Chronos: a CRISPR cell population dynamics model

Abstract

CRISPR loss of function screens are a powerful tool to interrogate cancer biology but are known to exhibit a number of biases and artifacts that can confound the results, such as DNA cutting toxicity, incomplete phenotype penetrance and screen quality bias. Computational methods that more faithfully model the CRISPR biological experiment could more effectively extract the biology of interest than typical current methods. Here we introduce Chronos, an algorithm for inferring gene knockout fitness effects based on an explicit model of the dynamics of cell proliferation after CRISPR gene knockout. Chronos is able to exploit longitudinal CRISPR data for improved inference. Additionally, it accounts for multiple sources of bias and can effectively share information across screens when jointly analyzing large datasets such as Project Achilles and Score. We show that Chronos outperforms competing methods across a range of performance metrics in multiple types of experiments.

Understanding the source of METTL3-independent m6A in mRNA

“…The copyright holder for this preprint this version posted December 16, 2021. ; https://doi.org/10.1101/2021.12.15.472866 doi: bioRxiv preprint the DepMap dataset [40][41][42][43][44], we found that 801 of 1054 cell lines are dependent on METTL3 (dependency probability score > 0.5). Therefore most cells lines will not survive after METTL3 knockout.…”

“…To understand which cell lines require METTL3 for survival, we screened the Cancer Dependency Map Project (DepMap) 21Q4 dataset [40][41][42][43][44]. This dataset measures cell proliferation in 1054 cell lines following a CRISPR loss-of-function screen.…”

Nuclear RNA Exosome and Pervasive Transcription: Dual Sculptors of Genome Function

“…An inspection of CRISPR screening of 1054 cancer cell lines using DepMap showed that EXOSC1-10 and DIS3 (EXOSC11) are essential in most cancer cell lines (Figure 3a) [100,101]. PABPN1 and MTR4, as well as CBCA components and ZC3H18, also exhibit similar essentialness.…”

Lagging strand gap suppression connects BRCA-mediated fork protection to nucleosome assembly by ensuring PCNA-dependent CAF-1 recycling

“…Cellular dependency data was obtained from the DepMap Public 21Q3 dataset using the DepMap portal (depmap.org/portal). Gene knockout effects (CERES) from project Achilles CRISPR screens were obtained for BRCA1-wildtype cell lines and for cell lines bearing a deleterious mutation for BRCA1 in accordance to the Cancer Cell Line Encyclopedia Project (CCLE) (Dempster et al, 2019(Dempster et al, , 2021Meyers et al, 2017;Pacini et al, 2021). CHAF1A and HIRA gene effects were then plotted as a regression for BRCA1wildtype and BRCA-mutant samples.…”