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#scrnaseq — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #scrnaseq, aggregated by home.social.

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  1. The main #ECCB2026 conference may be over, but today lots of great people are meeting to discuss single-cell standards and collaborations:
    Single-cell and spatial data FAIRification, community standards and training
    organised by @fbastian @SIB & @elixir_europe
    eccb2026.org/communities-day#single-cell-and-spatial-data-fairification
    #scRNAseq #SingleCell #FAIRdata #bioinformatics #OpenData #GenevaLovesData

  2. The main #ECCB2026 conference may be over, but today lots of great people are meeting to discuss single-cell standards and collaborations:
    Single-cell and spatial data FAIRification, community standards and training
    organised by @fbastian @SIB & @elixir_europe
    eccb2026.org/communities-day#single-cell-and-spatial-data-fairification
    #scRNAseq #SingleCell #FAIRdata #bioinformatics #OpenData #GenevaLovesData

  3. The main #ECCB2026 conference may be over, but today lots of great people are meeting to discuss single-cell standards and collaborations:
    Single-cell and spatial data FAIRification, community standards and training
    organised by @fbastian @SIB & @elixir_europe
    eccb2026.org/communities-day#single-cell-and-spatial-data-fairification
    #scRNAseq #SingleCell #FAIRdata #bioinformatics #OpenData #GenevaLovesData

  4. The main #ECCB2026 conference may be over, but today lots of great people are meeting to discuss single-cell standards and collaborations:
    Single-cell and spatial data FAIRification, community standards and training
    organised by @fbastian @SIB & @elixir_europe
    eccb2026.org/communities-day#single-cell-and-spatial-data-fairification
    #scRNAseq #SingleCell #FAIRdata #bioinformatics #OpenData #GenevaLovesData

  5. The main #ECCB2026 conference may be over, but today lots of great people are meeting to discuss single-cell standards and collaborations:
    Single-cell and spatial data FAIRification, community standards and training
    organised by @fbastian @SIB & @elixir_europe
    eccb2026.org/communities-day#single-cell-and-spatial-data-fairification
    #scRNAseq #SingleCell #FAIRdata #bioinformatics #OpenData #GenevaLovesData

  6. Pipeline release! nf-core/scnanoseq v1.3.0 - nf-core/scnanoseq v1.3.0 - Steel Elephant!
    Single-cell/nuclei pipeline for data derived from Oxford Nanopore and 10X Genomics
    Please see the changelog: github.com/nf-core/scnanoseq/r

    #10xgenomics #longreadsequencing #nanopore #rnaseq #rnaseq #scrnaseq #singlecell #nfcore #openscience #nextflow #bioinformatics

  7. Pipeline release! nf-core/scnanoseq v1.3.0 - nf-core/scnanoseq v1.3.0 - Steel Elephant!
    Single-cell/nuclei pipeline for data derived from Oxford Nanopore and 10X Genomics
    Please see the changelog: github.com/nf-core/scnanoseq/r

    #10xgenomics #longreadsequencing #nanopore #rnaseq #rnaseq #scrnaseq #singlecell #nfcore #openscience #nextflow #bioinformatics

  8. Pipeline release! nf-core/scnanoseq v1.3.0 - nf-core/scnanoseq v1.3.0 - Steel Elephant!
    Single-cell/nuclei pipeline for data derived from Oxford Nanopore and 10X Genomics
    Please see the changelog: github.com/nf-core/scnanoseq/r

    #10xgenomics #longreadsequencing #nanopore #rnaseq #rnaseq #scrnaseq #singlecell #nfcore #openscience #nextflow #bioinformatics

  9. Pipeline release! nf-core/scnanoseq v1.3.0 - nf-core/scnanoseq v1.3.0 - Steel Elephant!
    Single-cell/nuclei pipeline for data derived from Oxford Nanopore and 10X Genomics
    Please see the changelog: github.com/nf-core/scnanoseq/r

    #10xgenomics #longreadsequencing #nanopore #rnaseq #rnaseq #scrnaseq #singlecell #nfcore #openscience #nextflow #bioinformatics

  10. Pipeline release! nf-core/scnanoseq v1.3.0 - nf-core/scnanoseq v1.3.0 - Steel Elephant!
    Single-cell/nuclei pipeline for data derived from Oxford Nanopore and 10X Genomics
    Please see the changelog: github.com/nf-core/scnanoseq/r

    #10xgenomics #longreadsequencing #nanopore #rnaseq #rnaseq #scrnaseq #singlecell #nfcore #openscience #nextflow #bioinformatics

  11. Join us for a 2-part workshop on Mastering Reproducible Enrichment Analysis! 📊

    Presented by Anusuiya Bora and myself, with a focus on reproducibility and best practices.

