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Viladomat Jasso M, García-Ulloa M, Zapata-Peñasco I, Eguiarte LE, Souza V. Metagenomic insight into taxonomic composition, environmental filtering and functional redundancy for shaping worldwide modern non-lithifying microbial mats. PeerJ 2024; 12:e17412. [PMID: 38827283 PMCID: PMC11144394 DOI: 10.7717/peerj.17412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 04/26/2024] [Indexed: 06/04/2024] Open
Abstract
Modern microbial mats are relictual communities mostly found in extreme environments worldwide. Despite their significance as representatives of the ancestral Earth and their important roles in biogeochemical cycling, research on microbial mats has largely been localized, focusing on site-specific descriptions and environmental change experiments. Here, we present a global comparative analysis of non-lithifying microbial mats, integrating environmental measurements with metagenomic data from 62 samples across eight sites, including two new samples from the recently discovered Archaean Domes from Cuatro Ciénegas, Mexico. Our results revealed a notable influence of environmental filtering on both taxonomic and functional compositions of microbial mats. Functional redundancy appears to confer resilience to mats, with essential metabolic pathways conserved across diverse and highly contrasting habitats. We identified six highly correlated clusters of taxa performing similar ecological functions, suggesting niche partitioning and functional specialization as key mechanisms shaping community structure. Our findings provide insights into the ecological principles governing microbial mats, and lay the foundation for future research elucidating the intricate interplay between environmental factors and microbial community dynamics.
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Affiliation(s)
- Mariette Viladomat Jasso
- Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
| | | | - Icoquih Zapata-Peñasco
- Dirección de Investigación en Transformación de Hidrocarburos, Instituto Mexicano del Petróleo, Ciudad de México, Mexico
| | - Luis E. Eguiarte
- Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
| | - Valeria Souza
- Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
- Centro de Estudios del Cuaternario de Fuego-Patagonia y Antártica (CEQUA), Punta Arenas, Chile
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Hu X, Hurtado-Gonzales OP, Adhikari BN, French-Monar RD, Malapi M, Foster JA, McFarland CD. PhytoPipe: a phytosanitary pipeline for plant pathogen detection and diagnosis using RNA-seq data. BMC Bioinformatics 2023; 24:470. [PMID: 38093207 PMCID: PMC10717670 DOI: 10.1186/s12859-023-05589-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 11/30/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Detection of exotic plant pathogens and preventing their entry and establishment are critical for the protection of agricultural systems while securing the global trading of agricultural commodities. High-throughput sequencing (HTS) has been applied successfully for plant pathogen discovery, leading to its current application in routine pathogen detection. However, the analysis of massive amounts of HTS data has become one of the major challenges for the use of HTS more broadly as a rapid diagnostics tool. Several bioinformatics pipelines have been developed to handle HTS data with a focus on plant virus and viroid detection. However, there is a need for an integrative tool that can simultaneously detect a wider range of other plant pathogens in HTS data, such as bacteria (including phytoplasmas), fungi, and oomycetes, and this tool should also be capable of generating a comprehensive report on the phytosanitary status of the diagnosed specimen. RESULTS We have developed an open-source bioinformatics pipeline called PhytoPipe (Phytosanitary Pipeline) to provide the plant pathology diagnostician community with a user-friendly tool that integrates analysis and visualization of HTS RNA-seq data. PhytoPipe includes quality control of reads, read classification, assembly-based annotation, and reference-based mapping. The final product of the analysis is a comprehensive report for easy interpretation of not only viruses and viroids but also bacteria (including phytoplasma), fungi, and oomycetes. PhytoPipe is implemented in Snakemake workflow with Python 3 and bash scripts in a Linux environment. The source code for PhytoPipe is freely available and distributed under a BSD-3 license. CONCLUSIONS PhytoPipe provides an integrative bioinformatics pipeline that can be used for the analysis of HTS RNA-seq data. PhytoPipe is easily installed on a Linux or Mac system and can be conveniently used with a Docker image, which includes all dependent packages and software related to analyses. It is publicly available on GitHub at https://github.com/healthyPlant/PhytoPipe and on Docker Hub at https://hub.docker.com/r/healthyplant/phytopipe .
