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- Application of computational approaches to analyze metagenomic data
- Ho-Jin Gwak , Seung Jae Lee , Mina Rho
- J. Microbiol. 2021;59(3):233-241. Published online February 10, 2021
- DOI: https://doi.org/10.1007/s12275-021-0632-8
- 61 View
- 0 Download
- 12 Web of Science
- 12 Crossref
-
Abstract
-
Microorganisms play a vital role in living systems in numerous
ways. In the soil or ocean environment, microbes are
involved in diverse processes, such as carbon and nitrogen
cycle, nutrient recycling, and energy acquisition. The relation
between microbial dysbiosis and disease developments has
been extensively studied. In particular, microbial communities
in the human gut are associated with the pathophysiology
of several chronic diseases such as inflammatory bowel disease
and diabetes. Therefore, analyzing the distribution of microorganisms
and their associations with the environment
is a key step in understanding nature. With the advent of nextgeneration
sequencing technology, a vast amount of metagenomic
data on unculturable microbes in addition to culturable
microbes has been produced. To reconstruct microbial
genomes, several assembly algorithms have been developed
by incorporating metagenomic features, such as uneven
depth. Since it is difficult to reconstruct complete microbial
genomes from metagenomic reads, contig binning approaches
were suggested to collect contigs that originate from the same
genome. To estimate the microbial composition in the environment,
various methods have been developed to classify
individual reads or contigs and profile bacterial proportions.
Since microbial communities affect their hosts and environments
through metabolites, metabolic profiles from metagenomic
or metatranscriptomic data have been estimated.
Here, we provide a comprehensive review of computational
methods
that can be applied to investigate microbiomes using metagenomic and metatranscriptomic sequencing data. The limitations of metagenomic studies and the key approaches to overcome such problems are discussed. -
Citations
Citations to this article as recorded by
- A Review of Web-Based Metagenomics Platforms for Analysing Next-Generation Sequence Data
Arunmozhi Bharathi Achudhan, Priya Kannan, Annapurna Gupta, Lilly M. Saleena
Biochemical Genetics.2024; 62(2): 621. CrossRef - Benchmarking bacterial taxonomic classification using nanopore metagenomics data of several mock communities
Alexander Van Uffelen, Andrés Posadas, Nancy H. C. Roosens, Kathleen Marchal, Sigrid C. J. De Keersmaecker, Kevin Vanneste
Scientific Data.2024;[Epub] CrossRef - Metagenomic approaches and opportunities in arid soil research
Muhammad Riaz Ejaz, Kareem Badr, Zahoor Ul Hassan, Roda Al-Thani, Samir Jaoua
Science of The Total Environment.2024; 953: 176173. CrossRef - DeepCheck: multitask learning aids in assessing microbial genome quality
Guo Wei, Nannan Wu, Kunyang Zhao, Sihai Yang, Long Wang, Yan Liu
Briefings in Bioinformatics.2024;[Epub] CrossRef - Integrated multi-omics analyses of microbial communities: a review of the current state and future directions
Muzaffer Arıkan, Thilo Muth
Molecular Omics.2023; 19(8): 607. CrossRef - Selenium Metabolism and Selenoproteins in Prokaryotes: A Bioinformatics Perspective
Yan Zhang, Jiao Jin, Biyan Huang, Huimin Ying, Jie He, Liang Jiang
Biomolecules.2022; 12(7): 917. CrossRef - Advances in experimental and computational methodologies for the study of microbial-surface interactions at different omics levels
Juan José González-Plaza, Cristina Furlan, Tomaž Rijavec, Aleš Lapanje, Rocío Barros, Juan Antonio Tamayo-Ramos, Maria Suarez-Diez
Frontiers in Microbiology.2022;[Epub] CrossRef - Efficient and Quality-Optimized Metagenomic Pipeline Designed for Taxonomic Classification in Routine Microbiological Clinical Tests
Sylvie Buffet-Bataillon, Guillaume Rizk, Vincent Cattoir, Mohamed Sassi, Vincent Thibault, Jennifer Del Giudice, Jean-Pierre Gangneux
Microorganisms.2022; 10(4): 711. CrossRef - Establishment and Validation of a New Analysis Strategy for the Study of Plant Endophytic Microorganisms
Feng Chen, Xianjin Wang, Guiping Qiu, Haida Liu, Yingquan Tan, Beijiu Cheng, Guomin Han
International Journal of Molecular Sciences.2022; 23(22): 14223. CrossRef - Novel technologies to characterize and engineer the microbiome in inflammatory bowel disease
Alba Boix-Amorós, Hilary Monaco, Elisa Sambataro, Jose C. Clemente
Gut Microbes.2022;[Epub] CrossRef - Current Understanding on the Genetic Basis of Key Metabolic Disorders: A Review
Kenneth Francis Rodrigues, Wilson Thau Lym Yong, Md. Safiul Alam Bhuiyan, Shafiquzzaman Siddiquee, Muhammad Dawood Shah, Balu Alagar Venmathi Maran
Biology.2022; 11(9): 1308. CrossRef - Omics-based microbiome analysis in microbial ecology: from sequences to information
Jang-Cheon Cho
Journal of Microbiology.2021; 59(3): 229. CrossRef
- A Review of Web-Based Metagenomics Platforms for Analysing Next-Generation Sequence Data
Research Support, Non-U.S. Gov't
- Identification and Characterization of a Novel Bacterial ATP-Sensitive K+ Channel
- Seung Bum Choi , Jong-Uk Kim , Hyun Joo , Churl K. Min
- J. Microbiol. 2010;48(3):325-330. Published online June 23, 2010
- DOI: https://doi.org/10.1007/s12275-010-9231-9
- 43 View
- 0 Download
- 6 Scopus
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Abstract
- Five bacterial species that are most likely to have putative prokaryotic inward rectifier K+ (Kir) channels were selected by in silico sequence homology and membrane topology analyses with respect to the number of transmembrane domains (TMs) and the presence of K+ selectivity filter and/or ATP binding sites in reference to rabbit heart inward rectifier K+ channel (Kir6.2). A dot blot assay with genomic DNAs when probed with whole rabbit Kir6.2 cDNA further supported the in silico analysis by exhibiting a stronger hybridization in species with putative Kir’s compared to one without a Kir. Among them, Chromobacterium violaceum gave rise to a putative Kir channel gene, which was PCR-cloned into the bacterial expression vector pET30b(+), and its expression was induced in Escherichia coli and confirmed by gel purification and immunoblotting. On the other hand, this putative bacterial Kir channel was functionally expressed inXenopus oocytes and its channel activity was measured electrophysiologically by using two electrode voltage clamping (TEVC). Results revealed a K+ current with characteristics similar to those of the ATP-sensitive K+ (K-ATP) channel. Collectively, cloning and functional characterization of bacterial ion channels could be greatly facilitated by combining the in silico analysis and heterologous expression in Xenopus oocytes.
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