nf-corePipeline release! nf-core/rnaseq v3.26.0 - nf-core/rnaseq v3.26.0 - Chromium Cuttlefish!
RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.
Please see the changelog: https://github.com/nf-core/rnaseq/releases/tag/3.26.0
Pierre LindenbaumRE: https://mstdn.science/@nf_core/116460527673313236
kudos to my internship student who added stringtie_merge to the workflow. 🥳
Pipeline release! nf-core/rnaseq v3.25.0 - nf-core/rnaseq v3.25.0 - Plutonium Pangolin!
RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.
Please see the changelog: https://github.com/nf-core/rnaseq/releases/tag/3.25.0
Mark Ziemann 🇺🇦🌻🦩Join us for a 2-part workshop on Mastering Reproducible Enrichment Analysis! 📊
Presented by Anusuiya Bora and myself, with a focus on reproducibility and best practices.
📅 When: 12 and 13 May 2026
🕑 Time: 2:00 PM – 4:00 PM (AEST)
📍 Where: Online
💰 Cost: FREE for academic sector (places are limited!)
🔗Registration form link: https://lnkd.in/gQcHggGF
#Bioinformatics #RNAseq #scRNAseq #Genomics #ReproducibleResearch #OpenScience #RStats
Pipeline release! nf-core/rnaseq v3.24.0 - nf-core/rnaseq v3.24.0 - Selenium Seahorse!
RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.
Please see the changelog: https://github.com/nf-core/rnaseq/releases/tag/3.24.0
CSBJ🧬 Can AI truly decode gene expression, or is it being misled by too much data?
🔗 Artificial Intelligence in Bulk RNA-Seq: Challenges and Potential Solutions. Computational and Structural Biotechnology Journal (CSBJ). DOI: https://doi.org/10.34133/csbj.0039
📚 CSBJ - A Science Partner Journal: https://spj.science.org/journal/csbj
#ArtificialIntelligence #MachineLearning #Bioinformatics #Genomics #RNAseq #ComputationalBiology #SystemsBiology #AI #PrecisionMedicine #BigData #AIinHealthcare
Dr. Robert M FlightIf you are someone who has ever thought running a bunch of samples through recount3 on your own hardware is a good idea, and you are horrid at writing shell scripts to manage it all, I've created a little #RStats package that helps with:
- running samples through recount-pump and unify;
- copying unify outputs into a directory that recount3 will see and load;
- checking your fq.gz files to make sure they aren't bad before running pump.
Phil Ewels
Albert Cardona"Cellular morphology emerges from polygenic, distributed transcriptional variation", Paylakhi et al. 2026
https://www.biorxiv.org/content/10.64898/2026.03.12.711281v1
Cellular morphology emerges from polygenic, distributed transcriptional variation
Height and most disease risk are known polygenic traits: characteristics governed by multiple genes at different loci instead of a select few. Though we are beginning to understand how genetic variation impacts cell morphology, whether such an analogous polygenic architecture operates at the cellular level, where morphology integrates cytoskeletal organization, organelle positioning, and metabolic state, has yet to be systematically tested. Here, we demonstrate that cellular morphology behaves as a polygenic trait by integrating multimodal modeling, perturbation profiling, and population scale genetic variation. A shared latent-space autoencoder trained on four large perturbation datasets predicts morphology from gene expression and generalizes without retraining to matched RNA-seq and Cell Painting profiles from 100 genetically diverse iPSC donors. The model predicted 17 morphological features (R > 0.6, permutation FDR q < 0.05), enriched for spatial organelle distribution and cytoskeletal architecture. Predictive performance does not arise from dominant gene-phenotype relationships: individual genes contribute modestly, and marginal gene-morphology correlations are uniformly weak, revealing a distributed regulatory architecture. Despite this polygenicity, CRISPR perturbation data from the JUMP consortium validates specific model-prioritized genes, such as the cytoskeletal regulator TIAM1, membrane trafficking factor RAB31, and mitochondrial-associated membrane transporter ABCC5, as molecular anchors whose disruption produces feature-specific morphological shifts. Transcriptome-wide association analyses identify correlational variant-gene-morphology chains linking cis-regulatory variation through mitochondrial metabolism (PDHX) and iron transport (SLC11A2) to cellular architecture. These results establish cellular morphology as a polygenic systems phenotype, extending the omnigenic framework to the cellular level and providing a biological basis for interpreting cross-modal prediction in functional genomics. ### Competing Interest Statement The authors have declared no competing interest. AnalytiXIN Fellowship in Life Sciences
Pipeline release! nf-core/rnafusion v4.1.0 - 4.1.0!
Please see the changelog: https://github.com/nf-core/rnafusion/releases/tag/4.1.0
#fusion #fusiongenes #genefusion #rna #rnaseq #nfcore #openscience #nextflow #bioinformatics