Epigenetic fingerprints link early-onset colon and rectal cancer to pesticide exposure - Nature Medicine An analysis of exposome traits in patients with early-onset colorectal cancer (CRC) (<50 years) compared with late-onset CRC (≥70 years) based on epigenetic markers shows that pesticide usage, in p...

Delighted to share our new paper which has just been published in Nature Medicine. www.nature.com/articles/s41...

Those amazing #phages 🦠 figured out 5mC chemistry before we did:
📄 Out today in @MolecularCell we describe a 5mC-selective #deaminase family in environmental phage genomes with broad applications for #5base #sequencing
#epigenetics #DNAmethylation #genomics #NGS 🧬🧪
www.cell.com/molecular-ce...

Interesting changes to ERC eligibility, low scores now exclude you from reapplications for up to 3 years.

Tumour promotion through the lens of evolution - Nature This Review revisits tumour initiation and promotion in light of clonal diversity and the presence of cancer driver mutations in normal tissues, aiming to understand mechanisms that enable environment...

new out in Nature: wonderful review article on tumour promotion www.nature.com/articles/s41...

Excited to share our latest work in Nature. Applying single-molecule and single-cell DNA sequencing methods, we uncover an extraordinary landscape of somatic mutations in immune checkpoint genes in autoimmune B cells, suggesting that somatic mutations may be key to autoimmunity [1/n] rdcu.be/fdqbr

How does a cell learn? In our new perspective in @nature.com we propose a model where the properties of the AP-1 family of transcription factors – stress-induced feedback, regulatory combinatorics and cellular memory – encode a mechanism for cellular learning. www.nature.com/articles/s41...

My main gripe with the alphafold example is how it shows you need decades and decades of high quality data, well structured, open and accessible to train a model -- and yet they always gloss over it and pretend it's just AI and magic. No, we need to continuously invest in real data and FAIR data.

Thanks for sharing, I also missed it!

Just in case the latter paper uses sc data, check these methods too!

doi.org/10.1093/bib/...
doi.org/10.64898/202...

Hope it helps! :D

If you use dim. reduction, you may be interested in two recent preprints we've posted on contrastive PCA:
The Rayleigh Quotient and Contrastive Principal Component Analysis I & II
w/ Maria Carilli & Kayla Jackson. They cover a lot of ground from theory to practice. 1/🧵

Data. A mouse model of glioblastoma leads to inevitable death within 50 days. Delivery of HSV+TK plus IL2, driven by a strong and specific synthetic superenhancer, allows almost all mice to survive even after nearly 150 days. From Fig5 of Koeber et al 2026

Just look at this graph (Fig 5A,B from Koeber et al).

Amazing.

Congratulations to the Pollard lab and all authors.

www.nature.com/articles/s41...

I dropped you a mail :)

I just saw simpleaf.readthedocs.io/en/latest/fl.... I was not aware of it!

Thanks for sharing it! Really needed study

We're looking for a publicly available 10x Flex v2 gene expression dataset to test out our simpleaf pipeline on (we need raw FASTQ data and, ideally, CellRanger count matrices). Unfortunately, 10x only has Flex v1 data available on their website, and the v2 chemistry is different. Any ideas?

Have you run simpleaf on Flex v1 data so far? How does pseudoalignment deal with a reference of 50bp sequences, especially during indexing? I'm really interested in better modular implementations for preprocessing probe-based data (flexibility in sequencing geometry, omitting UMIs, different CBs...)

I think the following dataset is v1, cause they used Cell Ranger v9 instead of v10 (which supports Flex v2). But it may me a good dataset to benchmark any tool.

www.10xgenomics.com/datasets/320...

It was used here: doi.org/10.64898/202...

Axial Patterning Beyond the Individual: Colony-level Organization in a Siphonophore Colony www.biorxiv.org/content/10.64898/2026.04...

The U-method: Leveraging expression probability for robust biological marker detection www.biorxiv.org/content/10.64898/2026.03...

