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.
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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.
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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.
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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.
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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.
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User guides for biologists to learn computational methods Dokyun Na Journal of Microbiology.2020; 58(3): 173. CrossRef
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.
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Due to accumulating protein structure information and advances
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screen multiple compounds (small scale) to identify potential
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potential inhibitor compounds at large scale. In this protocol
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