Biology subjects

Brouwer, C. R.

Publications and source records attributed to Brouwer, C. R..

4 recordsLinked to original sources

bioRxiv2023

MetaCerberus: distributed highly parallelized scalable HMM-based implementation for robust functional annotation across the tree of life

SummaryMetaCerberus is an exclusive HMM/HMMER-based tool that is massively parallel, on low memory, and provides rapid scalable annotation for functional gene inference across genomes to metacommunities. It provides robust enumeration of functional genes and pathways across many current public databases including KEGG (KO), COGs, CAZy, FOAM, and viral specific databases (i.e., VOGs and PHROGs). In a direct comparison, MetaCerberus was twice as fast as EggNOG-Mapper, and produced better annotation of viruses, phages, and archaeal viruses than DRAM, PROKKA, or InterProScan. MetaCerberus annotates more KOs across domains when compared to DRAM, with a 186x smaller database and a third less memory. MetaCerberus is fully integrated with differential statistical tools (i.e., DESeq2 and edgeR), pathway enrichment (GAGE R), and Pathview R for quantitative elucidation of metabolic pathways. MetaCerberus implements the key to unlocking the biosphere across the tree of life at scale. Availability and implementationMetaCerberus is written in Python and distributed under a BSD-3 license. The source code of MetaCerberus is freely available at https://github.com/raw-lab/metacerberus. Written in python 3 for both Linux and Mac OS X. MetaCerberus can also be easily installed using mamba create -n metacerberus -c bioconda -c conda-forge metacerberus

bioinformatics↗

bioRxiv2022

Genetic basis and selection of glyceollin induction in wild soybean

O_LIGlyceollins, a family of phytoalexin induced in legume species, play essential roles in responding to environmental stresses and in human health. However, little is known about the genetic basis and selection of glyceollin induction. C_LIO_LIWe employed a metabolite-based genome-wide association (mGWA) approach to identify candidate genes involved in glyceollin induction from genetically diverse and understudied wild soybeans subjected to soybean cyst nematode stress. C_LIO_LIEight SNPs on chromosomes 3, 9, 13, 15, and 20 showed significant association with glyceollin induction. Six genes close to one of the significant SNPs (ss715603454) on chromosome 9 fell into two clusters, and they encode enzymes in the glycosyltransferase class within the phenylpropanoid pathway. Transcription factors (TFs) genes, such as MYB and WRKY were also found within the linkage disequilibrium of the significant SNPs on chromosome 9. Epistasis and a strong selection signal were detected on the four significant SNPs on chromosome 9. C_LIO_LIGene clusters and transcription factors may play important roles in regulating glyceollin induction in wild soybeans. Additionally, as major evolutionary factors, epistatic interactions and selection may influence glyceollin variation in natural populations. C_LI

plant biology↗

bioRxiv2020

A Universal Deep Neural Network for In-Depth Cleaning of Single-Cell RNA-Seq Data

Single cell RNA sequencing (scRNA-Seq) has been widely used in biomedical research and generated enormous volume and diversity of data. The raw data contain multiple types of noise and technical artifacts and need thorough cleaning. The existing denoising and imputation methods largely focus on a single type of noise (i.e. dropouts) and have strong distribution assumptions which greatly limit their performance and application. We designed and developed the AutoClass model, integrating two deep neural network components, an autoencoder and a classifier, as to maximize both noise removal and signal retention. AutoClass is free of distribution assumptions, hence can effectively clean a wide range of noises and artifacts. AutoClass outperforms the state-of-art methods in multiple types of scRNA-Seq data analyses, including data recovery, differential expression analysis, clustering analysis and batch effect removal. Importantly, AutoClass is robust on key hyperparameter settings including bottleneck layer size, pre-clustering number and classifier weight. We have made AutoClass open source at: https://github.com/datapplab/AutoClass.

bioinformatics↗

bioRxiv2019

Yersiniabactin producing AIEC promote inflammation-associated fibrosis in gnotobiotic Il10-/- mice

Fibrosis is a significant complication of intestinal disorders associated with microbial dysbiosis and pathobiont expansion, notably Crohns disease (CD). Mechanisms that favor fibrosis are not well understood and therapeutic strategies are limited. Here we demonstrate that colitis susceptible Il10-deficient mice develop inflammation-associated fibrosis when mono-associated with adherent/invasive Escherichia coli (AIEC) that harbor the yersiniabactin (Ybt) pathogenicity island. Inactivation of Ybt siderophore production in AIEC nearly abrogated fibrosis development in inflamed mice. In contrast, inactivation of Ybt import through its cognate receptor FyuA enhanced fibrosis severity. This corresponded with increased colonic expression of profibrogenic genes prior to the development of histological disease, therefore suggesting causality. FyuA-deficient AIEC also exhibited greater localization within sub-epithelial tissues and fibrotic lesions that was dependent on Ybt biosynthesis and corresponded with increased fibroblast activation in vitro. Together, these findings suggest that Ybt establishes a pro-fibrotic environment in the host in the absence of binding to its cognate receptor and indicates a direct link between intestinal AIEC and the induction of inflammation-associated fibrosis.

microbiology↗