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Biology subjects

Ambani, K.

Publications and source records attributed to Ambani, K..

2 recordsLinked to original sources

Temporal niche pursuit in a simulated evolution of sleep/wake patterns

It was long believed early mammals were nocturnal to avoid interactions with day-active dinosaurs. However, recent evidence indicates many dinosaurs were likely nocturnal, suggesting more complex coevolutionary dynamics prevailed. We simulated coevolution of sleep in a general predator/prey system, using a physiological model. We discovered temporal niche pursuit cycles across evolutionary timescales: prey repeatedly escaping into a novel temporal niche, with predators subsequently invading that niche. We characterized multiple oscillatory patterns for pursuit, involving distinct genetic and phenotypic mechanisms. A low-dimensional model recapitulated the dynamics of the physiological model. These findings reveal rich dynamical processes underlying selection of temporal niche.

evolutionary biology↗

Benchmarking normalisation methods for differential binding analysis in CUT&RUN

CUT&RUN (Cleavage Under Targets and Release Using Nuclease) is an increasingly popular method for profiling protein interactions (transcription factors, histone modifications, etc) with DNA across the whole genome. When performing differential binding analysis of CUT&RUN data to identify genomic regions where interaction profiles vary between conditions, data normalisation is essential for accurate biological interpretations. Despite this, there are no clear guidelines on the optimal normalisation method for CUT&RUN datasets. Here, we examine five normalisation approaches (spike-in, library size, background, reads-in-peak and greenlist) and highlight that different methods can result in widely discrepant interpretations of the data. We test these normalisation methods by simulating a variety of plausible differential binding scenarios as well as an in-house generated dataset. We determined that normalisation by either (i) library size or (ii) background to be the most robust. Importantly, we find spike-in normalisation to be the least reliable method. Our findings inform the use of normalisation methods for CUT&RUN data and should thus facilitate reproducible and robust analysis.

bioinformatics↗