Biology subjects

Abe, K.

Publications and source records attributed to Abe, K..

3 recordsLinked to original sources

bioRxiv2018

A Latent Allocation Model for the Analysis of Microbial Composition and Disease

BackgroundEstablishing the relationship between microbiota and specific disease is important but requires appropriate statistical methodology. A specialized feature of microbiome count data is the presence of a large number of zeros, which makes it difficult to analyze in case-control studies. Most existing approaches either add a small number called a pseudo-count or use probability models such as the multinomial and Dirichlet-multinomial distributions to explain the excess zero counts, which may produce unnecessary biases and impose a correlation structure taht is unsuitable for microbiome data.\n\nResultsThe purpose of this article is to develop a new probabilistic model, called BERMUDA (BERnoulli and MUltinomial Distribution-based latent Allocation), to address these problems. BERMUDA enables us to describe the differences in bacteria composition and a certain disease among samples. We also provide a simple and efficient learning procedure for the proposed model using an annealing EM algorithm.\n\nConclusionWe illustrate the performance of the proposed method both through both the simulation and real data analysis. BERMUDA is implemented with R and is available from GitHub (https://github.com/abikoushi/Bermuda).

bioinformatics↗

bioRxiv2018

ENIGMA: An Enterotype-Like Unigram Mixture Model for Microbial Association Analysis

BackgroundOne of the major challenges in microbial studies is to discover associations between microbial communities and a specific disease. A specialized feature of microbiome count data is that intestinal bacterial communities have clusters reffered as enterotype characterized by differences in specific bacterial taxa, which makes it difficult to analyze these data under health and disease conditions. Traditional probabilistic modeling cannot distinguish dysbiosis of interest with the individual differences.\n\nResultsWe propose a new probabilistic model, called ENIGMA (Enterotype-like uNIGram mixture model for Microbial Association analysis), to address these problems. ENIGMA enables us to simultaneously estimate enterotype-like clusters characterized by the abundances of signature bacterial genera and environmental effects associated with the disease.\n\nConclusionWe illustrate the performance of the proposed method both through the simulation and clinical data analysis. ENIGMA is implemented with R and is available from GitHub (https://github.com/abikoushi/enigma).

bioinformatics↗

bioRxiv2017

Cell Type Specific Survey of Epigenetic Modifications by Tandem Chromatin Immunoprecipitation Sequencing

BackgroundThe nervous system of higher eukaryotes is composed of numerous types of neurons and glia that together orchestrate complex neuronal responses. However, this complex pool of cells typically poses analytical challenges in investigating gene expression profiles and their epigenetic basis for specific cell types. Here, we developed a novel method that enables cell type-specific analyses of epigenetic modifications using tandem chromatin immunoprecipitation sequencing (tChIP-Seq).\n\nResultsFLAG-tagged histone H2B, a constitutive chromatin component, was first expressed in Camk2a-positive pyramidal cortical neurons and used to purify chromatin in a cell type-specific manner. Subsequent chromatin immunoprecipitation using antibodies against H3K4me3--an active promoter mark--allowed us to survey neuron-specific coding and non-coding transcripts. Indeed, tChIP-Seq identified hundreds of genes associated with neuronal functions and genes with unknown functions expressed in cortical neurons.\n\nConclusionstChIP-Seq thus provides a versatile approach to investigating the epigenetic modifications of particular cell types in vivo.

molecular biology↗