    📅 When: 12 and 13 May 2026
    🕑 Time: 2:00 PM – 4:00 PM (AEST)
    📍 Where: Online
    💰 Cost: FREE for academic sector (places are limited!)

    🔗Registration form link: lnkd.in/gQcHggGF

    #Bioinformatics #RNAseq #scRNAseq #Genomics #ReproducibleResearch #OpenScience #RStats

  12. Join us for a 2-part workshop on Mastering Reproducible Enrichment Analysis! 📊

    Presented by Anusuiya Bora and myself, with a focus on reproducibility and best practices.

    📅 When: 12 and 13 May 2026
    🕑 Time: 2:00 PM – 4:00 PM (AEST)
    📍 Where: Online
    💰 Cost: FREE for academic sector (places are limited!)

    🔗Registration form link: lnkd.in/gQcHggGF

    #Bioinformatics #RNAseq #scRNAseq #Genomics #ReproducibleResearch #OpenScience #RStats

  13. Join us for a 2-part workshop on Mastering Reproducible Enrichment Analysis! 📊

    Presented by Anusuiya Bora and myself, with a focus on reproducibility and best practices.

    📅 When: 12 and 13 May 2026
    🕑 Time: 2:00 PM – 4:00 PM (AEST)
    📍 Where: Online
    💰 Cost: FREE for academic sector (places are limited!)

    🔗Registration form link: lnkd.in/gQcHggGF

    #Bioinformatics #RNAseq #scRNAseq #Genomics #ReproducibleResearch #OpenScience #RStats

  14. Join us for a 2-part workshop on Mastering Reproducible Enrichment Analysis! 📊

    Presented by Anusuiya Bora and myself, with a focus on reproducibility and best practices.

    📅 When: 12 and 13 May 2026
    🕑 Time: 2:00 PM – 4:00 PM (AEST)
    📍 Where: Online
    💰 Cost: FREE for academic sector (places are limited!)

    🔗Registration form link: lnkd.in/gQcHggGF

    #Bioinformatics #RNAseq #scRNAseq #Genomics #ReproducibleResearch #OpenScience #RStats

  15. Join us for a 2-part workshop on Mastering Reproducible Enrichment Analysis! 📊

    Presented by Anusuiya Bora and myself, with a focus on reproducibility and best practices.

    📅 When: 12 and 13 May 2026
    🕑 Time: 2:00 PM – 4:00 PM (AEST)
    📍 Where: Online
    💰 Cost: FREE for academic sector (places are limited!)

    🔗Registration form link: lnkd.in/gQcHggGF

    #Bioinformatics #RNAseq #scRNAseq #Genomics #ReproducibleResearch #OpenScience #RStats

  16. Aligning #scRNAseq datasets along a shared temporal axis across studies, species & systems is hard. This study uses meta-analytic models to develop a #transcriptomic measure of #neurodevelopmental timing that is applicable to different organisms & tissue types @PLOSBiology plos.io/4ch0XiX

  17. Aligning #scRNAseq datasets along a shared temporal axis across studies, species & systems is hard. This study uses meta-analytic models to develop a #transcriptomic measure of #neurodevelopmental timing that is applicable to different organisms & tissue types @PLOSBiology plos.io/4ch0XiX

  18. Aligning #scRNAseq datasets along a shared temporal axis across studies, species & systems is hard. This study uses meta-analytic models to develop a #transcriptomic measure of #neurodevelopmental timing that is applicable to different organisms & tissue types @PLOSBiology plos.io/4ch0XiX

  19. Aligning #scRNAseq datasets along a shared temporal axis across studies, species & systems is hard. This study uses meta-analytic models to develop a #transcriptomic measure of #neurodevelopmental timing that is applicable to different organisms & tissue types @PLOSBiology plos.io/4ch0XiX

  20. Aligning #scRNAseq datasets along a shared temporal axis across studies, species & systems is hard. This study uses meta-analytic models to develop a #transcriptomic measure of #neurodevelopmental timing that is applicable to different organisms & tissue types @PLOSBiology plos.io/4ch0XiX

