Biology subjects

Bunk, D.

Publications and source records attributed to Bunk, D..

3 recordsLinked to original sources

bioRxiv2021

YeastMate: Neural network-assisted segmentation of mating and budding events in S. cerevisiae

Here, we introduce YeastMate, a user-friendly deep learning-based application for automated detection and segmentation of Saccharomyces cerevisiae cells and their mating and budding events in microscopy images. We build upon Mask R-CNN with a custom segmentation head for the subclassification of mother and daughter cells during lifecycle transitions. YeastMate can be used directly as a Python library or through a stand-alone GUI application and a Fiji plugin as easy to use frontends. The source code for YeastMate is freely available at https://github.com/hoerlteam/YeastMate under the MIT license. We offer packaged installers for our whole software stack for Windows, macOS and Linux. A detailed user guide is available at https://yeastmate.readthedocs.io.

bioinformatics↗

bioRxiv2020

Deciphering the regulatory logic of a Drosophila enhancer through systematic sequence mutagenesis and quantitative image analysis

Transcriptional enhancers are short DNA sequences controlling the spatial activity, timing and levels of eukaryotic gene transcription. Their quantitative transcriptional output is thought to result from the number and organization of transcription factor binding sites (TFBSs). Yet, how the various aspects of regulatory information are encoded in enhancer sequences remains elusive. We addressed this question by quantifying the spatial activity of the yellow spot enhancer active in developing Drosophila wings. To identify which enhancer DNA sequence contributes to enhancer activity, we introduced systematic mutations along the enhancer. We developed an analytic framework that uses comprehensive descriptors to quantify reporter assay in transgenic flies and measure spatial variations in activity levels across the wing. Our analysis highlights an unexpected density of regulatory information in the spot enhancer sequence. Furthermore, it reveals an unanticipated regulatory logic underlying the activity of this enhancer, and how it reads the wing trans-regulatory landscape to encode a spatial pattern.

genetics↗

bioRxiv2020

Distributed coding of stimulus magnitude across the rodent prefrontal cortex

As we interact with the external world, we judge magnitudes from sensory information. The estimation of magnitudes has been characterized in primates, yet it is largely unexplored in non-primate species. Here, we show that gerbils that solve a time-interval reproduction task display primate-like magnitude estimation characteristics, most prominently a systematic overestimation of small stimuli and an underestimation of large stimuli, often referred to as regression effect. We investigated the underlying neural mechanisms by recording from medial prefrontal cortex and show that the majority of neurons respond either during the measurement or the reproduction of a time-interval. Cells that are active during both phases display distinct response patterns. We categorize the neural responses into multiple types and demonstrate that only populations with mixed responses can encode the bias of the regression effect. These results reveal the organizing neural principles of magnitude estimation a process important to higher cognitive functions.

neuroscience↗