Biology subjects

Chan, R. C. W.

Publications and source records attributed to Chan, R. C. W..

4 recordsLinked to original sources

bioRxiv2020

Semi-supervised segmentation and genome annotation

Segmentation and genome annotation methods automatically discover joint signal patterns in whole genome datasets. Previously, researchers trained these algorithms in a fully unsupervised way, with no prior knowledge of the functions of particular regions. Adding information provided by expert-created annotations to supervise training could improve the annotations created by these methods. We implemented semi-supervised learning using virtual evidence in the annotation method Segway. Additionally, we defined a positionally tolerant precision and recall metric for scoring genome annotations based on the proximity of each annotation feature to the truth set. We demonstrate semi-supervised Segways ability to learn patterns corresponding to provided transcription start sites on a specified supervision label, and subsequently recover other transcription start sites in unseen data on the same supervision label.

bioinformatics↗

bioRxiv2018

The STROBE: a system for closed-looped optogenetic control of freely feeding flies

Manipulating feeding circuits in freely moving animals is challenging, in part because the timing of sensory inputs is affected by the animals behavior. To address this challenge in Drosophila, we developed the Sip-Triggered Optogenetic Behavior Enclosure (\"STROBE\"). The STROBE is a closed-looped system for real-time optogenetic activation of feeding flies, designed to evoke neural excitation coincident with food contact. We demonstrate that optogenetic stimulation of sweet sensory neurons in the STROBE drives attraction to tasteless food, while activation of bitter sensory neurons promotes avoidance. Moreover, feeding behavior in the STROBE is modified by the flys internal state, as well as the presence of chemical taste ligands. We also find that mushroom body dopaminergic neurons and their respective post-synaptic partners drive opposing feeding behaviors following activation. Together, these results establish the STROBE as a new tool for dissecting fly feeding circuits and suggest a role for mushroom body circuits in processing naive taste responses.

neuroscience↗

bioRxiv2017

Segway 2.0: Gaussian mixture models and minibatch training

SummarySegway performs semi-automated genome annotation, discovering joint patterns across multiple genomic signal datasets. We discuss a major new version of Segway and highlight its ability to model data with substantially greater accuracy. Major enhancements in Segway 2.0 include the ability to model data with a mixture of Gaussians, enabling capture of arbitrarily complex signal distributions, and minibatch training, leading to better learned parameters.\n\nAvailability and ImplementationSegway and its source code are freely available for download at https://segway.hoffmanlab.org. We have made available scripts (https://doi.org/10.5281/zenodo.802940) and datasets (https://doi.org/10.5281/zenodo.802907) for this papers analysis.\n\nContactmichael.hoffman@utoronto.ca

bioinformatics↗

bioRxiv2016

Semi-automated genome annotation using epigenomic data and Segway

Biochemical techniques measure many individual properties of chromatin along the genome. These properties include DNA accessibility (measured by DNase-seq) and the presence of individual transcription factors and histone modifications (measured by ChIP-seq). Segway is software that transforms multiple datasets on chromatin properties into a single annotation of the genome that a biologist can more easily interpret. This protocol describes how to use Segway to annotate the genome, starting with reads from a ChIP-seq experiment. It includes pre-processing of data, training the Segway model, annotating the genome, assigning biological meanings to labels, and visualizing the annotation in a genome browser.

bioinformatics↗