  21. Our new pre‑print is out!

    scReady – an automated and accessible pipeline for single‑cell RNA‑Seq preprocessing: Empowering novice bioinformaticians

    wellcomeopenresearch.org/artic

    @haessar.bsky.social @fionan-a.bsky.social @yiyicheng

    #scRNAseq #bioinformatics

  22. Our new pre‑print is out!

    scReady – an automated and accessible pipeline for single‑cell RNA‑Seq preprocessing: Empowering novice bioinformaticians

    wellcomeopenresearch.org/artic

    @haessar.bsky.social @fionan-a.bsky.social @yiyicheng

    #scRNAseq #bioinformatics

  23. Our new pre‑print is out!

    scReady – an automated and accessible pipeline for single‑cell RNA‑Seq preprocessing: Empowering novice bioinformaticians

    wellcomeopenresearch.org/artic

    @haessar.bsky.social @fionan-a.bsky.social @yiyicheng

    #scRNAseq #bioinformatics

  24. Our new pre‑print is out!

    scReady – an automated and accessible pipeline for single‑cell RNA‑Seq preprocessing: Empowering novice bioinformaticians

    wellcomeopenresearch.org/artic

    @haessar.bsky.social @fionan-a.bsky.social @yiyicheng

    #scRNAseq #bioinformatics

  25. Our new pre‑print is out!

    scReady – an automated and accessible pipeline for single‑cell RNA‑Seq preprocessing: Empowering novice bioinformaticians

    wellcomeopenresearch.org/artic

    @haessar.bsky.social @fionan-a.bsky.social @yiyicheng

    #scRNAseq #bioinformatics

  26. #NeuralStemCells (NSCs) & ependymal cells (ECs) are derived from #RadialGlialCells. This study uses #scRNAseq to characterize cell fate trajectories in the developing #VentricularZone, identifying TFEB as a regulator of the NSC/EPC balance @PLOSBiology plos.io/3HbrsJG

  27. #NeuralStemCells (NSCs) & ependymal cells (ECs) are derived from #RadialGlialCells. This study uses #scRNAseq to characterize cell fate trajectories in the developing #VentricularZone, identifying TFEB as a regulator of the NSC/EPC balance @PLOSBiology plos.io/3HbrsJG

  28. #NeuralStemCells (NSCs) & ependymal cells (ECs) are derived from #RadialGlialCells. This study uses #scRNAseq to characterize cell fate trajectories in the developing #VentricularZone, identifying TFEB as a regulator of the NSC/EPC balance @PLOSBiology plos.io/3HbrsJG

  29. #NeuralStemCells (NSCs) & ependymal cells (ECs) are derived from #RadialGlialCells. This study uses #scRNAseq to characterize cell fate trajectories in the developing #VentricularZone, identifying TFEB as a regulator of the NSC/EPC balance @PLOSBiology plos.io/3HbrsJG

  30. #NeuralStemCells (NSCs) & ependymal cells (ECs) are derived from #RadialGlialCells. This study uses #scRNAseq to characterize cell fate trajectories in the developing #VentricularZone, identifying TFEB as a regulator of the NSC/EPC balance @PLOSBiology plos.io/3HbrsJG

  31. Yuyao Song presents ScGOclust to compare #singlecell #scRNAseq between distant species, such as fly and mammal: gene level comparisons don’t work because there has been too much divergence. 💡 Instead of genes, use GO terms has features to compare cells. #ismbeccb2025
    doi.org/10.1093/bioinformatics

  32. Yuyao Song presents ScGOclust to compare #singlecell #scRNAseq between distant species, such as fly and mammal: gene level comparisons don’t work because there has been too much divergence. 💡 Instead of genes, use GO terms has features to compare cells. #ismbeccb2025
    doi.org/10.1093/bioinformatics

  33. Yuyao Song presents ScGOclust to compare #singlecell #scRNAseq between distant species, such as fly and mammal: gene level comparisons don’t work because there has been too much divergence. 💡 Instead of genes, use GO terms has features to compare cells. #ismbeccb2025
    doi.org/10.1093/bioinformatics

  34. Yuyao Song presents ScGOclust to compare #singlecell #scRNAseq between distant species, such as fly and mammal: gene level comparisons don’t work because there has been too much divergence. 💡 Instead of genes, use GO terms has features to compare cells. #ismbeccb2025
    doi.org/10.1093/bioinformatics

  35. Yuyao Song presents ScGOclust to compare #singlecell #scRNAseq between distant species, such as fly and mammal: gene level comparisons don’t work because there has been too much divergence. 💡 Instead of genes, use GO terms has features to compare cells. #ismbeccb2025
    doi.org/10.1093/bioinformatics

  36. Single cell RNA-sequencing (#scRNAseq) is an essential method to learn about cells in health and disease. Here we have studied "multiplets", an important source of error of scRNAseq. We find that multiplets are astonishingly frequent and hard to eliminate.
    doi.org/10.1101/2025.06.09.658