methylation, and others, while still maintaining high sequencing data output with high Q30 data quality. This demonstrates T7+’s operational flexibility and stability for commercial service labs that routinely handle heterogeneous sample mixes.

Looking ahead, T7+’s outstanding data quality, throughput, and cost efficiency position it to support an even broader spectrum of applications. We expect it to play a key role in large-scale population genomics initiatives and multi-omics studies, while also expanding into more clinical directions as the platform develops.

4. What has been the response from scientists?

Customers from both academia and industry have responded enthusiastically to the T7+, citing its accuracy, higher throughput, simplified workflow, and cost efficiency. The feedback spans different user segments, each highlighting distinct benefits that collectively demonstrate the platform’s broad impact on research and clinical workflows.

A professor from Tongji University studying microbial ecology noted that the collaboration with MGI has allowed him to explore lake microbial diversity, and T7+ delivered excellent sequencing results. This reflects how the platform empowers fundamental research with higher data quality.

Customers from clinical research have the same feeling. A customer from the U.S. emphasized the platform’s dual strengths: methylation data achieved exceptional Q40 scores with 14B reads, while whole-genome data were delivered at a remarkably low cost, at around $100 per genome. This combination of high accuracy and affordability makes the T7+ particularly attractive for large-volume oncology sequencing and clinical testing workflows.

What’s more, commercial service providers also voiced strong approval. The service provider JMDNA (Shanghai) Bio-Medical Technology Co., Ltd., specializing in multiomics research and clinical applications, commented that the T7+ features improved sequencing quality, enabling faster run times, and lowering sequencing costs. It makes large-scale sequencing services feasible for genomics research, helping end-user researchers reduce costs and boost efficiency.

Taken together, these responses reveal that T7+ has consistently proven itself as a transformative tool that not only enhances data quality and operational efficiency but also substantially reduces sequencing costs. This strong positive reception underscores the platform’s potential to accelerate scientific discovery and clinical translation worldwide.

Evercode™ WT FFPE
Parse Biosciences
Aisling Sinclair, Senior Product Marketing Manager, Parse Biosciences

1. What is the new innovation?

Parse Biosciences’ Evercode™ WT FFPE brings scalable, true whole-transcriptome single-cell sequencing to formalin-fixed, paraffin-embedded (FFPE) tissue. It combines reverse transcription-based chemistry optimized for degraded RNA with Parse’s instrument-free combinatorial barcoding approach.

Researchers can generate high-quality gene expression data from archived samples while capturing RNA directly across the whole transcriptome. This provides a direct, sequence-based view of gene expression and access to biology that targeted methods may miss, including unexpected transcripts, isoforms, long noncoding RNAs, and expressed variants.

2. What challenge is it solving?

FFPE is the standard preservation method for many clinical tissue samples. Biobanks therefore contain enormous collections of archived tissue paired with clinical information that can help researchers study disease progression and treatment response. However, fixation fragments and crosslinks RNA, making it difficult to generate high-quality single-cell data from these samples.

Probe-based methods have made FFPE tissue more accessible, but they measure predefined sequences selected during assay design. As a result, researchers are limited to the targets represented in the assay, and differences in probe binding and performance can influence observed gene counts.

Evercode WT FFPE makes true whole-transcriptome discovery possible from archived tissue. Researchers can follow the biology wherever it leads, from established disease pathways to unexpected cell states, transcripts, isoforms, and regulatory programs. The result is a more complete view of each sample and a stronger foundation for understanding disease biology.

3. How do you see scientists using this technology?

One of the clearest near-term applications is translational research, particularly oncology, where large FFPE collections span disease stages, treatment histories, and clinical outcomes. Researchers can use these samples to characterize the tumor microenvironment, compare treatment responders with nonresponders, examine changes before and after therapy, or follow how tumors evolve between primary disease and recurrence. Whole-transcriptome capture also supports the study of isoforms, long noncoding RNAs, expressed variants, and molecular programs that may contribute to disease progression or treatment resistance.

