gutMGene: a comprehensive database for target genes of gut microbes and microbial metabolites

Abstract

gutMGene (http://bio-annotation.cn/gutmgene), a manually curated database, aims at providing a comprehensive resource of target genes of gut microbes and microbial metabolites in humans and mice. Metagenomic sequencing of fecal samples has identified 3.3 × 106 non-redundant microbial genes from up to 1500 different species. One of the contributions of gut microbiota to host biology is the circulating pool of bacterially derived small-molecule metabolites. It has been estimated that 10% of metabolites found in mammalian blood are derived from the gut microbiota, where they can produce systemic effects on the host through activating or inhibiting gene expression. The current version of gutMGene documents 1331 curated relationships between 332 gut microbes, 207 microbial metabolites and 223 genes in humans, and 2349 curated relationships between 209 gut microbes, 149 microbial metabolites and 544 genes in mice. Each entry in the gutMGene contains detailed information on a relationship between gut microbe, microbial metabolite and target gene, a brief description of the relationship, experiment technology and platform, literature reference and so on. gutMGene provides a user-friendly interface to browse and retrieve each entry using gut microbes, disorders and intervention measures. It also offers the option to download all the entries and submit new experimentally validated associations.

Genomic Variation Prediction: A Summary From Different Views

“…Among them, the use of machine learning methods to predict pathogenic synonymous mutations is still in the preliminary stage. The main problems that remain to be solved include: 1) positive sample data is scarce and standard negative sample data is lacking ( Zhang et al, 2020b ); 2) feature representation ability is weak and not easy to promote ( Buske et al, 2013 ; Wei et al, 2018 ; Xiong et al, 2018 ; Jin et al, 2019 ; Shen et al, 2019 ; Su et al, 2019 ; Wei et al, 2019 ; Yang et al, 2020a ; Zhang et al, 2020c ; Peng et al, 2020 ; Su et al, 2020 ; Teng et al, 2020 ; Chu et al, 2021a ; Cheng et al, 2021b ; Chu et al, 2021b ; Jin et al, 2021 ; Su et al, 2021 ); and 3) the prediction performances of existing methods need to be improved, and the results of different methods have a low degree of coincidence ( Cheng et al, 2019 ). The methods reviewed in this article aim to solve these problems.…”

Research on the Computational Prediction of Essential Genes

“…At the same time, research show many proteins perform specific biological functions only after they participate in the formation of protein complexes and interact with other proteins in the complex, suggesting that protein interactions are related to protein complexes. Therefore, this part takes the key proteins as the research object and uses the methods based on the subcellular protein interaction network and subcellular importance to identify the key proteins ( Cheng et al, 2021a ; Zulfiqar et al, 2021 ).…”

“…The environment and genetics jointly determine important physiological processes such as reproduction, cell division, and protein synthesis. Genes are involved in diverse phenomena such as birth, growth, decline, illness, aging, and death ( Yang et al, 2015 ; Hu et al, 2020 ; Cheng et al, 2021a ). Except for some viruses whose genes are composed of ribonucleic acid (RNA) ( Sun et al, 2013 ; Riaz and Li, 2019 ), in most organisms, genes are composed of deoxyribonucleic acid (DNA) arranged linearly on chromosomes.…”

Bioinformatics Research on Drug Sensitivity Prediction

“…We first discuss the types of evolutionary models of drug resistance studies on drug-tolerant tumor cells after treatment. Meanwhile, we describe the process of models, including the construction of a time-series biological network and the evolution prediction of the tumor drug resistance state via k-means++ clustering, random walk, and other machine learning methods ( Oxnard and Geoffrey, 2016 ; Hangauer et al, 2017 ; Recasens and Munoz, 2019 ; Yu et al, 2020a ; Cheng et al, 2021 ). Second, we describe the strategy of sensitivity prediction of tumors to anti-cancer drugs using graph representation learning.…”