Alexandre Segers

PhD student @lievenClement and @elfridedebaere lab, Developing statistical software for transcriptomics analysis, Ghent University

[Alexandre Segers @Alex_Segers_]

Fast differential usage, aberrant splicing and expression analysis that scales to large RNA-seq data compendia? Check out our preprint to see how juggling offsets unlocks bulk RNA-seq tools for these applications. Great collaboration with @lievenclement and @elfridedebaere lab!

[bioRxiv @biorxivpreprint]

Juggling offsets unlocks RNA-seq tools for fast scalable differential usage, aberrant splicing and expression analyses. https://t.co/5pzlmgyRIl #bioRxiv

[Alexandre Segers @Alex_Segers_]

@KVittingSeerup @lievenClement @elfridedebaere saseR for aberrant or outlier expression/splicing analysis will hopefully be on GitHub in August. For the differential usage/splicing analysis, I currently advice following the proposed workflow at https://t.co/6GTdjCd5gJ. We will provide a clarified vignette in the near future.

[Alexandre Segers @Alex_Segers_]

Fast differential usage, aberrant splicing and expression analysis that scales to large RNA-seq data compendia? Check out our preprint to see how juggling offsets unlocks bulk RNA-seq tools for these applications. Great collaboration with @lievenclement and @elfridedebaere lab!

[bioRxiv @biorxivpreprint]

Juggling offsets unlocks RNA-seq tools for fast scalable differential usage, aberrant splicing and expression analyses. https://t.co/5pzlmgyRIl #bioRxiv

[Alexandre Segers @Alex_Segers_]

@KVittingSeerup @lievenClement @elfridedebaere The OUTRIDER paper (10.1016/j.ajhg.2018.10.025 - Figure 1b) has a very nice illustration upon the difference between differential and aberrant expression analyses. We do not search for outlier samples, but rather which gene is an expression/splicing outlier within a sample.

[Alexandre Segers @Alex_Segers_]

@KVittingSeerup @lievenClement @elfridedebaere Thanks Kristoffer! We refer to aberrant splicing in the context of splicing outlier detection. This design is required in the context of rare diseases, where often no replicates are available, and therefore the splicing of one sample is aberrant versus many ‘healthy’ controls.

[Michael Love @mikelove]

Also, regarding this new preprint on differential splicing / transcript usage, see @Alex_Segers_'s helpful answer on @jeremy_m_simon's Bioconductor support site post:

https://t.co/R7nZpSnb6b

https://t.co/CIRTqH2lGs

[Elfride De Baere @elfridedebaere]

saseR dramatically 💫 boosts the performance for aberrant splicing. Work from @Alex_Segers_ & @lievenClement, great collab with @GilisJeroen @mvheetve @elfridedebaere @RAREMED1 @ResearchUGent @FWOvlaanderen
#RNAseq #diagnosis #raredisease #splicing #expression #differentialusage

[Elfride De Baere @elfridedebaere]

📢 Important & cool paper alert! 📢 saseR is a Scalable Aberrant Splicing and Expression Retrieval framework that outperforms existing tools (e.g. DEXSeq, OUTRIDER, OutSingle, FRASER) in terms of computational speed and scalability 👇

[Alexandre Segers @Alex_Segers_]

saseR can further be used for many different applications that model proportions, and allows both short- and long-read sequencing data. It will soon be submitted to Bioconductor.

[Alexandre Segers @Alex_Segers_]

And last but not least, we also show that using saseR improves power significantly to detect aberrant splicing events.

[lieven clement @lievenClement]

Fast differential usage, aberrant splicing and expression analysis that scales to large RNA-seq data compendia? Check @Alex_Segers_ preprint to see how juggling offsets unlocks bulk RNA-seq tools for these applications. Great collaboration with @elfridedebaere lab and @RAREMED1