The opportunity extends beyond oncology to any field with valuable archived tissue. These samples are often precious and finite, and researchers may have only one opportunity to profile them at single-cell resolution. Whole-transcriptome sequencing helps them make the most of that opportunity by capturing everything present, which preserves the possibility of finding what they do not yet know to look for. As new questions emerge and our understanding of the transcriptome grows, the value of those datasets can grow with it.

4. What has been the response from scientists?

The response has been very encouraging. Researchers are especially excited by the combination of whole-transcriptome capture without predefined probes and the ability to scale that discovery across substantial FFPE archives. In our conversations to date, scientists have discussed moving from initial pilots to cohorts of hundreds and, in some cases, thousands of archived samples. For researchers who have traditionally had to work within targeted panels, Evercode WT FFPE offers something genuinely new, the freedom to ask broader questions of valuable tissue collections at meaningful scale.

AXELIOS 1
Roche
Bellal Moghis, Director of Marketing, Sequencing and Life Sciences, Roche Diagnostics

1. What is the new innovation?

The core innovation is sequencing by expansion (SBX), a new class of next-generation sequencing (NGS) that sequences a surrogate molecule called an Xpandomer. These Xpandomers are approximately 50 times longer than the original DNA, creating a much stronger signal and higher signal-to-noise ratio in nanopores. This enables real-time data generation, allowing sequencing to occur in as little as four hours without compromising data quality.

2. What challenge is it solving?

AXELIOS 1 addresses the high costs and rigid requirements that have historically created barriers to NGS adoption and scale. Many existing high-throughput systems require large sample batches, which is impractical for many laboratories. AXELIOS 1 offers a flexible, economically viable solution that allows labs to maintain cost efficiency with smaller batches while scaling as demand increases. It also provides complete workflow solutions, helping researchers integrate new technology more seamlessly.

3. How do you see scientists using this technology?

Whole-genome sequencing (WGS) is a primary application, especially where rapid turnaround is critical for research. We also see significant interest in quantitative applications, such as single-cell sequencing, where the simplex workflow reduces costs per million reads. Researchers can leverage the platform’s distinct run modes—duplex for high-accuracy sequencing and simplex for quantitative counts—to tailor performance to their application. Key research fields include oncology, neurodegenerative disease, and rare genetic disease research.

4. What has been the response from scientists?

Scientists appreciate the flexibility to match sequencing capacity to the work at hand and switch between different workflows—such as routine, high-efficiency sequencing and rapid-response applications like SBX-Fast—on the same instrument. This capability effectively changes the economics for many service laboratories, allowing them to increase capacity and offer applications that were previously not financially feasible, all without the need to adopt multiple platforms.

For Research Use Only. Not for use in Diagnostic Procedures. AXELIOS is a trademark of Roche.

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/327696-new-genomics-technologies-take-aim-at-long-standing-limits How Multiomics Methods are Advancing Disease Research https://www.seqanswers.com/forum/sequencing-technologies-companies/vendor-forum/327692-how-multiomics-methods-are-advancing-disease-research Fri, 25 Sep 2026 20:00:56 GMT Modern multiomics lets scientists explore the convergence of cellular identity, function, and modulation. That convergence, powered by data from...

Modern multiomics lets scientists explore the convergence of cellular identity, function, and modulation. That convergence, powered by data from multiple layers, is reshaping disease research. The ability to capture data from multiple omics, including the genome, epigenome, transcriptome, and proteome, is powered by technological advancement. New tools facilitate automated high-throughput library prep, help ensure qPCR reliability and reproducibility, and accelerate sequencing data analysis.

In this ebook, explore how automation, quantitative rigor, and AI-assisted interpretation are helping researchers turn multiomic complexity into a clearer picture of disease mechanisms.

