What has been the most instructive moment in your scientific career? Being as open as possible about your results and sharing data. In many projects, I’ve received crucial feedback because we shared our results openly and publicly, often before we had published a paper or even a preprint. In my experience, you shouldn’t fear that someone will steal your results. A much bigger risk is that there’s an important error in your analysis or experiment that you’ve missed, but which could be spotted quickly in a short conversation.

Great interview here with @kauralasoo.bsky.social on genetics research and collaborative science.

blog.cs.ut.ee/2025/10/06/k...

1/
🧬 Happy to share our new preprint on modeling cis-regulatory variation in human brain enhancers across a large Parkinson’s disease cohort: www.biorxiv.org/content/10.6...
Details in the thread below:

My lab will be attending #EuroBioc2026 in June. This is one of the three annual conferences for one of the world’s largest open-source research communities. Registration is still open until May 3, and poster submissions for another two weeks. More information: eurobioc2026.bioconductor.org

Check out Rachel's website also for her awesome science communication work:

rrsharp122.github.io

✨ Preprint Alert! ✨ I’m thrilled to share that my latest research is now available on medRxiv! 🧬 🧠 📄 In this work, we evaluated polygenic risk scores (PRS: an estimate of common genetic risk)… | R... ✨ Preprint Alert! ✨ I’m thrilled to share that my latest research is now available on medRxiv! 🧬 🧠 📄 In this work, we evaluated polygenic risk scores (PRS: an estimate of common genetic risk) for ...

Rachel has a post describing the preprint:

www.linkedin.com/feed/update/...

Excited to share this preprint from Rachel Sharp @rrsharp.bsky.social in the lab, in collaboration with a great team of collaborators at UNC and Duke. We examine bipolar disorder case status with social risk variables and with polygenic risk in the All of Us Research Program.

✍️ [blog] Collaborating between Bioconductor and R-Universe on Development of Common Infrastructure by rOpenSci and Bioconductor Teams

The R-consortium ISC top-level project R-universe is working with Bioconductor to gradually modernize parts of its infrastructure while accommodating the […]

Great work Sean! Demonstrates how important it is to have a well-characterized catalog of transcripts for the tissue under study.

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...

New preprint on the genetic regulation of isoform expression and breast cancer risk! TL;DR: tissue-specific, long-read transcript annotations shape eQTL mapping, TWAS, and isoforms we prioritize at GWAS loci. @arjunbhattac.bsky.social

www.biorxiv.org/content/10.6...

Improving isoform-level eQTL and integrative genetic analyses of breast cancer risk with long-read RNA transcript assemblies www.biorxiv.org/content/10.64898/2026.03...

Kermit the frog screaming with excitement

We have summer internships y'all! Come work at Posit on the PyData, tidymodels, shiny, or Connect teams: grnh.se/tigz810a3us. You will have an awesome time, learn a ton, and help advance our open source and pro tools 🧰 #rstats #pydata

Efficient semiparametric estimation of marginal treatment effects with genetic instrumental variables Alcohol misuse is a key target of public health strategies aimed at reducing cardiovascular risk. The effect of excessive alcohol consumption on blood pressure may vary systematically with individuals...

New pre-print on treatment effect heterogeneity led by Ash Patel: "Efficient semiparametric estimation of marginal treatment effects with genetic instrumental variables" available at arxiv.org/abs/2603.08871. Brief summary:

RNA localization to nuclear speckles follows splicing logic Abstract. Nuclear speckles are membraneless organelles implicated in multiple RNA processing steps. In this work, we systematically characterize the sequen

Awesome new work from Jingyi Fei's lab in collaboration with Oded Regev's group.

RNA localization to nuclear speckles follows splicing logic url: academic.oup.com/nar/article/...

Beyond Mendel: a call to revisit the genotype–phenotype map through new experimental paradigms Abstract. The long-standing notion that genotypes map to phenotypes through simple one gene–one trait relationships continues to shape both research in the

Oft Herr Mendel dreams

that genes form living matter.

They do the latter.

doi.org/10.1093/gene...

In collab w @hdashnow.bsky.social, we have a new preprint out on tandem repeat (TR) mutagenesis! Using HiFi sequencing, we profiled nearly 8 million TR loci across four generations of a large CEPH/Utah family and identified 1,270 de novo expansions and contractions.
www.biorxiv.org/content/10.6...

We are pleased to announce that Heather Turner @heathrturnr.fosstodon.org.ap.brid.gy has joined the R Core Team. She has been an active contributor notably via the R Dev Days, and has greatly improved cooperation between R Core and the R community in more ways #RStats.

agree!

Wow! What a tool: half million downloads from Bioconda - Congrats @robp.bsky.social and Combine-Lab! 👏 Happy to see SSHash plugged here.

Release v1.11.4 · COMBINE-lab/salmon Released: 2026-03-11 Full Changelog: v1.10.3...v1.11.4 Overview Salmon v1.11.2 is a major release representing the most substantial architectural overhaul of the salmon codebase since the transiti...

I've been wanting to do a big refactor and build system update for salmon for a looong time. I finally got around to it, and updated the backend index to use the newest SSHash while I was at it : github.com/COMBINE-lab/... (also with native ARM support & on bioconda!)

Also linking to the previous thread by our co-author Sarah Zweifel @sarahbrotman.bsky.social on 'AdipoExpress', one of the meta-analysis eQTL studies compared here.

bsky.app/profile/sara...

Identification of de novo variants from parent-proband duos via long-read sequencing Determining de novo status is often what upgrades a variant from VUS to pathogenic, but it has not been possible without sequencing both biological parents. We developed, extensively evaluated, and ap...

🧬 New from @leandrosboukas.bsky.social & co
📄Identification of de novo variants from parent-proband duos via long-read sequencing
🖥️ github.com/sbergercnmc/...

Very happy to share our work with Seth Berger and UCI-GREGoR at @ajhgnews.bsky.social .

We developed and extensively evaluated a method - duoNovo - that uses long-read sequencing to detect de novo variants using *only one* biological parent. (1/n)

R package: github.com/sbergercnmc/...

Incorporating polygenic risk scores and social determinants of health across populations: Considerations and best practices in research Despite increasing focus on the intersection of genetic and social risk factors on health, heterogeneity in how these factors are measured, distributed, and analyzed across populations and datasets co...

🧬 New Review from Cromer et al!
📄 Incorporating polygenic risk scores and social determinants of health across populations: Considerations and best practices in research
#PRS #SDoH

Are you interested in understanding how social and structural determinants of health influence estimates of genetic risk? Looking for figures for teaching these concepts?

Check out our work on considerations for modeling them together, led by Sara Cromer and Dave Conti with @prsmethods.bsky.social

Mind the gap: Characterizing bias due to population mismatch in two-sample Mendelian randomization We demonstrate that differences between populations can lead to biased causal effect estimates in Mendelian randomization (MR), a causal inference method based on genetic data. We provide a broad empi...

📣New from Li & Morrison!
📄Mind the gap: Characterizing bias due to population mismatch in two-sample Mendelian randomization

Also this thread on pQTL / eQTL comparisons:

bsky.app/profile/tuul...

Thinking about where we are on the power curve is key. Noting also this thread from @ee-reh-neh.bsky.social:

bsky.app/profile/ee-r...

Here is the thread describing the preprint:

bsky.app/profile/mike...

Great to see this work out, led by Jon Rosen and co-supervised Karen Mohlke @klmohlke.bsky.social!

Connecting to some recent threads here: