https://www.seqanswers.com/ vBulletin Forums en Sat, 29 Aug 2026 05:00:28 GMT vBulletin 60 https://www.seqanswers.com/images/misc/rss.png https://www.seqanswers.com/ https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327661-accessible-bioinformatics-for-windows-users-–-livestream-seminar Tue, 25 Aug 2026 05:53:58 GMT Hi everyone Instats is excited to offer a 5-day seminar, Accessible Bioinformatics for Windows Users...
Instats is excited to offer a 5-day seminar, Accessible Bioinformatics for Windows Users, livestreaming October 2nd - 6th and led by Andres Zhou Tsang from Adelaide University. Because many bioinformatics tools are designed for Linux while Windows remains the most common operating system on personal and institutional computers, this workshop provides a practical pathway for researchers who need to run Linux-based bioinformatics workflows without investing in a dedicated Linux machine or relying on cloud services. Participants will learn to install and configure a Windows-based bioinformatics environment using Windows Subsystem for Linux (WSL), while working with tools such as conda, Notepad++, R, Python, IGV, and FastQC to process, visualize, and assess sequencing data. Through hands-on sessions covering FASTA formats, basecalling, genome assembly, sequence alignment, RNA-seq, gene annotation, and differential expression analysis, this seminar equips Windows users with accessible, practical skills for conducting common bioinformatics analyses.

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


Best wishes

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

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


Best wishes

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

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



Best wishes

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

WHAT YOU'LL GAIN

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

COURSE FORMAT

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

WHO IS THIS COURSE FOR?

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

WHY TAKE THIS COURSE?

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

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

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

COURSE DETAILS

Course dates: 23–27 November 2026

Duration: 25 hours

Format: Live online

Registration: £400

LEARN MORE & ENROL

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

QUESTIONS?

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

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

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

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

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

WHAT YOU'LL GAIN

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

COURSE FORMAT

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

WHO IS THIS COURSE FOR?

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

WHY TAKE THIS COURSE?

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

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

COURSE DETAILS

Course dates: 9–13 November 2026

Duration: 25 hours

Format: Live online

Registration: £400

LEARN MORE & ENROL

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

QUESTIONS?

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

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

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

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

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

WHAT YOU'LL GAIN

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

COURSE FORMAT

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

WHO IS THIS COURSE FOR?

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

WHY TAKE THIS COURSE?

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

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

COURSE DETAILS

Course dates: 26–30 October 2026

Duration: 5 days, 5 hours per day

Format: Live online

Registration: £400

LEARN MORE & ENROL

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

QUESTIONS?

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

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

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

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

Salignon added that the tool can help identify candidates for future treatments of age-related diseases and cancer. In laboratory experiments, the team validated two new candidates: pralatrexate, which accelerated cellular ageing, and piperlongumine, which made cells more youthful. He cautioned that the results are based on cell-based experiments, so further research is needed before they could be translated into patient treatments.
]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327651-new-tool-estimates-cellular-age-from-gene-activity-patterns https://www.seqanswers.com/forum/events-conferences/327648-hands-on-ngs-data-analysis-workshops Mon, 17 Aug 2026 07:31:30 GMT Hi everyone, just a short note for those of you who are working with NGS data and maybe feel that you would like to understand a bit better what...
just a short note for those of you who are working with NGS data and maybe feel that you would like to understand a bit better what is actually happening during the analysis.

At ecSeq we have been teaching practical NGS data analysis courses for quite a few years now, mostly for PhD students, postdocs and researchers from biology or medicine who generate sequencing data themselves, but do not necessarily have a strong bioinformatics background.

The courses are very hands-on. We work with real sequencing data and commonly used open-source tools, and usually spend quite a lot of time looking at what happens at the individual analysis steps, what can go wrong, and how you can actually decide whether a result makes sense.

