Jake Reske

Bioinformatics & Systems Biology @Zoetis. Formerly @MSUMD @MSUgenetics. All things 'omics.

[Jake Reske @reskejak]

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.
https://t.co/L73u4BgwEY

[Jake Reske @reskejak]

@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?

[William J. Greenleaf @WJGreenleaf]

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! https://t.co/4zjMOlOgVV

[bioRxiv Genomics @biorxiv_genomic]

Perspectives on Codebook: sequence specificity of uncharacterized human transcription factors https://t.co/iA3AVfnJ94 #biorxiv_genomic

[Jamie Hackett @hackettlab]

Very stoked to go through this!

Sequenced-based deep learning is rapidly picking apart the underlying logic of gene regulation

https://t.co/CM1fbUQs5I

[Wanding Zhou @zhouwanding]

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: https://t.co/21Wx8tgpn4 #genomics #bioinformatics

[Jake Reske @reskejak]

Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures
https://t.co/tWBVJDvDjq

[Jake Reske @reskejak]

CASGEVY (FDA approved CRISPR therapy for sickle cell) prescribing packet claims no genotoxicity data? Curious on off-target effects and long-term consequences. #CASGEVY
https://t.co/AkbkazjMMg

[Hui Shen @shenhui1986]

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

[Andreas Gschwind @argschwind]

Excited to share our latest work on bioRxiv!

https://t.co/IeJUyi7Cvk

A highly collaborative effort with @ksmualim @karbalayghareh, @mayayayas, @kanishkadey, @evelynjagoda,@WangXijhu, @LarsMSteinmetz, @anshulkundaje, @jengreitz and many others from @ENCODE_NIH!

Thread👇

[Hajk-Georg Drost @HajkDrost]

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 ⏩⏩https://t.co/VPy4fYMOsb

[Nicole Renke @NicoleRenke]

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.
https://t.co/Juj9uLC2nV

[Jake Reske @reskejak]

@arjunrajlab A physical basis for quantitative ChIP-sequencing
https://t.co/GxFa2tkptL

[Jake Reske @reskejak]

@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.

[Steven Salzberg 💙💛 @StevenSalzberg1]

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 https://t.co/YPy9ICed9C

[Ronald Chandler @Chandler_Lab]

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. https://t.co/grlmPjct1y

[Michael Love @mikelove]

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

[Constantin Ahlmann-Eltze (@const-ae.bsky.social) @const_ae]

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 https://t.co/0caikoVmBa and https://t.co/X61ICd6sao

[Fabian Theis @fabian_theis]

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 https://t.co/dN1jwlmIZA & extension at https://t.co/NVurPpJaHH.

[Ben Martin @bmart87]

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):
https://t.co/2aNGRoVgJz

[Jake Reske @reskejak]

@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!

[Jake Reske @reskejak]

@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.