Download eBook

]]> Sequencing Resources mdimapilis https://www.seqanswers.com/forum/sequencing-technologies-companies/vendor-forum/327692-how-multiomics-methods-are-advancing-disease-research Bat1K Consortium Combines Genomes and Fossils to Trace Bat Origins https://www.seqanswers.com/forum/news/327691-bat1k-consortium-combines-genomes-and-fossils-to-trace-bat-origins Fri, 25 Sep 2026 17:06:42 GMT An international consortium has sequenced and compared 103 bat genomes spanning all 21 recognized bat families, then combined that genomic dataset... An international consortium has sequenced and compared 103 bat genomes spanning all 21 recognized bat families, then combined that genomic dataset with information from 44 bat fossils to reconstruct how bats evolved their extraordinary biology. The study, led by researchers at the University of St Andrews as part of the Bat1K consortium, was published in Nature.

Bats make up roughly one in five mammal species and are the only mammals capable of powered flight, but their evolutionary history has been difficult to pin down using fossils or genetics alone. By sequencing genomes from across the full breadth of living bat diversity and integrating that data with the fossil record, the consortium built a more complete picture of bat evolution than either approach could produce on its own.

The genomic sequencing effort allowed the team to computationally reconstruct the ancestral bat genome, tracing the genetic changes that gave rise to traits such as echolocation and flight across the bat family tree. Comparing genomes across all 21 families also let researchers identify the genes and genetic changes associated with these specialized adaptations, linking specific sequence differences to the biological features that distinguish bat lineages from one another.

Combining this genomic resource with the fossil dataset gave the researchers a new way to date bat origins. According to Prof. Liliana M Dávalos, “The approach we used to model the evolution of fossil and living species together can do what other methods cannot: identify the oldest group of fossil bats while taking the genomic data into account, and uncover when and where bats originated.”

The scale of the genomic resource itself reflects years of coordinated sequencing work across the consortium. Prof. Sonja Vernes, who leads the Bat1K initiative, said, “Bats constantly surprise us. They are one of evolution’s greatest experiments. This extraordinary genomic resource, the culmination of years of international cooperation of Bat1K, is finally allowing us to understand how their remarkable biology evolved.”

By pairing whole-genome sequencing across the entire bat order with fossil-informed modeling, the study offers a genomic framework other researchers can use to investigate the molecular basis of bat-specific traits, and demonstrates how integrating large-scale comparative genomics with paleontological data can resolve evolutionary questions that neither dataset could answer alone.
]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327691-bat1k-consortium-combines-genomes-and-fossils-to-trace-bat-origins <![CDATA[Postdoc Opportunities in RNA Directed Evolution: ERC Advanced Grant "REVOLVER"]]> https://www.seqanswers.com/forum/jobs-forums/academic-non-profit-jobs/327690-postdoc-opportunities-in-rna-directed-evolution-erc-advanced-grant-revolver Fri, 25 Sep 2026 08:31:11 GMT The Laboratory for Genomics of Long Noncoding RNA and Disease (GOLD Lab) is recruiting three postdoctoral researchers for a new ERC-funded programme... The Laboratory for Genomics of Long Noncoding RNA and Disease (GOLD Lab) is recruiting three postdoctoral researchers for a new ERC-funded programme applying directed evolution for the first time to long noncoding RNAs. The goal is to develop a deeper understanding of their molecular mechanisms and to engineer RNAs as a new class of therapeutic for liver disease.
You will lead your own research area within a diverse, well-resourced team, and develop expertise at the intersection of RNA, evolution, genome editing and translational medicine. The project benefits from a team of world leading international collaborators.
We are looking for ambitious, independent researchers with a PhD and strong track record in one or more of:

Directed evolution
Bio engineering
RNA biology
Genome editing
Computational genomics, evolutionary biology, fitness landscapes, or machine learning for biological sequences