Depending on the workshop, we cover topics such as:

• NGS data analysis and variant calling
• RNA-Seq
• Single-cell RNA-Seq
• NGS epigenomics
• Nextflow and bioinformatics pipeline development
• Linux and command-line basics for NGS analysis

What we try to teach is not just a list of commands that you can copy and run. Especially with NGS data, I think it is much more useful if you understand why a certain step is there, which assumptions are made, what the QC actually tells you, and when you should probably stop and have a closer look at the data.

Some of the courses start quite basic, so you don't need to be a bioinformatician to participate.

If this sounds useful for your own work, you can find the current workshops here: https://www.ecseq.com/ngs-workshops]]> Events / Conferences ecSeq Bioinformatics https://www.seqanswers.com/forum/events-conferences/327648-hands-on-ngs-data-analysis-workshops https://www.seqanswers.com/forum/news/327647-dna-repair-enzymes-favor-specific-genome-sequences-study-finds Thu, 13 Aug 2026 20:22:48 GMT An unhealed wound leaves a scar. In the genome, an unrepaired DNA lesion leaves a mutation — a permanent edit to the genetic code. Mutations can... An unhealed wound leaves a scar. In the genome, an unrepaired DNA lesion leaves a mutation — a permanent edit to the genetic code. Mutations can disrupt gene function, drive disease and aging, but they are also the raw material of genetic diversity that has allowed new traits, and ultimately new species, to emerge. What has remained unclear is why some damaged stretches of DNA get repaired while others do not.

A new study published in Nature Communications, from the laboratory of Ariel Afek at the Weizmann Institute of Science, pinpoints which DNA sequences and structures several major repair enzymes preferentially target—and suggests those preferences have left a lasting signature on the human genome, one that may also factor into how cells turn cancerous.

Every cell absorbs thousands of DNA-damaging chemical reactions daily. “When DNA repair systems work properly, they repair most of the damage, but not all of it,” Afek explains. “Therefore, the rate at which mutations accumulate is a balance between the rate of damage and the rate of repair. This balance varies across different areas of the genome, and we still do not understand why certain mutations manage to accumulate in specific regions. Most studies to date have focused on changes that leave a ‘scar’ – a mutation. But to fully understand these changes, we must also consider the genetic ‘wounds’ themselves, including those that are repaired without leaving a trace.”

To read out where in the genome repair succeeds or fails, Afek’s team built a chip loaded with thousands of short synthetic DNA sequences, each engineered with identical damage but distinct flanking sequence context. Doctoral student Noga Levy, who led the study, says the three repair enzymes tested behaved “like a good editor examining every word in its context”— sensitive to the exact combination of bases surrounding the damage, with bases even five positions away influencing recognition and binding. The preferred sequences shared structural traits; one enzyme favored sequences producing an unusually narrow region of the DNA helix.

To explain that structural preference, the Weizmann group partnered with Brian P. Weiser’s team at Rowan University for a computer simulation, which showed an amino acid on the enzyme scanning nearby sequence and gravitating toward the negative charge typical of narrow helical regions.

The team then asked whether these sequence preferences left a mark on the genome over evolutionary time. DNA’s paired-base structure normally keeps A matched with T and C with G; a common form of damage swaps out a C base, breaking that pairing, and repair enzymes are tasked with catching and correcting it. The researchers found that genomic regions repaired efficiently by one enzyme retained more C bases, while regions it repaired poorly accumulated more unrepaired changes—a correlation matching their prediction. “This finding paves the way for a better understanding of the course of human evolution,” Afek says. “To identify which genomic changes were adopted by humans to survive a changing environment, we must first understand which changes accumulate naturally due to the preferences of the repair mechanisms.”

The same dynamic plays out within a single lifetime, not just across evolutionary time: tumors arise when mutations pile up in one cell until it multiplies uncontrollably. “We found a correlation between the repair enzymes’ preferences and mutational signatures—patterns of mutations—in human tumors,” Levy says. “One possibility is that a process in the bodies of cancer patients damaged the repair enzymes, allowing mutations to pile up in areas that were previously protected. Alternatively, over the course of evolution, the repair enzymes may have adapted to target regions that are inherently more vulnerable. Either way, this reinforces our understanding that repair mechanisms play a key role in how cancer develops.”

