Pipeline release! nf-core/rnaseq v3.19.0 - nf-core/rnaseq v3.19.0 - Tungsten Turtle!
Please see the changelog: https://github.com/nf-core/rnaseq/releases/tag/3.19.0
Pipeline release! nf-core/rnaseq v3.19.0 - nf-core/rnaseq v3.19.0 - Tungsten Turtle!
Please see the changelog: https://github.com/nf-core/rnaseq/releases/tag/3.19.0
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!
https://tinyurl.com/3aewk286
#EvoDevo #MolecularEvolution #bioinformatics #PopulationGenomics #RNAseq #PostDoc
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!
https://tinyurl.com/3aewk286
#EvoDevo #MolecularEvolution #bioinformatics #PopulationGenomics #RNAseq #PostDoc
Pipeline release! nf-core/denovotranscript v1.2.0 - nf-core/denovotranscript v1.2.0!
Please see the changelog: https://github.com/nf-core/denovotranscript/releases/tag/1.2.0
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: https://buff.ly/40CJL02
Learn more: https://training.bioconductor.org/workshops/2025-03-Nairobi/index.html
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.
Pipeline release! nf-core/scrnaseq v3.0.0 - 3.0.0!
Please see the changelog: https://github.com/nf-core/scrnaseq/releases/tag/3.0.0
Reminder for people doing single-cell sequencing work: where possible, try to validate your single-cell data before reporting on it.
"Whole-embryo Spatial Transcriptomics at Subcellular Resolution from Gastrulation to Organogenesis", by Wan et al. 2024
https://www.biorxiv.org/content/10.1101/2024.08.27.609868v1
Something that just came to mind: any reason why PCA still dominates over UMAP for dimensionality reduction visualization of Bulk #rnaseq ? Or any reason why UMAP is inadequate for this purpose?
In principle we can use the PC vectors to derive insights, but honestly I don't see it used very often. People tend to plot PC1 vs PC2 anyway.
Bgee has a new look! We have officially switched to the new Bgee website. Check us out at https://www.bgee.org/ @SIB #scRNAseq #RNAseq #singlecell #GeneExpression #bgee #biocuration #bioinformatics
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. https://www.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.
If you are annotating genes in a eukaryotic genome with #rnaseq , how do you handle antisense transcripts? It seems like they are quite common in many situations.
I'm using tophat + stringtie, and sometimes I see features being called on the wrong strand.
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: https://tomsing1.github.io/blog/posts/parquet/ #TIL #RStats #duckdb #parquet #spark #compbio #rnaseq
Significance analysis for clustering with single-cell RNA-sequencing 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