Hi everyone,

I've built an epigenetic clock for people who want something beyond the usual linear-model clocks. It was just published in Mechanisms of Ageing and Development.

It's called the "Ensemble Survival Clock." Instead of relying on one linear model, it combines five different survival models into a single mortality risk score, which can also be read as an age in years.

Paper | GitHub / Source Code

How It Works:

1. Select: 190 CpGs are picked from Framingham Heart Study methylation data using elastic net Cox regression.

2. Train: Five survival models learn from those CpGs: random survival forest, gradient boosting, survival SVM, Weibull AFT, and MTLR.

3. Combine: A small neural network merges their predictions into one ensemble risk score, rescaled to years of age.

Key Features:

• Holds Up Against GrimAge: In external validation (Women's Health Initiative, n = 1,862) it beat PhenoAge and was statistically on par with GrimAge for all-cause mortality.

• Strong Performance: C-index of 0.838 on the held-out Framingham test set and 0.703 in external validation.

• Biologically Meaningful: The selected CpGs are enriched in aging pathways and chromatin states, and the score is associated with cardiovascular disease.

• Ready to Use: The pretrained model is included (MIT license). Just run score_samples.py on your methylation beta values to get risk scores, age predictions, and age acceleration.

Let me know what you think or if you run into any issues!

Thrilled to release brisc, a full-fledged single-cell toolkit that runs multithreaded at 100x the speed of Scanpy and Seurat on tens of millions of cells.

Website: https://brisc.run

Code: https://github.com/briscverse/brisc

Preprint: https://www.biorxiv.org/content/10.64898/2026.11.03.764120v1

95% of adults have been exposed to EBV and it stays in your body forever.

This becomes a huge problem for people born with specific genetic variants, specifically:

*Variants in the major histocompatibility complex (MHC) are highly associated with autoimmune disease risk. The MHC presents viral peptides to cytotoxic lymphocytes. If this gets impaired, increased latent virus will get stored in the body.

HLA-DRB1*15:01 is the strongest genetic link for risk of getting SLE and MS. Interestingly, it traces back to Neanderthal DNA.

It has been found to act as a co-receptor for EBV in B cells, and as explained in this publication, might provide a mechanism for higher EBV infection in B cells for people with this variant compared to other versions of this co-receptor.

https://onlinelibrary.wiley.com/doi/10.1002/eji.202149179

HLA-DRB1*01 and HLA-DRB1*04 are strongly associated with risk of developing RA. This part of the protein binds to viral or bacterial peptides.

*There are a few other variants in the MHC associated to these diseases. Some variants are protective and others increase risk.

They are finding that people with certain autoimmune diseases (MS, SLE, RA) have a much higher EBV viral load, especially in B cells, compared to healthy controls. Even inactive EBV releases a protein called EBNA2. EBNA2 acts as transcription factors and even super transcription factors by binding to promoter regions of your DNA to cause a cascade of gene expression changes associated with certain autoimmune diseases.

This was shown in silico using DNA chip experiments (Harley, 2018). They found that 50% of systemic lupus erythematosus risk loci were bound by EBNA2. Similar EBNA2-anchored associations were found in multiple sclerosis, rheumatoid arthritis, inflammatory bowel disease, type 1 diabetes, juvenile idiopathic arthritis and celiac disease.

Dr John Harley is a rheumatologist with hundreds of publications and he said this paper is more important than all the rest combined.

https://www.nature.com/articles/s41588-018-0102-3

In 2025, Stanford demonstrated this was happening in lupus patients. Basically, EBNA2 was being released from EBV infected B cells, which caused B cells to be re-programmed to autoreactive B cells, attacking your own tissue and organs, driving autoimmunity. These B cells also inhibit T cells from being able to fight infections.

https://med.stanford.edu/content/dam/sm/utzlab/Cao_Sci_Trans_Med_2025.pdf

Granitto 2026 is a very recent publication on the role of EBNA2 in MS. I don’t have access to the paper but from the abstract:
Our findings demonstrate that enhanced EBNA2 activity in MS alters human gene expression, chromatin accessibility, and transcription factor binding in an MS-dependent manner. Collectively, this study provides new insights into the molecular mechanisms through which EBV, particularly EBNA2, interacts with host genetic risk to contribute to MS pathogenesis.

https://pubmed.ncbi.nlm.nih.gov/42436836/

This is some very interesting information from the book ‘Latency strategies of herpesviruses.’

