Pipeline release! nf-core/spatialaxe v1.0.0 - 1.0.0!
A bioinformatics best-practice processing and quality control pipeline for Xenium and Artera data
Please see the changelog: https://github.com/nf-core/spatialaxe/releases/tag/1.0.0
#10xgenomics #atera #imageprocessing #spatial #spatialdataanalysis #spatialtranscriptomics #transcriptomics #xenium #nfcore #openscience #nextflow #bioinformatics
Our paper (with Julie Cartier, Johanna Lagoas, Youmna Ayadi, Adeline Fermanian and @flomass) on the use of statistical knockoffs for the differential analysis of transcriptomics data just came out, very appropriately as it nicely illustrates my point:
https://academic.oup.com/bib/article/27/3/bbag148/8687371
Using simulated outcomes on real transcriptomics data, we've shown that KOs (and in particular, the KOPI approach) do retrieve important variables with better power than classical approaches (Wilcoxon, Lasso), while controlling FDR.
However, all methods perform poorly when the relationship between gene expressions and outcome is nonlinear.
On real outcomes, the method is overly conservative (having no discoveries is a surefire way of controlling your number of false discoveries), and we had to turn the false discovery rate threshold to 50% to select any gene at all.
#machineLearning #genomics #featureSelection #biomarkerDiscovery #transcriptomics
Curious about the dark side of plant biology?
Check out our latest preprint on the secrets of black pigmentation in Rubus:
https://doi.org/10.64898/2026.05.05.723051
#Anthocyanins #Transcriptomics #PlantScience #Fruits
@PuckerLab
🧬 Could a single metric decode how genes are regulated across cells?
🔗 Regulation Ratio: A Singular Multi-Omic Measurement of Gene Regulatory Mechanisms. Computational and Structural Biotechnology Journal (CSBJ). DOI: https://doi.org/10.34133/csbj.0044
📚 CSBJ - A Science Partner Journal: https://spj.science.org/journal/csbj
#GeneRegulation #Genomics #MultiOmics #SystemsBiology #Bioinformatics #ComputationalBiology #MolecularBiology #RNA #GeneExpression #Epigenetics #Transcriptomics
🧬 What if disease isn’t written in DNA, but in how RNA is edited and spliced?
🔗 Long-Read Sequencing Reveals RNA Splicing Complexity in Human Diseases. Computational and Structural Biotechnology Journal (CSBJ). DOI: https://doi.org/10.34133/csbj.0052
📚 CSBJ - A Science Partner Journal: https://spj.science.org/journal/csbj
#RNASequencing #Genomics #Transcriptomics #PrecisionMedicine #MolecularBiology #Bioinformatics #NextGenSequencing #RNA #DNA
"Cellular morphology emerges from polygenic, distributed transcriptional variation", Paylakhi et al. 2026
https://www.biorxiv.org/content/10.64898/2026.03.12.711281v1
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
Is spatial transcriptomics data preprocessing giving you a headache? You're not alone! 🤯
Join our webinar designed for new researchers to simplify the complex world of image-based spatial omics. We'll walk through the practical workflow step-by-step, from initial cell segmentation to cleaning up your data.
Get the foundational skills you need. 📅 Sign up here: https://t1p.de/lqbg1
#Transcriptomics #SpatialTranscriptomics #Omics #DataScience #ResearchWebinar #GHGA #ELIXIR #deNBI
Research Associate
University of Ottawa
See the full job description on jobRxiv: https://jobrxiv.org/job/university-of-ottawa-27778-research-associate-2/
#cellbiology #molecularbiology #transcriptomics #ScienceJobs #hiring #research
https://jobrxiv.org/job/university-of-ottawa-27778-research-associate-2/?fsp_sid=4260
nf-core
Chloé Azencott
Boas Pucker
CSBJ
Albert Cardona
galaxyproject
de.NBI & ELIXIR Germany
GHGA
jobRxiv