Fig 1: Study design and transcript discovery pipeline showing Nanopore cDNA libraries from villous placenta (n=72 term births, 36 controls and 36 GDM-affected); comparison of annotated features between GENCODE v45 and lr-assembly showing 63.5% reduction in isoforms and 73.1% reduction in genes; transcript distribution by structural category (FSM, ISM, NIC, NNC, and other classes) for all and high-confidence isoforms; transcriptional breadth across 15 GTEx tissues and cell lines; isoforms detected at increasing expression thresholds with placenta shown as thick black line; and transcriptional complexity as mean isoforms per gene (±1 SD) with placenta maximum of 108..

🎉 It's published! Our placental long-read transcriptome is now in @natcomms.nature.com! Thank you to @arjunbhattac.bsky.social, @jonhuang.bsky.social, and @mikelove.bsky.social for collaborating on this first project of my postdoc @mdanderson.bsky.social. A recap 🧬🫄🧵 www.nature.com/articles/s41...

Struggling to focus on research when the world is ‘on fire’? Some ways to cope Keeping up with distressing news is affecting some researchers’ mental health and their scientific work.

World events getting you down? Here are some handy tips from Nature about studying the world while ignoring what happens on it.

www.nature.com/articles/d41...

Cell type composition drives patient stratification in single-cell RNA-seq cohorts www.biorxiv.org/content/10.6...

SNP calling, haplotype phasing and allele-specific analysis with long RNA-seq reads Nature Methods - In this study, long-read RNA sequencing achieves accurate single-nucleotide polymorphism calling, haplotype phasing and allele-specific expression analysis.

LongcallR for competitive SNP calling and haplotype phasing, and simplified allele-specific analysis with long RNA-seq reads. Found ~100 junctions affected by SNPs per sample with most junctions novel.

Developed by Neng Huang. Published in @natmethods.nature.com. Read at rdcu.be/faKhL

AI is getting really good at tasks with verifiable rewards namely coding.

Computational biology feels like coding but it is not. Much of biology is about identifying and using symbolic causal representations of how things work, and hierarchical values guiding experiments, data, benchmarks.

A high-affinity split-HaloTag for live-cell protein labeling Nature Communications - Lin and colleagues present high-affinity split-HaloTag pairs for protein tagging and multiplexed labelling. This versatile system allows protein visualisation with diverse...

A high-affinity split-HaloTag for live-cell protein labeling. And much more.
www.nature.com/articles/s41...

The issue will be the ease of producing plausibly looking wrong or fabricated research and how to deal with it in science. Open Source software development and conference prepreints are already facing issues with poor quality submissions. But I don't think this means we should not use these models.

Epigenetic memory of colitis promotes tumour growth - Nature Colonic stem cells retain a memory of inflammation following disease resolution and there is a mechanistic link between chronic inflammation and malignancy, suggesting potential strategies to mitigate...

It’s well known that inflammation increases cancer risk, but how?

The answer: the epigenome "remembers" inflammation and primes stem cells for cancer.

Here is our paper: nature.com/articles/s41...
And a special shoutout to the lead author
@snaga13.bsky.social

A 🧵

Statistical Rethinking 2026 is done: 20 new lectures emphasizing logical and critical statistical workflow, from basics of probability theory to causal inference to reliable computation to sensitivity. It's all free, made just for you. Lecture list and links: github.com/rmcelreath/s...

ScienceAdviser: Hidden culprit found for puzzling static electricity Today in Science and science: Turning cellular powerhouses into therapies, a comet falls apart, and more

"That’s why the Center for Scientific Integrity, the nonprofit organization behind Retraction Watch, has launched a new annual award celebrating scientists who discover substantial errors in their published work and take meaningful steps to correct the scientific record."

Embracing Bayesian Methods in Clinical Trials

t.co/7mMecV2EI1

One of my very favorite papers from the lab! Shows that individual cells can learn by forming memories. Amazing work by Jess Li!