Discover and read the best of Twitter Threads about #RNASEQ
Most recents (16)
Tuxedo Protocols: github.com/broadinstitute…
This repository contains a collection of protocols and tools for analyzing RNAseq data, including alignment, quantification, and differential expression analysis. (1/10)
This repository contains a collection of protocols and tools for analyzing RNAseq data, including alignment, quantification, and differential expression analysis. (1/10)
RNASeq Workflow: github.com/gringer/RNASeq…
This repository contains a workflow for analyzing RNAseq data using the R programming language, including quality control, alignment, and differential expression analysis. (2/10)
This repository contains a workflow for analyzing RNAseq data using the R programming language, including quality control, alignment, and differential expression analysis. (2/10)
Doing #SingleCell #RNAseq? Ever wonder if all those clusters are real? Turns out most feature selection & clustering pipelines can't tell when there's only 1 cluster! But I found a solution! 🧵👇 
Happy to release (and welcome feedback!) on my new feature selection algorithm that can help prevent false discoveries in scRNAseq datasets! bitbucket.org/scottyler892/a… (pip installable & works easily with scanpy :-) @fabian_theis
@fabian_theis By starting from first principles I asked: what is a cell type? We've long identified them as subsets of cells with different functions that have corresponding marker gene expression
Analysis of #scRNAseq requires constant, tedious, interaction with genomics databases. To facilitate querying from @ensembl et al., @NeuroLuebbert developed gget:
biorxiv.org/content/10.110… (code @ github.com/pachterlab/gget).
gget has many uses; a 🧵on the its amazing versatility: 1/
biorxiv.org/content/10.110… (code @ github.com/pachterlab/gget).
gget has many uses; a 🧵on the its amazing versatility: 1/
gget works from the command line or python. Just `pip install gget`.
Need reference files for your analysis? 2/
Need reference files for your analysis? 2/
Simple with `gget ref`...3/
Very pleased to share our ✨New Paper✨ about an epigenetic conflict between a restriction–modification system and a methylation-dependent nuclease.
Want to know how that pans out? Continue reading here 🧵👇🏻 or head over to @NAR_Open for the full story: academic.oup.com/nar/advance-ar…
Want to know how that pans out? Continue reading here 🧵👇🏻 or head over to @NAR_Open for the full story: academic.oup.com/nar/advance-ar…
It all began when we started investigating the anti-#CRISPR repressor Aca2 from Pectobacterium #phage ZF40. Back then, we stumbled upon an RM system in Pecto that inhibited transformation and conjugation and completely blocked phage infection. 🧬🚫 [2/n]
Because this strain could only take up DNA with RM-compatible methylation, we knocked out the restriction endonuclease of the RM system, enabling us to pursue our anti-CRISPR regulation project:
academic.oup.com/nar/article/47…
And that could have been the end of it... [3/n]
academic.oup.com/nar/article/47…
And that could have been the end of it... [3/n]
Does #gut #microbiota interact with #microglia in the #aging #brain? Are microglia metabolically affected (#mitochondria)? Does #diet take part? any #translational data?
Our paper made it last week in @NatureNeuro
so it's about time for a 🧵!
nature.com/articles/s4159…
Our paper made it last week in @NatureNeuro
so it's about time for a 🧵!
nature.com/articles/s4159…
1. Microglia from aged and young-adult #male & #female mice, under SPF or #germ_free conditions were profiled by bulk #RNAseq. We identified a gene-set, which depended on the housing condition regardless of #age & regulate central processes in microglia. "Microglial GF signature"
2. Utilizing #WGCNA, we found major differences between microglia of SPF and GF mice in the aged groups. But do we see any functional difference on a cellular level?
Absolutely thrilled to share our VASA-seq pre-print on #biorxiv today: Droplet-based single-cell total RNA-seq reveals differential non-coding expression and splicing patterns during mouse development 🧵
biorxiv.org/content/10.110…
biorxiv.org/content/10.110…
VASA-seq captures most parts of the transcriptome (total RNA) of a #singlecell rather than just a portion. It was expertly developed by Fredrik Salmen, a postdoc in @AlexandervanOu1's lab at the @_Hubrecht and partner in crime for this project 🕵️ (1/n).