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Affiliation(s)
- Xiaojun Hu
- United States Department of Agriculture (USDA), Animal and Plant Health Inspection Service (APHIS), Plant Protection and Quarantine (PPQ), Plant Germplasm Quarantine Program (PGQP), Beltsville, MD, USA.
| | - Oscar P Hurtado-Gonzales
- United States Department of Agriculture (USDA), Animal and Plant Health Inspection Service (APHIS), Plant Protection and Quarantine (PPQ), Plant Germplasm Quarantine Program (PGQP), Beltsville, MD, USA
| | - Bishwo N Adhikari
- United States Department of Agriculture (USDA), Animal and Plant Health Inspection Service (APHIS), Plant Protection and Quarantine (PPQ), Plant Germplasm Quarantine Program (PGQP), Beltsville, MD, USA
| | - Ronald D French-Monar
- United States Department of Agriculture (USDA), Animal and Plant Health Inspection Service (APHIS), Plant Protection and Quarantine (PPQ), Plant Germplasm Quarantine Program (PGQP), Beltsville, MD, USA
| | - Martha Malapi
- United States Department of Agriculture (USDA), Animal and Plant Health Inspection Service (APHIS), Plant Protection and Quarantine (PPQ), Plant Germplasm Quarantine Program (PGQP), Beltsville, MD, USA
- American Seed Trade Association (ASTA), Alexandria, VA, USA
| | - Joseph A Foster
- United States Department of Agriculture (USDA), Animal and Plant Health Inspection Service (APHIS), Plant Protection and Quarantine (PPQ), Plant Germplasm Quarantine Program (PGQP), Beltsville, MD, USA
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Navgire GS, Goel N, Sawhney G, Sharma M, Kaushik P, Mohanta YK, Mohanta TK, Al-Harrasi A. Analysis and Interpretation of metagenomics data: an approach. Biol Proced Online 2022; 24:18. [PMID: 36402995 PMCID: PMC9675974 DOI: 10.1186/s12575-022-00179-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 10/19/2022] [Indexed: 11/20/2022] Open
Abstract
Advances in next-generation sequencing technologies have accelerated the momentum of metagenomic studies, which is increasing yearly. The metagenomics field is one of the versatile applications in microbiology, where any interaction in the environment involving microorganisms can be the topic of study. Due to this versatility, the number of applications of this omics technology reached its horizons. Agriculture is a crucial sector involving crop plants and microorganisms interacting together. Hence, studying these interactions through the lenses of metagenomics would completely disclose a new meaning to crop health and development. The rhizosphere is an essential reservoir of the microbial community for agricultural soil. Hence, we focus on the R&D of metagenomic studies on the rhizosphere of crops such as rice, wheat, legumes, chickpea, and sorghum. These recent developments are impossible without the continuous advancement seen in the next-generation sequencing platforms; thus, a brief introduction and analysis of the available sequencing platforms are presented here to have a clear picture of the workflow. Concluding the topic is the discussion about different pipelines applied to analyze data produced by sequencing techniques and have a significant role in interpreting the outcome of a particular experiment. A plethora of different software and tools are incorporated in the automated pipelines or individually available to perform manual metagenomic analysis. Here we describe 8-10 advanced, efficient pipelines used for analysis that explain their respective workflows to simplify the whole analysis process.
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Affiliation(s)
- Gauri S Navgire
- Department of Microbiology, Savitribai Phule Pune University, Pune, Maharastra, 411007, India
| | - Neha Goel
- Department of Genetics and Tree Improvement, Forest Research Institute, 248006, Dehradun, India
| | - Gifty Sawhney
- Inflammation Pharmacology Division, Academy of Scientific and Innovative Research (AcSIR), CSIR-Indian Institute of Integrative Medicine, Jammu-180001, Jammu Kashmir, India
| | - Mohit Sharma
- Department of Molecular Medicine, Medical University of Warsaw and Malopolska Center of Biotechnology, Karkow, Poland
| | | | | | - Tapan Kumar Mohanta
- Natural and Medical Sciences Research Center, University of Nizwa, Nizwa, 616, Oman.
| | - Ahmed Al-Harrasi
- Natural and Medical Sciences Research Center, University of Nizwa, Nizwa, 616, Oman.
