#Tag · bonfire.cafe

🧬 #Bioinformatics: In RNA sequencing of tissue samples, researchers measure how much #RNA from a gene is present in the sample as a whole. So-called #deconvolution methods then try to infer the cellular composition from these measurements. A team from Innsbruck and Munich has now developed “omnideconv” — an openly accessible framework that makes different methods comparable through systematic benchmarking.

👉 https://omnideconv.org/

📖 https://link.springer.com/article/10.1186/s13059-026-03955-w

#Genomics #openScience

SpringerLink

omnideconv: a unifying framework for using and benchmarking single-cell-informed deconvolution of bulk RNA-seq data - Genome Biology

Background In silico cell-type deconvolution from bulk transcriptomics data is a powerful technique to gain insights into the cellular composition of complex tissues. While first-generation methods used precomputed expression signatures covering limited cell types and tissues, second-generation tools use single-cell RNA sequencing data to build custom signatures for deconvoluting arbitrary cell types, tissues, and organisms. This flexibility poses significant challenges in assessing their deconvolution performance. Results Here, we comprehensively benchmark second-generation tools, disentangling different sources of variation and bias using a diverse panel of real and simulated data. Our results reveal substantial differences in accuracy, scalability, and robustness across methods, depending on factors such as cell-type similarity, reference composition, and dataset origin. Conclusions Our study highlights the strengths, limitations, and complementarity of state-of-the-art tools, shedding light on how different data characteristics and confounders impact deconvolution performance. We provide the scientific community with an ecosystem of tools and resources, omnideconv, simplifying the application, benchmarking, and optimization of deconvolution methods.

Egon Willigh☮gen 🟥 boosted

Ecology & Evolution of Health

@ecoevohealth@mstdn.science · 3 weeks ago

Mathematical modelling led by @tsukushi_kamiya reveals the role of #nutrients in the stability of the vaginal #microbiota in a way that is consistent with #genomics and #metabolomics data shared by Jacques Ravel. This is one of the rare #models on this understudied #microbiome.

https://doi.org/10.1371/journal.pbio.3003575

🙏 Thanks to the #FRM for funding Tsukushi's post-doctoral fellowship and to @PeerCommunityIn for the #open peer-review process ( @PLOSBiology editors did not require any additional review!).

Resource landscape shapes the composition and stability of the human vaginal microbiota

Ecology & Evolution of Health

@ecoevohealth@mstdn.science · 3 weeks ago

https://doi.org/10.1371/journal.pbio.3003575

🙏 Thanks to the #FRM for funding Tsukushi's post-doctoral fellowship and to @PeerCommunityIn for the #open peer-review process ( @PLOSBiology editors did not require any additional review!).