A separate project in Afek’s lab, led by graduate student Noga Carmon, is now extending this approach to additional repair mechanisms. “Understanding how repair mechanisms ‘choose’ their targets opens up all kinds of ways to harness them for our own needs,” Afek says. “We already use these enzymes as tools in gene editing. A deeper understanding will allow us to fine-tune these technologies and perhaps even engineer enzymes that provide better genetic protection. Since failures in repair mechanisms are a primary driver of cancer, my hope is that understanding them will pave the way for new, more targeted therapies.”]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327647-dna-repair-enzymes-favor-specific-genome-sequences-study-finds https://www.seqanswers.com/forum/sequencing-technologies-companies/illumina-solexa/327646-miseq-tear-down Wed, 12 Aug 2026 17:00:20 GMT Is there a detailed video tear down of the original MiSeq? Is there a detailed video tear down of the original MiSeq? Illumina (Solexa) cement_head https://www.seqanswers.com/forum/sequencing-technologies-companies/illumina-solexa/327646-miseq-tear-down https://www.seqanswers.com/forum/news/327642-single-cell-atlas-pinpoints-spinal-neurons-controlling-movement-speed Tue, 11 Aug 2026 18:35:45 GMT Scientists at St. Jude Children’s Research Hospital have built a single-cell atlas of V1 interneurons, a key class of spinal neurons involved in... Scientists at St. Jude Children’s Research Hospital have built a single-cell atlas of V1 interneurons, a key class of spinal neurons involved in motor coordination, and used it to identify a small subgroup tied to the speed of rhythmic movements such as walking. The findings, which carry implications for understanding spinal cord function and recovery from injury, were published in Nature Communications.

A group of neurons running the length of the spinal cord processes and coordinates signals from the brain and peripheral nervous system on their way to motor neurons, which drive muscle contraction and control movement. These interneurons include a population called V1 interneurons, long known to be involved in motor control, though the roles of their specific subgroups had remained unclear. By comprehensively documenting V1 interneurons in a mouse model, the study clarifies how these cells carry out multiple motor functions.

“Understanding the role of different neurons gives us better insights into how these brain-to-muscle neuronal circuits work, which may be important for helping aid recovery after spinal cord damage,” said corresponding author Jay Bikoff. “Providing this single-cell database and showing how it can be used to understand function is a first step toward that goal.”

To build the atlas, the researchers used single-nucleus sequencing to measure gene expression in V1 interneurons from a mouse model, organized the cells into subgroups based on molecular features, and created an online database for other researchers to explore. They then turned to a longstanding question: how the spinal cord maintains rhythmic movement. Prior work showed V1 interneurons help control the speed of behaviors like locomotion, and removing them slows the rhythm and disrupts limb flexion and extension. But because V1 interneurons include many subgroups, it wasn’t known whether the same cells governed both speed and flexion-extension, or whether those roles were split among different cells.

Bikoff’s group compared their atlas to a single-cell screen from mice missing a key gene in V1 cells. Those mice had slower movement rhythms but no hyperflexion, and the comparison showed that only one group of cells was missing. “We found that slowed locomotor speed and hyperflexion were separable, suggesting they may be controlled by different cells,” Bikoff said. “Intriguingly, we found that loss of one small subpopulation of V1 interneurons, V1Pou6f2, was associated only with slowed speed, implicating these neurons in controlling the speed of our body’s rhythmic movements but not flexion or extension.”

The study demonstrates how a single-cell atlas can subdivide a large neuron population to reveal the roles of its individual parts, giving researchers a resource to generate and test new hypotheses about spinal cord circuitry. “We showed that this single-cell interneuron database can be used as a resource to tease apart how a multifunctional population of cells can be separated into its individual components,” Bikoff said, adding that it “can reveal as-yet unknown biology, giving it the potential to discover new parts of neuronal circuitry that may help us find new ways to improve recovery for patients with spinal cord damage.”
]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327642-single-cell-atlas-pinpoints-spinal-neurons-controlling-movement-speed https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327639-single-cell-rna-seq-analysis-scrn03-–-live-online-course Thu, 06 Aug 2026 19:59:14 GMT Single-cell RNA-Seq Analysis (SCRN03) – Live Online Course https://prstats.org/course/single-cell-rna-seq-analysis-scrn03/ Delivered by... Single-cell RNA-Seq Analysis (SCRN03) – Live Online Course

https://prstats.org/course/single-ce...alysis-scrn03/

Delivered by experienced bioinformaticians with expertise in single-cell transcriptomics, RNA sequencing, and computational biology.

