ABSTRACT:Rhodococcus equi was isolated from the submaxillary lymph nodes of wild boars (Sus scrofa) in Wakayama and Hyogo, Japan, with a high prevalence. Rhodococcus equi isolation rates between locations, sexes, or body weights were not different, except in the prevalence of vapB-positive R. equi between locations.
In selecting drug target candidates for pharmaceutical research, the linkage to disease and the tractability of the target are two important factors that can ultimately determine the drug efficacy. Several existing resources can provide gene-disease associations, but determining whether such a list of genes are attractive drug targets often requires further information gathering and analysis. In addition, few resources provide the information required to evaluate the tractability of a target. To address these issues, we have updated TargetMine, a data warehouse for assisting target prioritization, by integrating new data sources for gene-disease associations and enhancing functionalities for target assessment. As a data mining platform that integrates a variety of data sources, including protein structures and chemical compounds, TargetMine now offers a powerful and flexible interface for constructing queries to check genetic evidence, tractability and other relevant features for the candidate genes. We demonstrate these features by using several specific examples.
Optimizing and automating a protocol for 16S microbiome data analysis with QIIME2 is a challenging task. It involves a multi-step process, and multiple parameters and options that need to be tested and determined. In this article, we describe Snaq, a snakemake pipeline that helps automate and optimize 16S data analysis using QIIME2. Snaq offers an informative file naming system and automatically performs the analysis of a data set by downloading and installing the required databases and classifiers, all through a single command-line instruction. It works natively on Linux and Mac and on Windows through the use of containers, and is potentially extendable by adding new rules. This pipeline will substantially reduce the efforts in sending commands and prevent the confusion caused by the accumulation of analysis results due to testing multiple parameters.
In selecting drug target candidates for pharmaceutical research, the linkage to disease and the tractability of the target are two important factors that can ultimately determine the drug efficacy. Several existing resources can provide gene-disease associations, but determining whether such a list of genes are attractive drug targets often requires further information gathering and analysis. In addition, few resources provide the information required to evaluate the tractability of a target. To address these issues, we have updated TargetMine, a data warehouse for assisting target prioritization, by integrating new data sources for gene-disease associations and enhancing functionalities for target assessment. As a data mining platform that integrates a variety of data sources, including protein structures and chemical compounds, TargetMine now offers a powerful and flexible interface for constructing queries to check genetic evidence, tractability and other relevant features for the candidate genes. We demonstrate these features by using several specific examples.
Medical information is valuable information obtained from humans regarding the phenotype of diseases. Omics data is informative to understand diseases at biomolecular level. We aimed to detect patient stratification patterns in a data-driven manner and identify candidate drug targets by investigating biomolecules that are linked to phenotype-level characteristics of a targeted disease. Such data integration is challenging because the data types of them are different, and these data contain many items that are not directly related to the disease. Hence, we developed an algorithm, subset binding, to find inter-related attributes in heterogeneous data. To search for potential drug targets for intractable IPF (idiopathic pulmonary fibrosis), we collected medical information and proteome data of serum extracellular vesicles from patients with interstitial pneumonia including IPF. Our approach detected 20 proteins linked with IPF characteristics, whose expression intensities were confirmed to be high in fibrotic areas of human lung tissues. Furthermore, ponatinib, which inhibits these proteins, suppressed EMT (epithelial mesenchymal transition) in vitro. This workflow paves the way for data-driven drug target discovery even for intractable diseases whose mechanisms of pathogenesis are not fully understood.
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