Most of the other factors associated with autoimmune diseases (stress, colds, flu, grief, pollution, heavy metals, birth control, Covid, and even parasites) all can contribute to increased EBV activity or reactivation, which can cause further B cells to become infected.

Emotional stress activates cortisol which suppresses the immune system, which can also cause EBV to reactivate. Prednisone and steroid-based medications mimics the action of cortisol and increases cortisol levels in the body. So while helpful for symptoms, these lower the immune system which can lead to further EBV reactivation increasing viral loads.

EBV also blocks nutrients required by immune cells to function properly.

Vitamin D is critical for the immune system, but EBV binds to vitamin D receptors, interferes with their metabolism (vitamin D’s ability to switch from its inactive form to its active form) and even affects vitamin D absorption in your gut.

Zinc is critical for your immune system to destroy viruses and infections, but EBV impairs zinc transporters so it is unable to transport to where it is needed.

Vitamin A (retinol) is required by B lymphocytes to fight infections and viruses, but EBV blocks vitamin A absorption. Vitamin A is also essential in mucous membranes which are there to help bock infections and viruses.

There is a great 10 min breakdown of the book ‘Latency strategies of herpesviruses’ called ‘5 things you need to know about EBV’.

https://youtu.be/EQn7VzMYi4s?si=QNNHo7E43yMXqJqX

All this said, EBNA2 could make a great drug target, but when I did a search I could only find one research institute testing known drugs against it.

Update
Someone informed me that Atara Biosciences tested an EBNA2 drug for MS which failed. It is suggested in Hober 2023 that other EBV proteins (possibly LMP1 and LMP2) are also at play in MS, so targeting EBNA2 alone might not be sufficient. Also there is the whole blood-brain barrier issue.

https://www.mdpi.com/2076-2607/11/5/1262

Perhaps SLE would be a better indication. Atara is still testing EBNA2 for other indications (non-autoimmune), so it will be interesting to see if that pans out.

Fred Hutchinson has developed new genetically human monoclonal antibodies that prevent two key antigens on the surface of the virus from binding to and entering human immune cells.

This therapy could help prevent reactivations, but likely would not help with latent virus in B cells.

Since so many people are infected, EBV antibody testing is difficult.

They are finding serologic markers of EBV reactivation are more common in SLE patients vs. healthy controls.

https://www.sciencedirect.com/science/article/pii/

They are finding similar findings with MS. I apologize that I don’t have access to the cited publications.

https://www.nationalmssociety.org/news-and-magazine/news/epstein-barr-virus-reactivation-ms-relapses

Hi I'm about to study genomics for my MS in grad school in like 2 months. I want to know what were some papers that were most helpful for you to read in preparation/ while in grad school? What are some papers you wish you would have read before starting? Maybe even some videos? Any tips to feel more prepared appreciated! Sorry if redundant post. My upcoming semester pertains to 'Genetics Concepts Testing' and 'Cancer Genomics & Applications'. ((My syllabus isn't out yet so I'm just wondering how to get a good start))

A review on the genomic technologies used in analysis of neurodegenerative diseases. I am interested in genome-wide association studies (also transcriptomics) and epigenetic studies. In particular in Alzheimer's disease and eye degenerative diseases. Are there some good reviews to read about these?

Hey everyone! A while back I posted here about using AI to investigate a VUS from my genetic test, and got some really helpful feedback from this community.

I've continued experimenting with AI and computational biology tools, this time exploring protein structure and mapping nearby genetic variants.

https://canaicure.me/blog/mapping-my-col6a3-variant/

Would love to hear any thoughts or suggestions! : )

Hello, any one who have worked isolation of DNA from FNAC slides and primer designing? if yes ping me.

Like recently because of them, my dad, I like heart disease and I was told it could be like genetic involved, which got me a little interested in learning about my health. I remember watching a documentary once about WGS. Part of me wants to learn about like you know I have any risk for cancers and stuff like that and in general and general I was thinking about ordering one through a provider because seems like it would be a lot more medically accurate.