Positions are fully funded for up to 5 years with competitive salary.
Start: March 2027 or soon after.
Location: University College Dublin, Ireland.
More about us: We at GOLD Lab love to study lncRNAs and exploit them for medicine. We employ an interdisciplinary wet/dry approach and develop new methods based on genome-editing. We work closely with leading international consortia and collaborators. Our core values are love of science, respect for each other, team work and rigorous thinking. See our website at https://gold-lab.org

To apply: Send a brief email outlining your relevant experience and motivation, telling us where you saw our advert, with attached CV, to rory.johnson@ucd.ie with subject line "REVOLVER PD". Applications will be accepted on a rolling basis until positions are filled (check our website).
]]> Academic/Non-Profit Jobs roryjohnson2 https://www.seqanswers.com/forum/jobs-forums/academic-non-profit-jobs/327690-postdoc-opportunities-in-rna-directed-evolution-erc-advanced-grant-revolver Microgravity Experiments Reveal Gravity’s Impact on Human Genome Organization https://www.seqanswers.com/forum/news/327689-microgravity-experiments-reveal-gravity’s-impact-on-human-genome-organization Wed, 23 Sep 2026 19:05:04 GMT The Human Genome Project, launched in 1990, eventually produced a full sequence of human DNA, but many of the physical rules governing how that... The Human Genome Project, launched in 1990, eventually produced a full sequence of human DNA, but many of the physical rules governing how that genome is organized inside cells remain unclear—including the influence of one force that’s present everywhere on Earth: gravity.

A new study in Science Advances tackles that question with an unusual approach: creating a zero-gravity, or microgravity, environment to study how a human cell’s genome behaves without gravity acting on it. The method serves two purposes at once—isolating gravity’s effect on the genome by removing it from the equation, and showing how the genome might function in the true zero-gravity conditions of outer space.

“On Earth, the role of gravity is intriguing—it is a constant mechanical stress on everything,” says NYU’s Alexandra Zidovska, who led the study. “We wanted to know what gravity’s role is in the genome’s organization and function here on Earth. To uncover it, you have to remove gravity as a force so we simulated zero gravity in our experiments.” The question matters beyond Earth too, she adds: “Beyond Earth, the question of lack of gravity is also compelling: How will the human genome be affected when in outer space? We think our findings can be useful in better understanding how space travel affects us.”

The human genome is packed with remarkable density—two meters of DNA folded into a cell nucleus roughly 10 micrometers across, about the width of a silk fiber. That structure is tightly linked to function, and disruptions to it are associated with diseases including cancer and developmental disorders, yet the physical principles behind the genome’s organization remain poorly understood. As Zidovska puts it, “We do not know if or how the presence of gravity affects this organization and if the absence of gravity would cause genomic aberrations.”

To investigate, the NYU team built a custom random positioning machine—similar in principle to devices used in astronaut training—that rotates dishes of live, bubble-free cells along two independent axes to simulate weightlessness. They also developed new rotational algorithms to minimize the fluid flows that rotation normally produces (but that don’t exist in true zero gravity), plus separate algorithms to study how those flows alone affect cells. Together, these advances reduced both flow effects and cell clumping that had obscured results in earlier studies of simulated microgravity.

After exposing cells to simulated microgravity, to strong flows, or to neither for 24 hours, the researchers used detailed imaging to track changes in cell and nuclear shape and volume, the nuclear envelope, the genome’s organization and movement, and the nucleolus. Cells exposed to flows became elongated, while cells in simulated microgravity kept their shape, though their nuclei grew larger—suggesting gravity normally helps keep the nucleus more compact. The nuclear envelope’s thickness and structure stayed unchanged in both conditions, and the genome held onto its normal organization and motion after the 24-hour exposure. Simulated microgravity did not cause DNA damage, but exposure to flows did, and the nucleolus became smoother under either condition. The researchers note that these subtle changes could compound over longer exposures and eventually affect cell physiology.

