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You can still apply: Postdoc job opportunity! We're looking for early career researcher in evolutionary genomics to study the relation between intra-specific gene expression variability, polymorphism, and macro-evolutionary rates. We have all of the data in 3 fishes and amphioxus, just waiting for your expertise and enthusiasm!
tinyurl.com/3aewk286
#EvoDevo #MolecularEvolution #bioinformatics #PopulationGenomics #RNAseq #PostDoc

tinyurl.comOpportunités de carrière : Postdoctoral position in evolutionary genomics (22280)

Postdoc job opportunity! We're looking for early career researcher in evolutionary genomics to study the relation between intra-specific gene expression variability, polymorphism, and macro-evolutionary rates. We have all of the data in 3 fishes and amphioxus, just waiting for your expertise and enthusiasm!
tinyurl.com/3aewk286
#EvoDevo #MolecularEvolution #bioinformatics #PopulationGenomics #RNAseq #PostDoc

tinyurl.comOpportunités de carrière : Postdoctoral position in evolutionary genomics (22280)

🌍 Bioconductor Course, Kenya 2025 🎓

We’re thrilled to announce our first in-person Bioconductor course in Kenya, happening March 24–28, 2025, at the ILRI Campus in Nairobi!

This free, week-long course is for bioinformatics beginners, covering R, Bioconductor, and RNA-seq workflows, and aims to grow the Bioconductor community in Africa.

🔗 Apply now: buff.ly/40CJL02
🔗 Learn more: training.bioconductor.org/work

Continued thread

We obtained #RNAseq from tooth germs over the embryonic and postnatal period where the major events of morphogenesis occur, from bud, to cap, to bell stage until differentiation and enamel/dentin secretion, and obtained clusters of coexpressed genes per stage.

"Whole-embryo Spatial Transcriptomics at Subcellular Resolution from Gastrulation to Organogenesis", by Wan et al. 2024
biorxiv.org/content/10.1101/20

bioRxiv · Whole-embryo Spatial Transcriptomics at Subcellular Resolution from Gastrulation to OrganogenesisSpatiotemporal patterns of gene expression underlie embryogenesis. Despite progress in single-cell genomics, mapping these patterns across whole embryos with comprehensive gene coverage and at high resolution has remained elusive. Here, we introduce a whole-embryo imaging platform using multiplexed error-robust fluorescent in-situ hybridization (weMERFISH). We quantified the expression of 495 genes in whole-mount zebrafish embryos at subcellular resolution. Integration with single-cell multiomics data generated an atlas detailing the expression of 25,872 genes and the accessibility of 294,954 chromatin regions, explorable with an online interface MERFISHEYES (beta version). We found that temporal gene expression aligns with cellular maturation and morphogenetic movements, diverse expression patterns correspond to composites of tissue-specific accessible elements, and changes in gene expression generate sharp boundaries during gastrulation. These results establish a novel approach for whole-organism spatial transcriptomics, provide a comprehensive spatially resolved atlas of gene expression and chromatin accessibility, and reveal the diversity, precision and emergence of embryonic patterns. ### Competing Interest Statement The authors have declared no competing interest.

We are proud to announce the release of Bgee 15.2 with the addition of thousands of single-cell 10X Genomics and bulk RNA-Seq libraries to allow for more meaningful gene expression comparisons across species. We also added the free text anatomical, developmental and cell type information provided by authors in addition to the standardized ontology terms. bgee.org/ #scRNAseq #RNAseq @SIB

STATGEN 2024 talk
Statistical Methods for Single-Cell RNA-Seq Analysis and Spatial Transcriptomics
Rafael Irizarry

tSNE and UMAP plots:
"They really aren't informative, but they are really pretty."

Negative control scRNAseq data set: the percent of zeros is very high, and contributes strongly to the first PCA. tSNE plot 'discovers' new cells.

Transformed to log2(1 + CPM): looks zero-inflated.

Raw counts: Poisson

1/

What RNA-Seq datasets are everyone using for RNA-Seq differential analysis workshops, using {DESeq2} or {limma} #RStats?

Two or three categories, and ideally a good (4 or 5+) number of replicates, human or mouse?

We have some new methods in ontology enrichment, and we really could use some more datasets to try them on.

I've found the {airways} dataset, and lung adenocarcinoma from {recount3} (as an easy pull). Looking for others. TIA.

Hello !

We are a Genomics core facility located in the centre of Paris, France, within the Ecole normale supérieure.

✅ We support our collaborators by providing access to high-throughput sequencing, with particular expertise in functional genomics applications in eukaryotes. We propose transcriptomics: #RNAseq (low input, short-read and long-read, single-cell #scRNA-seq).

Today I learned how to store gene expression data in (multiple) parquet files, and query them as a single dataset from R with the {arrow}, {duckdb} or {sparklyr} packages. I am amazed by {duckdb}'s speed 🚀 - even on my laptop! Here's a blog post with what I learned: tomsing1.github.io/blog/posts/ #TIL #RStats #duckdb #parquet #spark #compbio #rnaseq

tomsing1.github.ioThomas Sandmann’s blog - Adventures with parquet: Storing & querying gene expression data

A substantial base calling quality improvement observed beta testing the #nanopore RNA004 direct #rnaseq protocol.
This is empirical data from commercial, multi-tissue RNA after poly(A) selection, aligned to hg38 with miminap2 -a -x splice --secondary=no.
Alignments (especially at splice junctions) look exceptional; I could see potential RNA modifications directly from basecalling output as mutations in IGV