And a big thank you to support from:
@novo-nordisk.bsky.social
@igvfconsortium.bsky.social
NIH-NHLBI
@americanheart.bsky.social

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Thanks to Lars Steinmetz + @argschwind.bsky.social for developing the original TAP-seq method and collaborating on this study

Thanks to Gene Katsevich and Tim Barry for developing SCEPTRE and collaborating to explore how best to apply it to enhancer perturbation data

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This was a huge team effort —

Judhajeet + Evvie + Dulguun led the development DC-TAP-seq and design + execution of the random screens

James + Evvie led analysis of random screens

Maya + Andreas compared the effects to models and teased out indirect effects

Congratulations all!

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e.g. see preprint from the Quertermous lab using DC-TAP-seq to target elements containing variants for coronary artery disease in smooth muscle cells

The high statistical power of these experiments will be very important for finding effects in GWAS loci

www.medrxiv.org/content/10.1...

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We are excited to help you set these types of experiments in new systems and expand these data 10- to 100-fold in the next few years to better understand regulatory elements, improve predictive models, and interpret genetic variants.

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We hope that these tools are useful for you! This study presents our most complete toolkit to date for designing, conducting, and analyzing regulatory element CRISPR perturbation studies.

Code, protocols, data — all available now!

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Overall, these observations were consistent across the 2 cell types (K562 and hiPSCs), suggesting that they are likely to be more general beyond the favorite workhorse cancer cell line.

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These unbiased CRISPRi datasets will help to evaluate predictive models (stay tuned for results for scE2G and ENCODE-rE2G)

Here, we show that this evaluation must account for the magnitude of effect sizes, frequency of indirect effects, chromatin states, and gene class.

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