π¨ Deadline: Jan 11, Sunday
π’ Donβt miss your chance to join the PhD course β #Bioinformatics Analysis of #Gene #Expression Data from Bulk #RNAseqβ at University of Copenhagen!
π
26 Jan β 12 Feb 2026
π Apply here: phdcourses.ku.dk/DetailKursus...
You will learn:
β QC & alignment
β Differential expression
β Pathway enrichment
β Hands-on with Galaxy, UCloud & R
β
Free for Danish PhD students
π§βπ« Together with Stefan E. Seemann, Nadezhda T. Doncheva & Jan Gorodkin.
#Bioinformatics #RNAseq #PhDCourses #DataAnalysis
π Excited to teach the PhD course βBioinformatics Analysis of Gene Expression Data from Bulk RNA-seqβ at University of Copenhagen!
π
26 Jan β 12 Feb 2026
π Bring your own data (or use a public one) and learn the full RNA-seq pipeline:
And finally, huge congrats, Bahtiyar YΔ±lmaz! π Thanks for the opportunity and for leading such an impressive piece of work. Proud to be part of this global effort with you! ππ§¬
π Want to explore the data yourself? Check out the interactive resources:
π yilmazlab.shinyapps.io/vivaria_app/
π yilmazlab.shinyapps.io/vivaria_app2/
π Excited to be part of this global effort! π¦
Microbial and metabolic diversity was mapped across 51 vivaria & 12 wild colonies, revealing striking functional convergence despite taxonomic divergence.
π www.cell.com/cell-host-mi...
π¨ Dealing with sparse, noisy single-cell DNA methylation data (scBS-seq)? Check out #scDMV.
It uses a zero-one inflated beta mixture model to tackle the excess of 0s/1s and low coverage, boosting accuracy in identifying differentially methylated regions (DMRs).
On the art of great article titles: this oneβs dedicated to all the Reviewer 2s out there.
π onlinelibrary.wiley.com/doi/10.1111/...
All you need is:
1) Transcriptome (equivalent of transcriptome #FASTA file)
2) Gene annotations (equivalent of #GTF/#GFF file)
3) RNAseq reads (your #FASTQ file)
#BgeeCall will handle the rest and will let you know if a gene is present or absent!
β And what if your species is not present in this database? What do you do then?
β Isn't it enough to decide on an arbitrary cutoff (e.g. TPM < 1) for expression?
π Actually, you can do better than that. How? #BgeeCall #R package:
www.bioconductor.org/packages/rel...
β How do you know if a gene is expressed or not in a certain tissue/organ?
π If you are interested in animals, #Bgee database is the first place to go. 52 species, several tissues/organs...
www.bgee.org
Before running your statistical test, double-check that its assumptions hold. Violations can lead to misleading results. Here's a handy tool to help:
π ahmed-bargheet.shinyapps.io/AssumpSure
βΉοΈ github.com/Ahmedbarghee...
#rstats #shiny #r
π medium.com/the-quantast...
"A drunk man will find his way home, but a drunk bird may get lost forever." -Shizuo Kakutani
π¨Our latest: HIF1Ξ± mediates circadian regulation of skeletal muscle metabolism and substrate preference in response to time-of-day exercise β°πͺ
π www.pnas.org/doi/10.1073/...
"(...) This is present for the pairs (body_mass_mm, bill_depth_mm) and (bill_depth_mm, bill_length_mm)."
From the vignette after I fixed the typos: "Pairs of numeric variables exhibit Simpson's paradox if the ungrouped correlation is negative and the grouped correlations are positive (or vice-versa). (...)"
A nice #R package to visualize #bullseye plots:
π cran.r-project.org/web/packages...
β Did you notice the Simpson's paradox?
Are you developing an R package? Great tips ahead β¬οΈ
journals.plos.org/ploscompbiol...
π Key takeaway: Check total RNA & RNA composition before comparing RPKM/TPM across samples/protocols to avoid misleading results!
π For DEGs, use count-based methods like DESeq2/edgeR, not direct RPKM/TPM.
π NEVER use TPM for quantitative comparisons across samples with very different total RNA or distributions!
π Abundant RNAs (rRNA, globin, etc.) can inflate their TPM and deflate others.
β οΈ Only compare RPKM/TPM if total RNA & RNA populations are similar. This is often not checked!
𧬠Stranded vs. non-stranded RNA-seq impacts results. Comparing TPM/RPKM across these can be wrong.
β οΈ Tissue types express diverse RNA repertoires. Direct TPM comparisons across such different tissues are problematic
π’ RNA-seq users! RPKM/TPM are NOT always comparable across samples/projects. They're relative within a sample. rnajournal.cshlp.org/content/26/8...
β TPM can be okay for qualitative comparisons (PCA, clustering) under the right conditions.
π¬ RNA location (nucleus vs. cytoplasm) matters! Don't directly compare TPMs from different compartments.