The method fragments the entire RNA payload of a cell and polyadenylates each fragment which makes them compatible with all poly(T) based barcoding methods, enabling UMI-tagging and retaining strand-specificity (2/n) 🧑🔬👨🔬
Our latest study to understand the dynamic regulation of #interferon (#IFN) and #SARSCoV2 infection.
🧵summarizing what we found and what needs to be done to understand the dynamic interplay of infection and #IFN regulation. @iScience_CP #COVID19
cell.com/iscience/fullt…
🧵summarizing what we found and what needs to be done to understand the dynamic interplay of infection and #IFN regulation. @iScience_CP #COVID19
cell.com/iscience/fullt…
Congrats Nick github.com/Nick-Eagles for your @biorxivpreprint first pre-print! 🙌🏽
SPEAQeasy is our @nextflowio implementation of the #RNAseq processing pipeline that produces @Bioconductor-friendly #rstats objects that we use at @LieberInstitute
📜 biorxiv.org/content/10.110…
SPEAQeasy is our @nextflowio implementation of the #RNAseq processing pipeline that produces @Bioconductor-friendly #rstats objects that we use at @LieberInstitute
📜 biorxiv.org/content/10.110…
This project started a few years ago with Emily Burke linkedin.com/in/emilyeburke/ @BadoiPhan @andrewejaffe with whom we wrote a version specific to our HPC 🖥️ (JHPCE)
github.com/LieberInstitut…
Then @Jake_Aaron96 Israel Aguilar et al from @wintergenomics rewrote using it @nextflowio
github.com/LieberInstitut…
Then @Jake_Aaron96 Israel Aguilar et al from @wintergenomics rewrote using it @nextflowio
Nick continued developing it with input from @sudo_BreeB and tests from colleagues
Nick also wrote the documentation website research.libd.org/SPEAQeasy/
Then @JoshStolz2 @lahuuki helped show the outputs can be analyzed with @Bioconductor
research.libd.org/SPEAQeasy-exam…
Nick also wrote the documentation website research.libd.org/SPEAQeasy/
Then @JoshStolz2 @lahuuki helped show the outputs can be analyzed with @Bioconductor
research.libd.org/SPEAQeasy-exam…
Finally the second chapter of my #PhD #thesis is out 💪🥳
> 1 year after thesis submission!
Thanks so much 🙏 to my co-authors @CFlensburg @ianjmajewski and Terry Speed for their persistence and support!
(thread 👇) #RNAseq #CancerGenomics #mutations
bmcbioinformatics.biomedcentral.com/articles/10.11…
> 1 year after thesis submission!
Thanks so much 🙏 to my co-authors @CFlensburg @ianjmajewski and Terry Speed for their persistence and support!
(thread 👇) #RNAseq #CancerGenomics #mutations
bmcbioinformatics.biomedcentral.com/articles/10.11…
Our goal was to decide on the library size needed to sequence a cohort of ~90 in-house Leukaemia RNA-Seq samples to allow good sensitivity for both DE analyses and mutation calling.
We used publicly available deeply sequenced RNA-Seq samples (from Leucegene) from the same cancer type and called mutations using 6 methods (combinations of 3 popular callers and filtering methods).
We called mutations on the initial and randomly downsampled data.
We called mutations on the initial and randomly downsampled data.
