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Laufer, L.

Publications and source records attributed to Laufer, L..

2 recordsLinked to original sources

Neurodegeneration impairs the consolidation of developmentally synchronized behavioral patterns

Animals generate predictable patterns of behavior that are robustly synchronized with the developmental clock. However, the dynamics of the long-term establishment of synchronized behaviors across the entire developmental trajectory, and the neuronal circuits that control this developmental synchronization, remain underexplored. Here, we show that in C. elegans individuals, developmental synchronization of behavior continuously consolidates throughout development time, and that this consolidation is controlled by multiple neuronal pathways. In particular, we found that the degeneration of a specific mechanosensory circuit early in development strongly impairs the developmental synchronization of behavior. Furthermore, early neuronal activity patterns in downstream motor neurons are altered following the mechanosensory circuit degeneration. Interestingly, we show that either pharmacological or genetic neuroprotection of the mechanosensory circuit from degeneration restores normal modes of developmentally synchronized behaviors. These results imply the control of time-locked behaviors across the developmental trajectory by a localized sensory circuit within the nervous system.

neuroscience↗

EpiSegMix: A Flexible Distribution Hidden Markov Model with Duration Modeling for Chromatin State Discovery

MotivationAutomated chromatin segmentation based on ChIP-seq data reveals insights into the epigenetic regulation of chromatin accessibility. Existing segmentation methods are constrained by simplifying modeling assumptions, which may have a negative impact on the segmentation quality. ResultsWe introduce EpiSegMix, a novel segmentation method based on a hidden Markov model with flexible read count distribution types and state duration modeling, allowing for a more flexible modeling of both histone signals and segment lengths. In a comparison with two existing tools, ChromHMM, Segway and EpiCSeg, we show that EpiSegMix is more predictive of cell biology, such as gene expression. Its flexible framework enables it to fit an accurate probabilistic model, which has the potential to increase the biological interpretability of chromatin states. Availability and implementationSource code: https://gitlab.com/rahmannlab/episegmix.

bioinformatics↗