Jake Reske

A chromatin mechanism hiding in plain sight. We found that a fundamental function of mammalian ARID1A-SWI/SNF chromatin remodeling is to facilitate H3.3 variant histone exchange, likely by ejecting canonical H3.1/3.2. bmcbiol.biomedcentral.com/articles/10.11…

@Anders_S_Hansen @hansen_lab @MITdeptofBE @kochinstitute @SGrosseHolz @labronic_mike @MachPia @Phlya @zechnerlab @leonidmirny Naive question but does this agree with TF dynamics (on/off rates) and other chromatin interacting factors e.g. from live imaging?

Mostly on BlåSky but excited to share James Jusuf's preprint: By integrating Micro-C with SuperRes Live-Imaging we perform absolute quantification of looping (e.g. this loop is present 3%). We quantify mESC 36k loops: <loops> are generally rare (2.3%). biorxiv.org/content/10.110…

Incredibly excited that our paper linking single molecule states of TF binding to gene expression using quantitative thermodynamic models is out in Nature today. An amazing collaboration with the Bintu Lab. Congrats to Ben, Michaela, and Julia! nature.com/articles/s4158…

Perspectives on Codebook: sequence specificity of uncharacterized human transcription factors biorxiv.org/cgi/content/sh… #biorxiv_genomic

Very stoked to go through this! Sequenced-based deep learning is rapidly picking apart the underlying logic of gene regulation biorxiv.org/content/10.110…

Facing challenges with data harmonization on EPICv2? Our latest preprint introduces mLiftOver, designed for data conversion between HM27/HM450/EPIC and EPICv2, including support for the new MSA array! Details here: biorxiv.org/content/10.110… #genomics #bioinformatics

Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures nature.com/articles/s4159…

CASGEVY (FDA approved CRISPR therapy for sickle cell) prescribing packet claims no genotoxicity data? Curious on off-target effects and long-term consequences. #CASGEVY fda.gov/vaccines-blood…

Check out our tool for WGBS duplicate marking (you can use it for WGS too)! Great work by Jacob Morrison, @biobenkj, @zhouwanding

Excited to share our latest work on bioRxiv! biorxiv.org/content/10.110… A highly collaborative effort with @ksmualim @karbalayghareh, @mayayayas, @kanishkadey, @evelynjagoda,@WangXijhu, @LarsMSteinmetz, @anshulkundaje, @jengreitz and many others from @ENCODE_NIH! Thread👇

Data retrieval for massive (comparative) genomics is not easy, especially when you care about genome origin, assembly quality & functional annotation. We hope new release {biomartr} v1.0.5 will help you to automate retrieval & reproducibility at scale ⏩⏩github.com/ropensci/bioma…

Evaluation of MethylationEPIC v2.0 now published in Epigenetics Communication! Another great collaboration led by the labs of @zhouwanding and @peterwlaird. Comment below if you have any questions about the BeadChip & its performance. epicom.biomedcentral.com/articles/10.11…

@arjunrajlab A physical basis for quantitative ChIP-sequencing sciencedirect.com/science/articl…

Part of what I’m wondering about is if there’s any comparison to some other ground truth. But not sure what that ground truth would be…

I common hear that ChIP seq et al are not quantitative. What does that mean? If a peak is like 3x higher, should I not interpret that as higher binding probability? What is the evidence for or against?

@arjunrajlab Signal at a given locus tells you nothing about the overall efficiency of the ChIP reaction in that sample. Reads mapping to "background" genome can vary widely between samples. Relative binding signal at a locus within the sample is a better indicator.

R fans! If you or your students want a gentle, free intro to R programming, check out "R for the Intimidated" by @bugsized (my favorite scientist). It was on @DataCamp (h/t @jtleek) but they removed it, so @bugsized put all 27 lessons on @youtube youtube.com/playlist?list=…

Happy to share our new paper where we show the inflammatory response to insulin in PIK3CA mutant endometrial cells is due to Viperin/RSAD2 overexpression. Thanks to @MikeRoyWilson and our collaborators @reskejak @VAInstitute @UNC_Lineberger @MSUCancer. rdcu.be/dbWBU

This is a really well done python reimplementation of DESeq2 and apeglm, I’ve checked the code closely. Nice work @BorisMuzc @Kramarek et al

Incredibly proud to see our benchmark of single-cell preprocessing methods finally published 🥳🥳🎉 We show that despite its theoretical limitations, no other transformation consistently outperforms log(y/s+1). All details at nature.com/articles/s4159… and github.com/const-ae/trans…

Glad to see our review on best practices for single-cell analysis across modalities out @NatureRevGenet! In a big team effort led by @LukasHeumos & @AnnaCSchaar, we recommend workflows based on benchmarks. Paper at rdcu.be/c8RU5 & extension at sc-best-practices.org.

So excited to see my postdoc work out in the world! Here we show that EP400/TIP60 can broadly compensate for loss of SWI/SNF activity at promoters. See below for a short(ish) thread on our work (1/X): biorxiv.org/content/10.110…

@LewisLab @iskander E.g. repeat the same experiment two more independent times, and plot the data for all 3 experimental batches together. :) Thanks for sharing your data here!

@LewisLab @iskander It would be interesting to explore the variation introduced between batches using the same extraction method. Until demonstrated otherwise, it remains possible that the observed difference between extraction methods can be attributed to batch.

Unsurprisingly, RNA extraction method has a big impact on expression analysis (PC1 = heat stressed vs. unstressed, PC2 = RNeasy vs trizol extraction) #MOESM1" target="_blank" rel="nofollow noopener">link.springer.com/article/10.118…