Journal of Microbiology

Volume 58(3); March 2020

Editorial

User guides for biologists to learn computational methods: Dokyun Na; J. Microbiol. 2020;58(3):173-175. Published online February 27, 2020; DOI: https://doi.org/10.1007/s12275-020-9723-1

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11 Citations

Abstract: System-wide studies of a given molecular type are referred to as “omics.” These include genomics, proteomics, and metabolomics, among others. Recent biotechnological advances allow for high-throughput measurement of cellular components, and thus it becomes possible to take a snapshot of all molecules inside cells, a form of omics study. Advances in computational modeling methods also make it possible to predict cellular mechanisms from the snapshots. These technologies have opened an era of computation-based biology. Component snapshots allow the discovery of gene-phenotype relationships in diseases, microorganisms in the human body, etc. Computational models allow us to predict new outcomes, which are useful in strain design in metabolic engineering and drug discovery from protein-ligand interactions. However, as the quantity of data increases or the model becomes complicated, the process becomes less accessible to biologists. In this special issue, six protocol articles are presented as user guides in the field of computational biology.

Journal Articles

Microbial community analysis using high-throughput sequencing technology: a beginner’s guide for microbiologists: Jihoon Jo , Jooseong Oh , Chungoo Park; J. Microbiol. 2020;58(3):176-192. Published online February 27, 2020; DOI: https://doi.org/10.1007/s12275-020-9525-5

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51 Citations

Abstract: Microbial communities present in diverse environments from deep seas to human body niches play significant roles in the complex ecosystem and human health. Characterizing their structural and functional diversities is indispensable, and many approaches, such as microscopic observation, DNA fingerprinting, and PCR-based marker gene analysis, have been successfully applied to identify microorganisms. Since the revolutionary improvement of DNA sequencing technologies, direct and high-throughput analysis of genomic DNA from a whole environmental community without prior cultivation has become the mainstream approach, overcoming the constraints of the classical approaches. Here, we first briefly review the history of environmental DNA analysis applications with a focus on profiling the taxonomic composition and functional potentials of microbial communities. To this end, we aim to introduce the shotgun metagenomic sequencing (SMS) approach, which is used for the untargeted (“shotgun”) sequencing of all (“meta”) microbial genomes (“genomic”) present in a sample. SMS data analyses are performed in silico using various software programs; however, in silico analysis is typically regarded as a burden on wet-lab experimental microbiologists. Therefore, in this review, we present microbiologists who are unfamiliar with in silico analyses with a basic and practical SMS data analysis protocol. This protocol covers all the bioinformatics processes of the SMS analysis in terms of data preprocessing, taxonomic profiling, functional annotation, and visualization.

Instruction of microbiome taxonomic profiling based on 16S rRNA sequencing: Hyojung Kim , Sora Kim , Sungwon Jung; J. Microbiol. 2020;58(3):193-205. Published online February 27, 2020; DOI: https://doi.org/10.1007/s12275-020-9556-y

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20 Citations

Abstract: Recent studies on microbiome highlighted their importance in various environments including human, where they are involved in multiple biological contexts such as immune mechanism, drug response, and metabolism. The rapid increase of new findings in microbiome research is partly due to the technological advances in microbiome identification, including the next-generation sequencing technologies. Several applications of different next-generation sequencing platforms exist for microbiome identification, but the most popular method is using short-read sequencing technology to profile targeted regions of 16S rRNA genes of microbiome because of its low-cost and generally reliable performance of identifying overall microbiome compositions. The analysis of targeted 16S rRNA sequencing data requires multiple steps of data processing and systematic analysis, and many software tools have been proposed for such procedures. However, properly organizing and using such software tools still require certain level of expertise with computational environments. The purpose of this article is introducing the concept of computational analysis of 16S rRNA sequencing data to microbiologists and providing easy-to-follow and step-by-step instructions of using recent software tools of microbiome analysis. This instruction may be used as a quick guideline for general next-generation sequencing-based microbiome studies or a template of constructing own software pipelines for customized analysis.

Machine learning methods for microbiome studies: Junghyun Namkung; J. Microbiol. 2020;58(3):206-216. Published online February 27, 2020; DOI: https://doi.org/10.1007/s12275-020-0066-8

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65 Citations

Abstract: Researches on the microbiome have been actively conducted worldwide and the results have shown human gut bacterial environment significantly impacts on immune system, psychological conditions, cancers, obesity, and metabolic diseases. Thanks to the development of sequencing technology, microbiome studies with large number of samples are eligible on an acceptable cost nowadays. Large samples allow analysis of more sophisticated modeling using machine learning approaches to study relationships between microbiome and various traits. This article provides an overview of machine learning methods for non-data scientists interested in the association analysis of microbiomes and host phenotypes. Once genomic feature of microbiome is determined, various analysis
methods
can be used to explore the relationship between microbiome and host phenotypes that include penalized regression, support vector machine (SVM), random forest, and artificial neural network (ANN). Deep neural network methods are also touched. Analysis procedure from environment setup to extract analysis results are presented with Python programming language.