Learn how to analyse single-cell RNA-Seq (scRNA-seq) data in R using Seurat, covering modern workflows for quality control, normalisation, clustering, dimensionality reduction, integration, differential expression, and cell type annotation.

Single-cell RNA sequencing has transformed transcriptomics by enabling gene expression to be measured at cellular resolution. This course provides practical, hands-on training in analysing scRNA-seq datasets using Seurat, from raw count matrices through to biological interpretation. Participants will work through complete analysis pipelines widely used in genomics and bioinformatics research. What you'll gain

• Understanding of single-cell RNA-Seq technologies and experimental design
• Practical experience using Seurat for end-to-end scRNA-seq analysis
• Skills in quality control, filtering, normalisation, feature selection, clustering, and dimensionality reduction
• Experience performing differential expression analysis, dataset integration, and cell type annotation
• Confidence interpreting and visualising single-cell transcriptomic datasets

Course format

• Live, instructor-led online training
• Hands-on coding in R using Seurat
• Real single-cell RNA-Seq datasets
• Interactive practical exercises throughout
• Research-focused workflows suitable for publication-quality analyses

Who is this course for?

• Bioinformaticians
• Computational biologists
• Genomics researchers
• RNA-Seq users moving into single-cell analysis
• PhD students and researchers working with sequencing data

Why take this course?

Single-cell RNA-Seq has rapidly become a standard technology across genomics, with Seurat established as one of the most widely used analysis frameworks. Researchers increasingly require robust computational skills to process large-scale single-cell datasets, integrate experiments, identify cell populations, and extract biologically meaningful insights.

This course provides practical experience with the complete Seurat workflow, enabling participants to confidently analyse their own single-cell RNA-Seq datasets using current best practices employed across academia and industry. Learn more & enrol

https://prstats.org/course/single-ce...alysis-scrn03/

Questions?

Email: oliver@prstats.org]]> Bioinformatics PR Stats https://www.seqanswers.com/forum/bioinformatics/bioinformatics-aa/327639-single-cell-rna-seq-analysis-scrn03-–-live-online-course https://www.seqanswers.com/forum/news/327638-consortium-completes-first-full-diploid-human-genome Thu, 06 Aug 2026 15:41:24 GMT Scientists have reconstructed the complete genome of a real person, including full sets of chromosomes from each parent, an achievement expected to... Scientists have reconstructed the complete genome of a real person, including full sets of chromosomes from each parent, an achievement expected to advance research, improve diagnosis of genetic diseases, and make personalized genomics a routine part of medical care. The work comes from the Telomere-to-Telomere (T2T) Consortium, led by researchers at Johns Hopkins University, the National Human Genome Research Institute, and the National Institute of Standards and Technology, and establishes the most complete and highest-quality human genome sequence built so far, filling gaps that previous approaches missed.

“It will soon become commonplace to sequence an individual’s entire genome,” said Adam Phillippy, senior author of the research published in Cell. “What will that enable? And what does the future of medicine look like when you can generate someone’s complete genome at birth, attach it to their medical record, and then use that to inform precision medicine throughout their life? Complete, personalized genomes are now possible for anyone.”

The findings, published as part of a 12-paper package in Cell and Cell Genomics, build on the T2T Consortium’s 2022 completion of the first full human genome, which filled in the last 8% of a single genome sequence. This time, the team reconstructed a “diploid” genome, containing two distinct chromosome copies, one from each parent. “The first T2T project was like assembling a huge jigsaw puzzle,” Phillippy said. “This time, we had pieces from two similar puzzles, one from mom and one from dad, all thrown into the same box. So, it’s a harder computational challenge, but we’ve figured it out.”