“Our data show that the genome, its organization, and dynamics are incredibly robust and seem unaffected by gravity, or lack thereof, after 24 hours,” Zidovska says. “In the same way, these results suggest that the physical organization of the human genome may undergo minimal changes in outer space during comparable times. However, such changes could occur during longer exposures and due to DNA damage occurring in space.”]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327689-microgravity-experiments-reveal-gravity’s-impact-on-human-genome-organization Join the Live Webinar: Programming Biology with Genome-scale AI - 7th December - 3 PM CEST https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327688-join-the-live-webinar-programming-biology-with-genome-scale-ai-7th-december-3-pm-cest Tue, 22 Sep 2026 16:15:38 GMT Dear all, We are pleased to invite you to our upcoming live webinar, “Programming Biology with Genome-scale AI,” on 7 December 2026, from...
We are pleased to invite you to our upcoming live webinar, “Programming Biology with Genome-scale AI,” on 7 December 2026, from 3:00–5:00 PM (Berlin).

The webinar will explore DNA language models, genome-scale foundation models, AI-based variant interpretation, DNA design, and applications of AI in modern genomics and programmable biology.

The session will also include a live introduction to the Genomic Intelligence Platform and its applications in computational research workflows.

Date: 7 December 2026
Time: 3:00–5:00 PM (Berlin)
Format: Online via Zoom

Register here: https://www.physalia-courses.org/cou...ps/webinar-ai/

Best regards,

Carlo

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

Carlo Pecoraro, Ph.D

Physalia-courses DIRECTOR]]> Bioinformatics Physalia-courses https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327688-join-the-live-webinar-programming-biology-with-genome-scale-ai-7th-december-3-pm-cest Massive Genomic Analysis Maps the Cellular Pathways Driving Psoriasis https://www.seqanswers.com/forum/news/327683-massive-genomic-analysis-maps-the-cellular-pathways-driving-psoriasis Fri, 18 Sep 2026 19:37:43 GMT A research team led by Hong-Hee Won from Sungkyunkwan University has pinpointed the genetic and cellular mechanisms underlying psoriasis by combining... A research team led by Hong-Hee Won from Sungkyunkwan University has pinpointed the genetic and cellular mechanisms underlying psoriasis by combining genomic data from more than 1.1 million people with single-cell analysis of patient skin samples. The findings, published in Nature Communications, also flag a slate of genes as candidate targets for new treatments.

Genetic and cellular studies of psoriasis have largely proceeded on separate tracks, with few efforts linking large-scale genomic findings to what individual cells are actually doing in diseased tissue. To close that gap, the team ran an integrated meta-analysis of genomic data from the 1.1 million-person cohort—all of European ancestry—and identified 125 independent genetic susceptibility loci associated with the disease. Seventeen of those loci had never been reported before.

The team then layered single-cell transcriptomic analysis on top of the genetic findings, examining gene activity at single-cell resolution in skin samples from both healthy individuals and psoriasis patients. That combination let them trace which specific cell populations are actually driving disease onset, rather than just which genes are statistically associated with it.

The single-cell data revealed a complex signaling interplay among several cell types: myeloid cells and T cells from the immune system, keratinocytes on the skin’s surface, and vascular endothelial cells lining blood vessels. These populations appear to interact in a cascade that fuels excessive inflammation. By mapping how these cell types interact across different skin layers—comparing non-lesional, lesional, and treated tissue—the researchers narrowed the field to 50 promising therapeutic target genes, each showing cell subtype-specific activity patterns.

“This research combines large-scale genomic data with precise cellular transcriptomic analysis to clearly reveal which genetic factors and cell types are involved in psoriasis pathology,” said Professor Won. “We expect our findings to serve as a key milestone in developing personalized immune-modulating treatments and new drugs tailored to individual patients.”
]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327683-massive-genomic-analysis-maps-the-cellular-pathways-driving-psoriasis Stanford Team Turns Scientific Papers into Interactive AI Agents 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