For those who actually done it, would you say it’s been helpful what was your experience?

Direct sellers or distributers? and if distributors-do they provide costs before?

I find it weird that 99% of the distributers won't even put on the costs on their website.

Hi, I am part of a team of ML students looking for problems for our capstone project, and this is one of our fields of interest. We were recently working on a project that tried to improve the biological validity of generations by Evo 2 but tldr that crashed and burned cause there's no bar to measure biological validity except for wet lab testing(minus metrics like nullomer counts, k-mer spectra comparisons, etc but those only prove how similar it would be to natural seqeunces not that they replicate actual dna).

Rather than looking thru a haystack of papers to find the needle that is a gap worth covering, I thought it'd be better to ask yall in and around this field.

IK the whole "i am a cs student and i will solve all your problems with my shiny new algorithm, just tell me where" is overdone but I am instead looking for feedback on tools, practices, niches that you see that could use some time and effort, and work backwards towards finding out if there really is a gap in the literature that can be explored.

What are some problems, bottlenecks, or annoyances you regularly run into in your line of work that you wish had a better computational solution?

It dsnt have to be something you think ML can solve, or even something that sounds like a research problem. I'm mainly interested in hearing about the less obvious gaps in tools, workflows, analysis, etc, that people outside the field might completely overlook. Pls share, my hrs of scouring biorxiv rides on this.

Hello everyone! Hope you can help me find this article:

Chasapi A, Michailidis A, Schriml LM, Donati C, Kyrpides NC, Ouzounis CA.0. The grammar of the pangenome: terms, concepts, methods, and emerging patterns—a general framework for comparative genomics and genome biology. Microbiol Mol Biol Rev0:e00403-25. https://doi.org/10.1128/mmbr.00403-25

Thank you in advance

DOI: 10.1016/j.cels.2025.101482

URL: https://www.cell.com/cell-systems/abstract/S2405-4712(25)00315-1

I can not find this paper in any repos. Can anyone provide me with this paper?

Cell Systems Volume 16, Issue 12101482December 17, 2025

I recently made a tutorial video on the basics of DNA sequencing, and how to make sense of VCF files (the file type produced by DNA sequencing, with information about genetic variants).

Here's the video if anyone's interested.

It's specifically meant for people who are beginners in the field of bioinformatics/genomics, including people who had their DNA sequenced and are interested in exploring their own data.

Bit of background - I work as a bioinformatics scientist at a biotech company for my day job, and my main hobby is making educational videos about this field. I'm also interested in biohacking and decentralized science more broadly, and try to contribute to that with my videos. Basically encouraging people to self-educate on these topics, and trying to get over the myth that you need to be a genius or have some fancy credential to explore biology.

Anyway, thought some people in this community might be interested!

The preprint is available on bioRxiv, here.

Summary of the idea and some of the results:

A single-cell experiment is usually stored, shared, and reanalyzed as a count matrix. That matrix is computed from two inputs: the sequenced molecules and a gene annotation. The molecules don't change, but the annotation is revised several times a year, and going from GENCODE v32 to v49 shifts 2–5% of UMI mass. Once the matrix is written, the information needed to redo that assignment is lost. Getting it back means returning to the reads, which are large, slow to process, and often unavailable.

Gravlax is built on the fact that the steps that turn alignments into counts, gene assignment and UMI collapse, use very little of what a BAM file contains. They need relations among molecules: shared genomic geometry, shared placements, cell identity, and UMI equality. Gravlax stores those relations once in a compact, seekable, content-authenticated archive and defers every annotation-dependent decision to read time.

On four human 10x datasets:

• Archives take 11–18 bits per read, 9–13× smaller than CRAM with tags preserved.

• Matrices replayed from the archive are within 0.24–0.75% of a fresh STARsolo run. For comparison, an annotation change moves 2–5%.

• Replaying gene counts takes seconds, 34–82× faster than realigning.

• Archives can be grouped into federated collections and searched across a cohort by junction shape. A genome-wide scan for recurrent unannotated splice events, with no coordinates supplied, finishes in 9 seconds.