The team sequenced the HG002 genome, a widely used reference sample from a living donor, spanning each chromosome “telomere to telomere” and revealing 15% more of the genome than before, including regions tied to cancer and neurological disorders, plus more than 900 million previously missing DNA letters. Co-senior author Justin Zook said the work “gives technology developers the standard they need to measure and improve accuracy across the most complex regions of the human genome.”

Phillippy called it “a paradigm shift from trying to find the differences between your genome and a reference to actually reconstructing your complete, unique genome.” The Human Genome Project cost roughly $5 billion in today’s dollars; a more complete result can now be produced for about $5,000. Phillippy said this could close diagnostic gaps in rare genetic diseases, where doctors currently can’t identify a cause in over half of cases, and eventually improve risk prediction for cancers, heart disease, and neuropsychiatric conditions. Companion papers applying the same approach to species including macaque, zebra finch, and giraffe are also expected to inform research into evolution and biodiversity.
]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327638-consortium-completes-first-full-diploid-human-genome https://www.seqanswers.com/forum/news/327637-long-read-sequencing-closes-decades-old-gaps-in-the-medaka-genome Mon, 03 Aug 2026 18:13:47 GMT Scientists have completed a long-standing effort to fully sequence the genome of the medaka, or Japanese rice fish, a widely used model organism in... Scientists have completed a long-standing effort to fully sequence the genome of the medaka, or Japanese rice fish, a widely used model organism in biological research. The work builds on genome sequencing efforts from nearly 10 and 20 years ago, when technological limitations left the genome only partially resolved.

In the first study, researchers used traditional Sanger sequencing, producing reads of about 800 to 1,000 base pairs. This approach left low-quality sequence in parts of the roughly 800 Mb genome and created many gaps. A decade later, in 2017, the team applied PacBio SMRT sequencing to two Japanese medaka strains, Hd-rR and HNI, and one Korean strain, HSOK. The longer reads, spanning tens of kilobases, improved the assembly but still left about 1,000 gaps.

The research, led by scientists from The University of Tokyo and the National Institute of Genetics, part of the Research Organization of Information and Systems (ROIS) in Japan, was published in Genome Research.

According to Yoshihiko Suzuki, primary author of the paper, the goal was to complete the medaka genome sequence and uncover biological information hidden in its gaps, including insights into chromosome stability, mobile DNA and sex determination. Many of these gaps fell within repetitive regions, including centromeres, telomeres, ribosomal DNA arrays, the XY chromosomes and the mobile genetic element Teratorn.

To resolve these regions, the team combined two newer sequencing technologies: PacBio HiFi sequencing, which produces highly accurate long reads, and Oxford Nanopore ultra-long sequencing, which generates reads hundreds of kilobases long. Together, these methods allowed the researchers to fully assemble the Hd-rR strain and nearly complete the HNI and HSOK strains, enabling comparisons of centromeric sequences, transposons and sex chromosomes across strains and species.

The comparisons showed medaka centromeric satellite sequences are more conserved between strains than in other eukaryotic organisms, which may help stabilize kinetochore attachment. The team also found that Teratorn, a herpesvirus genome fused to a transposable element, persists in the genome and can influence nearby gene expression, including medaka fin shape. Genes on the X and Y chromosomes remain paired except near the Y-specific region, a pattern that differs from the threespine stickleback's much older Y chromosome.

Suzuki said the next step is to test the functions of these newly identified regions, with the ultimate goal of using complete genome information to understand how DNA sequence differences shape traits in vertebrates, and eventually to help clarify human genome function.]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327637-long-read-sequencing-closes-decades-old-gaps-in-the-medaka-genome https://www.seqanswers.com/articles/327631-beyond-crispr-cas9-understand-choose-and-use-the-right-genome-editing-tool Mon, 03 Aug 2026 14:54:00 GMT CRISPR/Cas9 sparked the gene editing revolution for both research and therapeutics.1 But this system still showed severe issues that limited its...


