Biology subjects

Bonneau, R. A.

Publications and source records attributed to Bonneau, R. A..

3 recordsLinked to original sources

bioRxiv2020

A convolutional neural network for common coordinate registration of high-resolution histology images

Registration of histology images from multiple sources is a pressing problem in large-scale studies of spatial -omics data. Researchers often perform "common coordinate registration," akin to segmentation, in which samples are partitioned based on tissue type to allow for quantitative comparison of similar regions across samples. Accuracy in such registration requires both high image resolution and global awareness, which mark a difficult balancing act for contemporary deep learning architectures. We present a novel convolutional neural network (CNN) architecture that combines (1) a local classification CNN that extracts features from image patches sampled sparsely across the tissue surface, and (2) a global segmentation CNN that operates on these extracted features. This hybrid network can be trained in an end-to-end manner, and we demonstrate its relative merits over competing approaches on a reference histology dataset as well as two published spatial transcriptomics datasets. We believe that this paradigm will greatly enhance our ability to process spatial -omics data, and has general purpose applications for the processing of high-resolution histology images on commercially available GPUs.

bioengineering↗

bioRxiv2019

Structure-Based Function Prediction using Graph Convolutional Networks

The large number of available sequences and the diversity of protein functions challenge current experimental and computational approaches to determining and predicting protein function. We present a deep learning Graph Convolutional Network (GCN) for predicting protein functions and concurrently identifying functionally important residues. This model is initially trained using experimentally determined structures from the Protein Data Bank (PDB) but has significant de-noising capability, with only a minor drop in performance observed when structure predictions are used. We take advantage of this denoising property to train the model on > 200,000 protein structures, including many homology-predicted structures, greatly expanding the reach and applications of the method. Our model learns general structure-function relationships by robustly predicting functions of proteins with [≤] 40% sequence identity to the training set. We show that our GCN architecture predicts functions more accurately than Convolutional Neural Networks trained on sequence data alone and previous competing methods. Using class activation mapping, we automatically identify structural regions at the residue-level that lead to each function prediction for every confidently predicted protein, advancing site-specific function prediction. We use our method to annotate PDB and SWISS-MODEL proteins, making several new confident function predictions spanning both fold and function classifications.

bioinformatics↗

bioRxiv2018

Sequence-Dependent DNA Shape Features Regulate IL-4 Induced Chromatin Accessibility in Alternatively Activated Macrophages

Interleukin-4 (IL-4) activates macrophages to adopt a distinct phenotype associated with clearance of helminth infections and tissue repair. Here, we describe changes in the accessible chromatin landscape following IL-4 stimulation of terminally differentiated mouse peritoneal macrophages. This chromatin remodeling process occurs in both tissue resident and monocyte-derived macrophages, but the regions gaining accessibility post-stimulation are macrophage-specific. PU.1 motif is similarly associated with tissue resident and monocyte-derived IL-4 induced regions, but has macrophage-specific DNA shape and predicted co-factors. In addition, IL-4 stimulation leads to short-term dampening of macrophage secondary response. However, the degree of dampening differs between macrophages derived from different genetic backgrounds. Together, these results lead us to propose that DNA sequence variations can alter parts of the accessible chromatin landscape and differences in secondary responses due to host genetics can contribute to phenotypic variations in immune responses.

immunology↗