I'm very pleased to announce our publication "Whole genome, transcriptome and methylome profiling enhances actionable target discovery in high-risk paediatric cancer" online today in @NatureMedicine rdcu.be/b76h9 Here's a tweetorial of the main findings 1/22
This research was 7 years in the making & is the first major publication from #ZeroChildhoodCancer, a joint initiative of @KidsCancerInst and Kids Cancer Centre @Sydney_Kids. #Zero is now a national precision medicine program, including all 8 hospitals and 23 research partners /2
What value does deciphering the whole genome hold for guiding clinical care of children with high-risk cancer? LOTS. Does it provide more accurate diagnoses, more treatment options, better risk stratification? YES. Can it be done fast enough to have a clinical impact? YES. /3
We developed Visualization in Plugin on top of #cellxgene to generate violin, stacked violin, stacked bar, heatmap, volcano, embedding, dot, track, density, 2D density, sankey and dual-gene plot in high-resolution SVG/PNG format for #singlecell #rnaseq dataset #scRNAseq #scanpy
The preprint is available here
@cellxgene VIP unleashes full power of interactive visualization, plotting and analysis of #scRNA-seq data in the scale of millions of cells biorxiv.org/content/10.110…
@cellxgene VIP unleashes full power of interactive visualization, plotting and analysis of #scRNA-seq data in the scale of millions of cells biorxiv.org/content/10.110…
Want to play with cellxgene VIP live? Demo is here
cellxgenevip-ms.bxgenomics.com
Dataset: MS brain #snRNAseq dataset by @schirmerlab and #RowitchLab
cellxgenevip-ms.bxgenomics.com
Dataset: MS brain #snRNAseq dataset by @schirmerlab and #RowitchLab
There's been talk about ACE2 and its association with age, sex, smoking etc. as a way to explain #covid19 disease severity. Driving this is intuition that since ACE2 is a receptor for SARS-CoV-2, ⬆️ACE2 => worse disease. My take on this w/@sinabooeshaghi biorxiv.org/content/10.110…
First, if you're going to examine previously published expression data you have to be very careful with ACE2. Expression levels are very low in some organs, e.g. in the lung. This means you have to really worry about how data was processed, normalized, scaled etc.
We looked at mouse single-cell RNA-seq data published by @IliasAngelidis, @lkmklsmn et al. (@fabian_theis and @SchillerLab). Using #scRNAseq from eight 3-month old and seven 24-month old mice we found significantly *less* expression in aged mice.
I read the @biorxivpreprint every day. I've recently been asked by various people what/why/how I read so here is a short thread that may be helpful to others: 1/
@biorxivpreprint I read abstracts of articles on the @biorxivpreprint every morning and every evening, and typically a few times during the day as well. I am sometimes delving into the main paper, usually starting with the supplement. 2/
@biorxivpreprint I search for articles on @Google using keywords (and filtering for "past 24 hours"), my lab has @SlackHQ channels where people post articles, I check @rxivist, and I read and search twitter by keywords for topics I'm interested in every day. I follow @biorxivpreprint. 3/
In our #RNAseq & #scRNAseq work we use @ensembl's databases. Two months ago @sinabooeshaghi noticed some inconsistencies between @ensembl's cDNA fasta and GTF files and investigated with @lioscro. Now @LambdaMoses has written up an in-depth analysis: fromsystosys.netlify.com/2020/01/31/com…
@ensembl @sinabooeshaghi @lioscro @LambdaMoses To work around the inconsistencies, the `kb` wrapper for kallisto and bustools can easily generate genome consistent transcriptomes. pypi.org/project/kb-pyt…
@ensembl @sinabooeshaghi @lioscro @LambdaMoses We're not the only ones to stumble into this:
Would be great if @ensembl included the code used to make the various files distributed. Maybe they exist at github.com/Ensembl but couldn't find them. cc @PaulFlicek
Would be great if @ensembl included the code used to make the various files distributed. Maybe they exist at github.com/Ensembl but couldn't find them. cc @PaulFlicek
If you are interested in analyzing #SingleCell #RNAseq data in #Bioconductor using #rstats, please check out our paper Orchestrating single-cell analysis (OSCA) with @Bioconductor that was published in @naturemethods this week! #genomics #scRNAseq #dataviz #methodsmatter
@Bioconductor @naturemethods OSCA is a rich, reproducible, accessible (from beginners to experts!) resource with many #scRNAseq workflows & datasets. The resource is an online #bookdown book that compiles every night to track development by the open-source and open-development @Bioconductor #rstats community
@Bioconductor @naturemethods Now, OSCA is not the only set of packages / workflows for the analysis of #scRNAseq data. #Scanpy (scanpy.readthedocs.io/en/stable/) and #Seurat (satijalab.org/seurat/) and are two incredibly popular packages in #python and #rstats, respectively.
🚨Tweetorial: NTRK 101 🚨Given the data discussed at @asco #ASCO2019 & @sno #SNO2019 on #NTRK fusions in primary brain tumors & brain mets, I thought we could review the background of these fusions, how to find them, and then look at potential options for tx. #btsm
<Disclaimer: I’m a speaker for Bayer (larotrectinib) and a subI for the STARTRK trial using entrectinib. I also have ongoing research work w @carisls > I’m keeping this as bias-free as possible though. Ok, next...