CRISPR/Cas9 sparked the gene editing revolution for both research and therapeutics.¹ But this system still showed severe issues that limited its applications. The most prominent were the heavy reliance on PAM sequences, delivery limitations, double-stranded breaks that prompt unintended edits and cell death, and editing inefficiency (both in targeting and in knock-in reliability).

Despite this, “CRISPR helped turn genome editing from a specialized technique into a standard research tool. We now have clinical evidence that CRISPR-based approaches can change the course of disease,” said Mollie Schubert, Senior Innovation Product Manager at Integrated DNA Technologies.

“Since the first CRISPR/Cas9 applications, advances in editing technologies—including base editors, prime editors, and recombinases—have expanded the precision and control of genome modifications. Continued improvements in delivery platforms… have and will continue to broaden the range of therapeutically addressable targets,” added Loren Schoch, VP Gene Editing Therapeutics at Aldevron.

But with this new wave of next-generation gene editing techniques, tools, and reagents, scientists need to make the right decision for their experiments, whether they are for research or future therapies.

This article covers the current approaches and key considerations to help you choose the right one.




Figure 1. New technologies in genome editing. The image shows some of the emerging methods that are driving genome editing forward. Source: Pacesa, Pelea & Jinek. Cell, 2024. Figure 5, under CC_BY 4.0.²

A brief look at current gene editing approaches (2026)

The first part of choosing the right approach is to understand them. This brief table showcases the main general approaches of gene editing and how they work.

Table 1. Main gene editing approaches and how they work



Read more about the evolution of gene editing approaches and how they work.

Main considerations when choosing between gene editing approaches

With such a wide palette of techniques, and advances being published constantly, decisions can be hard to make. Using the wrong approach will cost researchers money and time.

“A common mistake is optimizing only for on-target efficiency without thinking through the broader biological consequences of the editing approach. Researchers should start with the biological question: what change is needed, in which cell type, and how durable does that change need to be?” Schubert stated.

When it comes to therapies, some of these questions are of extreme importance. “Delivery remains a primary hurdle, as many cell and tissue types are inherently difficult to access or are resistant to uptake of genome editing payloads. Scalable manufacturing, regulatory clarity, and, ultimately, reimbursement remain important barriers to broader adoption. Continued innovation in these areas will help improve the predictability, accessibility, and scalability of future treatments,” Schoch added.

It is important to remember that despite the significant advances, gene editing is still unpredictable and carries risk. For example, base editing and prime editing still show multiple off-target effects,^8,9 and gene therapy trials have resulted in holds, side effects, and deaths because of issues with the delivery methods and editing molecules.¹⁰

Preventing the main issues

Once a question is asked and an experimental approach chosen, researchers can and should still try to ensure the best results.

“The biggest sources of variability (in a gene editing experiment) are delivery efficiency, cell type and cell state, repair pathway activity, and reagent quality. Researchers can control for these by validating delivery up front, using the right controls and orthogonal readouts, choosing formats suited to the cell model, and sourcing consistent, high-quality editing components,” explained Schubert.

Here is how you can apply those tips directly to your experiments:

1. Validate delivery up front using a reporter with your delivery system (such as a fluorescent protein) to see how many cells actually receive the payload.
2. Perform orthogonal readouts by using two or more independent methods to assess the results of the experiments and determine if the edits occurred.
3. Choose the right format for your cell model, as plasmid DNA, mRNA, or viral vectors do not work the same way on different cell types.
4. Source high-quality editing components, making sure RNAs and proteins have high purity, low lot-to-lot variability, and come with thorough QC.

From research to therapeutics

Advancing gene editing from research settings to clinical trials and new therapies faces multiple challenges. Among them, regulatory hurdles, manufacturing, and reimbursement matter. But on the scientific side, off-target edits and delivery are the main safety issues for patients and researchers.

“Before advancing a gene-editing therapy, off-target characterization needs to be comprehensive, product-specific, and tied to the intended clinical context. That usually means combining in silico prediction, unbiased genome-wide methods, targeted deep sequencing, and assessment of chromosomal integrity in relevant cells or tissues, with enough sensitivity to support a clear risk-benefit rationale,” Schubert explained.