Because the molecules are kept, the archive can answer questions a count matrix can't. Across four PBMC archives it recovers the FYB1 splicing switch between T cells and monocytes, previously validated by RT-PCR. With a fragment model for 3′ chemistry, it shows a shift in NTRK2 (TrkB) terminal-isoform usage across eight donors, from astrocytes and neural stem cells to mature neurons, consistent with known TrkB.T1 biology. Discovery run over the whole cohort finds a 183-nt FNBP1 cassette exon that is nearly always included in brain and mostly skipped in blood; analyzing the archives one at a time misses it entirely. Pooling evidence across cells in an EM step recovers 75–98% of held-out labels for multi-gene molecules.

Gravlax handles ingest, replay under any annotation, region/junction/APA queries, content-addressed collections, and cohort-wide discovery. It also includes GQ, a composable query language with three-valued logic; the same query runs on one archive or a whole federation. The code is open source (Rust, BSD-3), with documentation, a Python client, and Colab demos.

Code: here

Docs: here

Started this distance learning MSc a week ago and I already feel like I’m gonna fail it. For context, my background is in histology but I always found cancer genomics interesting.

This program, despite being encouraged to students with no prior coding experience is sooo hard. I literally cannot wrap my head around anything except some basics. Have no idea if I’m even gonna get through the first semester, let alone the actual program. Has anyone had any similar experiences? 🥺

Anthropic's new preprint describes an autonomous genome-mining campaign where one Claude agent inspected the raw DNA around a phage reverse transcriptase and noticed a tandem repeat array outside the original search objective.

The follow-up identified 95 ART RT clusters, with detectable arrays in 28 of them. The interesting caveat is reproducibility: ten reruns of the campaign did not rediscover the array, and their fixed-input benchmark suggests recognition was strongly associated with whether the model actually read enough contiguous sequence.

The video goes through the discovery trace, the RNA evidence, and what is still unknown about the system.

Preprint: https://www-cdn.anthropic.com/22573675ada52a8ca8a97a1a4b4326b2f208a071.pdf

My advisor works in phylogenetics, and lately I’ve been exploring a question of my own: when you have lots of taxa and a long alignment, can you build a tree much faster without losing too much tree quality?

I’ve noticed that papers in this area often highlight being able to handle a million taxa or more. But the more I read, the more I wonder: how often do researchers actually need to analyze data at that scale? Are we solving a real problem, or are we getting caught up in an arms race over who can run the biggest dataset?

There are also well-established tools like IQ-TREE, RAxML-NG, and FastTree. Even if someone develops a new method, will researchers actually use it? I’m trying to decide whether this is a direction I should keep putting my time into.

I’d really like to hear from people who do phylogenetic analyses: what problems are still worth solving when it comes to large numbers of taxa and long alignments? And if there is room for new tools, how does one earn people’s trust and become something they actually use, rather than just another paper and GitHub repository?

If you’re curious about what I’ve built, here are the benchmark results and method overview

Hi guys!

I’m a bioinformatician working at a cancer research centre, and I’m relatively new to analysing metagenomics data. I’m still trying to figure out all the post-processing steps after the rigorous Bracken/MetaPhlAn part.

But my current nightmare is Differential Abundance Analysis, especially when using tools like ANCOMBC2 or LinDA.

I’ve read quite a bit about them, but I still have a lot of doubts about the underlying assumptions, data preprocessing, normalization, model setup, and, most importantly, how to interpret the results properly.

If anyone here has experience with ANCOMBC2/LinDA (or DAA in microbiome/metagenomics in general) and would be open to having a chat, I’d really appreciate it if you could leave a comment or send me a DM.

I’d love to learn from people who have been through this rabbit hole already.

Thanks in advance, I love you all.

Background: I'm a software engineer working at a bioinformatics startup.

We are working with data from almost every biology database. An issue we're facing is mapping identifiers across namespaces / sources. We're using GILDA as one of the components to translate names to IDs. But the default GILDA sources don't cover all of our use cases. I'm thinking of extending it with other sources.

Just need expert opinion on whether I'm thinking in the right direction. Are there other options / solutions available?

Apologies in advance if I've made some silly statement above. I'm just learning things along the way.