STATR: A simple analysis pipeline of Ribo-Seq in bacteria: Donghui Choe , Bernhard Palsson , Byung-Kwan Cho; J. Microbiol. 2020;58(3):217-226. Published online January 28, 2020; DOI: https://doi.org/10.1007/s12275-020-9536-2

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9 Citations

Abstract: Gene expression changes in response to diverse environmental stimuli to regulate numerous cellular functions. Genes are expressed into their functional products with the help of messenger RNA (mRNA). Thus, measuring levels of mRNA in cells is important to understand cellular functions. With advances in next-generation sequencing (NGS), the abundance of cellular mRNA has been elucidated via transcriptome sequencing. However, several studies have found a discrepancy between mRNA abundance and protein levels induced by translational regulation, including different rates of ribosome entry and translational pausing. As such, the levels of mRNA are not necessarily a direct representation of the protein levels found in a cell. To determine a more precise way to measure protein expression in cells, the analysis of the levels of mRNA associated with ribosomes is being adopted. With an aid of NGS techniques, a single nucleotide resolution footprint of the ribosome was determined using a method known as Ribo- Seq or ribosome profiling. This method allows for the highthroughput measurement of translation in vivo, which was further analyzed to determine the protein synthesis rate, translational pausing, and cellular responses toward a variety of environmental changes. Here, we describe a simple analysis pipeline for Ribo-Seq in bacteria, so-called simple translatome analysis tool for Ribo-Seq (STATR). STATR can be used to carry out the primary processing of Ribo-Seq data, subsequently allowing for multiple levels of translatome study, from experimental validation to in-depth analyses. A command- by-command explanation is provided here to allow a broad spectrum of biologists to easily reproduce the analysis.

Setup of a scientific computing environment for computational biology: Simulation of a genome-scale metabolic model of Escherichia coli as an example: Junhyeok Jeon , Hyun Uk Kim; J. Microbiol. 2020;58(3):227-234. Published online February 27, 2020; DOI: https://doi.org/10.1007/s12275-020-9516-6

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7 Citations

Abstract: Computational analysis of biological data is becoming increasingly important, especially in this era of big data. Computational analysis of biological data allows efficiently deriving biological insights for given data, and sometimes even counterintuitive ones that may challenge the existing knowledge. Among experimental researchers without any prior exposure to computer programming, computational analysis of biological data has often been considered to be a task reserved for computational biologists. However, thanks to the increasing availability of user-friendly computational resources, experimental researchers can now easily access computational resources, including a scientific computing environment and packages necessary for data analysis. In this regard, we here describe the process of accessing Jupyter Notebook, the most popular Python coding environment, to conduct computational biology. Python is currently a mainstream programming language for biology and biotechnology. In particular, Anaconda and Google Colaboratory are introduced as two representative options to easily launch Jupyter Notebook. Finally, a Python package COBRApy is demonstrated as an example to simulate 1) specific growth rate of Escherichia coli as well as compounds consumed or generated under a minimal medium with glucose as a sole carbon source, and 2) theoretical production yield of succinic acid, an industrially important chemical, using E. coli. This protocol should serve as a guide for further extended computational analyses of biological data for experimental researchers without computational background.

User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation: Bilal Shaker , Myung-Sang Yu , Jingyu Lee , Yongmin Lee , Chanjin Jung , Dokyun Na; J. Microbiol. 2020;58(3):235-244. Published online February 27, 2020; DOI: https://doi.org/10.1007/s12275-020-9563-z

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32 Citations

Abstract: Due to accumulating protein structure information and advances in computational methodologies, it has now become possible to predict protein-compound interactions. In biology, the classic strategy for drug discovery has been to manually screen multiple compounds (small scale) to identify potential drug compounds. Recent strategies have utilized computational drug discovery methods that involve predicting target protein structures, identifying active sites, and finding potential inhibitor compounds at large scale. In this protocol article, we introduce an in silico drug discovery protocol. Since multi-drug resistance of pathogenic bacteria remains a challenging problem to address, UDP-N-acetylmuramate- L-alanine ligase (murC) of Acinetobacter baumannii was used as an example, which causes nosocomial infection in hospital setups and is responsible for high mortality worldwide. This protocol should help microbiologists to expand their knowledge and research scope.

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