“Comprehensive evaluation of editing specificity and genomic integrity is now a core component of therapy development from the earliest stages and safety profiling before translating into the clinic,” Schoch added.

Delivery is crucial because approaches from viral vectors to lipid nanoparticles determine which cells, and how many, receive the treatment. Delivery often results in side effects, with adverse reactions caused by the delivery format as much as by the molecular payloads¹¹.

What the next decade of genome editing looks like

Genome editing will continue to evolve. From modified proteins and RNAs to better manufacturing and delivery approaches, changes will keep expanding what researchers can do both in the lab and the clinic. And this field has the potential to revolutionize not only research and human health, but also agriculture, animal health, and the environment.²

The experts working on gene editing seem to agree that new innovation will continue to advance the field. “In 10 years, genome editing is likely to be an established therapeutic modality. Together, advances in delivery, translational experience, and regulatory clarity will drive genome editing from a promising innovation to a routine component of the therapeutic toolkit,” Schoch explained. And if innovation continues at the same pace as it has since CRISPR-Cas9 was first demonstrated as a programmable genome-editing tool in 2012, Schoch is likely to be more right than wrong in his prediction.

References

1. Gostimskaya I. CRISPR–Cas9: A History of Its Discovery and Ethical Considerations of Its Use in Genome Editing, 2022, Biochemistry (Moscow), DOI:10.1134/S0006297922080090
2. Pacesa M, Pelea O, Jinek M. Past, present, and future of CRISPR genome editing technologies, 2024, Cell, DOI:10.1016/j.cell.2024.01.042
3. Guan K, Fregoso Ocampo R, et al. Comparative characterization of Cas12f orthologs reveals mechanistic features underlying enhanced genome editing efficiency, 2026, Nature Structural & Molecular Biology, DOI:10.1038/s41594-026-01788-6
4. Kantor A, McClements ME, MacLaren RE. CRISPR-Cas9 DNA Base-Editing and Prime-Editing, 2020, International Journal of Molecular Sciences, DOI:10.3390/ijms21176240
5. Rainaldi J, Mali P, Nourreddine S. Emerging clinical applications of ADAR based RNA editing, 2025, Stem Cells Translational Medicine, DOI:10.1093/stcltm/szaf016
6. Witte IP, Lampe GD, et al. Programmable gene insertion in human cells with a laboratory-evolved CRISPR-associated transposase, 2025, Science, DOI:10.1126/science.adt5199
7. Otero CP, Qi LS. Rewriting the epigenome: CRISPR tools for biological discovery and therapeutics, 2026, Current Opinion in Biomedical Engineering, DOI:10.1016/j.cobme.2026.100658
8. Shmuel-Eidelman M, Cohen-Fultheim R, Eisenberg E, Levanon EY. Off-target RNA editing hotspots caused by base editors, 2026, Molecular Therapy, DOI:10.1016/j.ymthe.2025.12.043
9. Zheng J, Wu M, et al. Prime Editing Exhibits Limited Genome-Wide Off-Target Effects in Cellular and Embryonic Gene Editing, 2026, Cells, DOI:10.3390/cells15050438
10. Wills CA, Drago D, Pietrusko RG. Clinical holds for cell and gene therapy trials: Risks, impact, and lessons learned, 2023, Molecular Therapy — Methods & Clinical Development, DOI:10.1016/j.omtm.2023.101125
11. Shchaslyvyi AY, Antonenko SV, Tesliuk MG, Telegeev GD. Current State of Human Gene Therapy: Approved Products and Vectors, 2023, Pharmaceuticals (Basel), DOI:10.3390/ph16101416