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Kishikawa T, Tomofuji Y, Inohara H, Okada Y. OMARU: a robust and multifaceted pipeline for metagenome-wide association study. NAR Genom Bioinform 2022; 4:lqac019. [PMID: 35265838 PMCID: PMC8900191 DOI: 10.1093/nargab/lqac019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/04/2022] [Accepted: 02/18/2022] [Indexed: 12/11/2022] Open
Abstract
Microbiome is an essential omics layer to elucidate disease pathophysiology. However, we face a challenge of low reproducibility in microbiome studies, partly due to a lack of standard analytical pipelines. Here, we developed OMARU (Omnibus metagenome-wide association study with robustness), a new end-to-end analysis workflow that covers a wide range of microbiome analysis from phylogenetic and functional profiling to case–control metagenome-wide association studies (MWAS). OMARU rigorously controls the statistical significance of the analysis results, including correction of hidden confounding factors and application of multiple testing comparisons. Furthermore, OMARU can evaluate pathway-level links between the metagenome and the germline genome-wide association study (i.e. MWAS-GWAS pathway interaction), as well as links between taxa and genes in the metagenome. OMARU is publicly available (https://github.com/toshi-kishikawa/OMARU), with a flexible workflow that can be customized by users. We applied OMARU to publicly available type 2 diabetes (T2D) and schizophrenia (SCZ) metagenomic data (n = 171 and 344, respectively), identifying disease biomarkers through comprehensive, multilateral, and unbiased case–control comparisons of metagenome (e.g. increased Streptococcus vestibularis in SCZ and disrupted diversity in T2D). OMARU improves accessibility and reproducibility in the microbiome research community. Robust and multifaceted results of OMARU reflect the dynamics of the microbiome authentically relevant to disease pathophysiology.
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Affiliation(s)
- Toshihiro Kishikawa
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Department of Otorhinolaryngology-Head and Neck Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Department of Head and Neck Surgery, Aichi Cancer Center Hospital, Nagoya 464-8681, Japan
| | - Yoshihiko Tomofuji
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan
| | - Hidenori Inohara
- Department of Otorhinolaryngology-Head and Neck Surgery, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa 230-0045, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita 565-0871, Japan
- Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan
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Liu J, Fan Z, Zhao W, Zhou X. Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges. Front Genet 2021; 12:655536. [PMID: 34135939 PMCID: PMC8203333 DOI: 10.3389/fgene.2021.655536] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 04/26/2021] [Indexed: 12/18/2022] Open
Abstract
The rapid development of single-cell technologies allows for dissecting cellular heterogeneity at different omics layers with an unprecedented resolution. In-dep analysis of cellular heterogeneity will boost our understanding of complex biological systems or processes, including cancer, immune system and chronic diseases, thereby providing valuable insights for clinical and translational research. In this review, we will focus on the application of machine learning methods in single-cell multi-omics data analysis. We will start with the pre-processing of single-cell RNA sequencing (scRNA-seq) data, including data imputation, cross-platform batch effect removal, and cell cycle and cell-type identification. Next, we will introduce advanced data analysis tools and methods used for copy number variance estimate, single-cell pseudo-time trajectory analysis, phylogenetic tree inference, cell-cell interaction, regulatory network inference, and integrated analysis of scRNA-seq and spatial transcriptome data. Finally, we will present the latest analyzing challenges, such as multi-omics integration and integrated analysis of scRNA-seq data.
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Affiliation(s)
- Jiajia Liu
- College of Electronic and Information Engineering, Tongji University, Shanghai, China
- School of Biomedical Informatics, The University of Texas Health Science Centre at Houston, Houston, TX, United States
| | - Zhiwei Fan
- School of Biomedical Informatics, The University of Texas Health Science Centre at Houston, Houston, TX, United States
- West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Weiling Zhao
- School of Biomedical Informatics, The University of Texas Health Science Centre at Houston, Houston, TX, United States
| | - Xiaobo Zhou
- School of Biomedical Informatics, The University of Texas Health Science Centre at Houston, Houston, TX, United States
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