About the author: Darío Sánchez Martín is a scientific writer and molecular biologist, specializing in biotechnology, molecular biology, and nanotechnology. He holds a Ph.D. in Biotechnology from Uppsala University, where he developed nanoparticle-based visual and magnetic assays to detect antimicrobial resistance genes. He co-founded and works at Helixa Communications as a scientific writer, helping life-science companies turn complex science into clear and persuasive content for diverse audiences.]]> Articles SEQadmin2 https://www.seqanswers.com/articles/327631-beyond-crispr-cas9-understand-choose-and-use-the-right-genome-editing-tool https://www.seqanswers.com/forum/site-news/webinar-series/327629-genomic-surveillance-as-a-public-health-measure Fri, 31 Jul 2026 15:00:01 GMT Next-generation sequencing (NGS) is now central to outbreak investigations and antimicrobial resistance tracking. Through sophisticated sequencing...

Next-generation sequencing (NGS) is now central to outbreak investigations and antimicrobial resistance tracking. Through sophisticated sequencing and bioinformatics methods, scientists can track respiratory, enteric, and emerging pathogens in the environment and in populations, turning sequence data into actionable surveillance.

In this webinar, Heba Mostafa from Johns Hopkins Medicine and Joshua Levy from Scripps Research will share their work involving genomic surveillance of pathogens.

Topics to be covered:

• Genomic surveillance of infectious diseases, from evolutionary insights to clinical applications
• A quantitative multimodal framework for global genomic surveillance

Who should attend:

• Genomics researchers
• Epidemiologists
• Bioinformaticians
• Public heal scientists
• Microbiologists
• Infectious disease researchers

Register Here]]> Webinar Series SEQadmin2 https://www.seqanswers.com/forum/site-news/webinar-series/327629-genomic-surveillance-as-a-public-health-measure https://www.seqanswers.com/forum/news/327627-new-genomic-method-uncovers-ancient-hominin-dna Fri, 31 Jul 2026 10:55:46 GMT UC Berkeley researchers have developed a new computational technique that identifies regions of the human genome inherited from previously unknown... UC Berkeley researchers have developed a new computational technique that identifies regions of the human genome inherited from previously unknown archaic hominins, using only genome sequences from present-day humans. The method, called TRACE (TRacking Archaic Contributions via ARG Estimation), reconstructs an ancestral recombination graph (ARG)—a detailed map of how DNA segments across the genome are related through shared ancestry over time—from hundreds of modern human genomes.

While ancient DNA extracted from Neanderthal and Denisovan fossils previously revealed those lineages’ contributions to the modern human genome, no DNA has been recovered from other extinct hominins, making their contributions difficult to detect. TRACE addresses this by identifying genomic regions whose ancestry traces back unusually far in time, even without a reference ancient genome.

“Genealogies preserve a record of our evolutionary past,” said Priya Moorjani, senior author of the paper published in Science. “TRACE reconstructs those histories across the genome. By identifying regions whose ancestry extends unusually far back in time, we can uncover genetic contributions from extinct human populations, even in the absence of ancient DNA.”

Applying TRACE to modern genome data, the team confirmed known regions of Neanderthal DNA, which makes up about 1% of the human genome, and correctly identified Denisovan introgression in genomes from Asia and Oceania. But many ancient regions matched neither lineage. The researchers traced these segments to two distinct, previously uncharacterized lineages with different introgression timelines: a “ghost” lineage found in all modern human genomes, comprising roughly 0.5% to 1% of each individual’s genome, and a “super-archaic” lineage detected only in regions of Denisovan ancestry within genomes from Oceania, where Denisovan DNA reaches up to 4%.

“We discovered that about 2% of the modern human genome is from archaic hominins,” said Yulin Zhang, co-first author of the study. Arjun Biddanda, co-first author, noted that “These contributions are widespread throughout the genome, and ghost ancestry is detected even in regions previously thought to be intolerant of Neanderthal and Denisovan ancestry.”

Many of these archaic segments are enriched in genomic regions associated with immunity and metabolic function. Moorjani said she hopes expanding genome databases and additional Denisovan genome sequences will help detect further archaic lineages, adding that TRACE “should also work with other species.”]]> News SEQadmin2 https://www.seqanswers.com/forum/news/327627-new-genomic-method-uncovers-ancient-hominin-dna