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Helmy M, Selvarajoo K. Application of GeneCloudOmics: Transcriptomic Data Analytics for Synthetic Biology. Methods Mol Biol 2023; 2553:221-263. [PMID: 36227547 DOI: 10.1007/978-1-0716-2617-7_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Research in synthetic biology and metabolic engineering require a deep understanding on the function and regulation of complex pathway genes. This can be achieved through gene expression profiling which quantifies the transcriptome-wide expression under any condition, such as a cell development stage, mutant, disease, or treatment with a drug. The expression profiling is usually done using high-throughput techniques such as RNA sequencing (RNA-Seq) or microarray. Although both methods are based on different technical approaches, they provide quantitative measures of the expression levels of thousands of genes. The expression levels of the genes are compared under different conditions to identify the differentially expressed genes (DEGs), the genes with different expression levels under different conditions. DEGs, usually involving thousands in number, are then investigated using bioinformatics and data analytic tools to infer and compare their functional roles between conditions. Dealing with such large datasets, therefore, requires intensive data processing and analyses to ensure its quality and produce results that are statistically sound. Thus, there is a need for deep statistical and bioinformatics knowledge to deal with high-throughput gene expression data. This represents a barrier for wet biologists with limited computational, programming, and data analytic skills that prevent them from getting the full potential of the data. In this chapter, we present a step-by-step protocol to perform transcriptome analysis using GeneCloudOmics, a cloud-based web server that provides an end-to-end platform for high-throughput gene expression analysis.
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Affiliation(s)
- Mohamed Helmy
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
- Department of Computer Science, Lakehead University, Thunder Bay, ON, Canada.
| | - Kumar Selvarajoo
- Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Singapore Institute of Food and Biotechnology Innovation, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
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Gut Microbiome Studies in Livestock: Achievements, Challenges, and Perspectives. Animals (Basel) 2022; 12:ani12233375. [PMID: 36496896 PMCID: PMC9736591 DOI: 10.3390/ani12233375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/16/2022] [Accepted: 11/28/2022] [Indexed: 12/03/2022] Open
Abstract
The variety and makeup of the gut microbiome are frequently regarded as the primary determinants of health and production performances in domestic animals. High-throughput DNA/RNA sequencing techniques (NGS) have recently gained popularity and permitted previously unheard-of advancements in the study of gut microbiota, particularly for determining the taxonomic composition of such complex communities. Here, we summarize the existing body of knowledge on livestock gut microbiome, discuss the state-of-the-art in sequencing techniques, and offer predictions for next research. We found that the enormous volumes of available data are biased toward a small number of globally distributed and carefully chosen varieties, while local breeds (or populations) are frequently overlooked despite their demonstrated resistance to harsh environmental circumstances. Furthermore, the bulk of this research has mostly focused on bacteria, whereas other microbial components such as protists, fungi, and viruses have received far less attention. The majority of these data were gathered utilizing traditional metabarcoding techniques that taxonomically identify the gut microbiota by analyzing small portions of their genome (less than 1000 base pairs). However, to extend the coverage of microbial genomes for a more precise and thorough characterization of microbial communities, a variety of increasingly practical and economical shotgun techniques are currently available.
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Saravanakumar K, Santosh SS, Ahamed MA, Sathiyaseelan A, Sultan G, Irfan N, Ali DM, Wang MH. Bioinformatics strategies for studying the molecular mechanisms of fungal extracellular vesicles with a focus on infection and immune responses. Brief Bioinform 2022; 23:6632620. [PMID: 35794708 DOI: 10.1093/bib/bbac250] [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: 04/07/2022] [Revised: 05/16/2022] [Accepted: 05/28/2022] [Indexed: 01/19/2023] Open
Abstract
Fungal extracellular vesicles (EVs) are released during pathogenesis and are found to be an opportunistic infection in most cases. EVs are immunocompetent with their host and have paved the way for new biomedical approaches to drug delivery and the treatment of complex diseases including cancer. With computing and processing advancements, the rise of bioinformatics tools for the evaluation of various parameters involved in fungal EVs has blossomed. In this review, we have complied and explored the bioinformatics tools to analyze the host-pathogen interaction, toxicity, omics and pathogenesis with an array of specific tools that have depicted the ability of EVs as vector/carrier for therapeutic agents and as a potential theme for immunotherapy. We have also discussed the generation and pathways involved in the production, transport, pathogenic action and immunological interactions of EVs in the host system. The incorporation of network pharmacology approaches has been discussed regarding fungal pathogens and their significance in drug discovery. To represent the overview, we have presented and demonstrated an in silico study model to portray the human Cryptococcal interactions.
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Affiliation(s)
- Kandasamy Saravanakumar
- Department of Bio-Health convergence, Kangwon National University, Chuncheon 200-701, Republic of Korea
| | | | - MohamedAli Afaan Ahamed
- School of Life Sciences, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu 600048, India
| | - Anbazhagan Sathiyaseelan
- Department of Bio-Health convergence, Kangwon National University, Chuncheon 200-701, Republic of Korea
| | - Ghazala Sultan
- Department of Computer Science, Aligarh Muslim University, Aligarh, Uttar Pradesh, 202002, India
| | - Navabshan Irfan
- Crescent School of Pharmacy, B.S Abdur Rahman Crescent Institute of Science and Technology, Chennai, 600048, India
| | - Davoodbasha Mubarak Ali
- School of Life Sciences, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu 600048, India
| | - Myeong-Hyeon Wang
- Department of Bio-Health convergence, Kangwon National University, Chuncheon 200-701, Republic of Korea
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Srivastava N, Sarethy IP, Jeevanandam J, Danquah M. Emerging strategies for microbial screening of novel chemotherapeutics. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2022.132419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Helmy M, Agrawal R, Ali J, Soudy M, Bui TT, Selvarajoo K. GeneCloudOmics: A Data Analytic Cloud Platform for High-Throughput Gene Expression Analysis. FRONTIERS IN BIOINFORMATICS 2021; 1:693836. [PMID: 36303746 PMCID: PMC9581002 DOI: 10.3389/fbinf.2021.693836] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 10/14/2021] [Indexed: 11/18/2022] Open
Abstract
Gene expression profiling techniques, such as DNA microarray and RNA-Sequencing, have provided significant impact on our understanding of biological systems. They contribute to almost all aspects of biomedical research, including studying developmental biology, host-parasite relationships, disease progression and drug effects. However, the high-throughput data generations present challenges for many wet experimentalists to analyze and take full advantage of such rich and complex data. Here we present GeneCloudOmics, an easy-to-use web server for high-throughput gene expression analysis that extends the functionality of our previous ABioTrans with several new tools, including protein datasets analysis, and a web interface. GeneCloudOmics allows both microarray and RNA-Seq data analysis with a comprehensive range of data analytics tools in one package that no other current standalone software or web-based tool can do. In total, GeneCloudOmics provides the user access to 23 different data analytical and bioinformatics tasks including reads normalization, scatter plots, linear/non-linear correlations, PCA, clustering (hierarchical, k-means, t-SNE, SOM), differential expression analyses, pathway enrichments, evolutionary analyses, pathological analyses, and protein-protein interaction (PPI) identifications. Furthermore, GeneCloudOmics allows the direct import of gene expression data from the NCBI Gene Expression Omnibus database. The user can perform all tasks rapidly through an intuitive graphical user interface that overcomes the hassle of coding, installing tools/packages/libraries and dealing with operating systems compatibility and version issues, complications that make data analysis tasks challenging for biologists. Thus, GeneCloudOmics is a one-stop open-source tool for gene expression data analysis and visualization. It is freely available at http://combio-sifbi.org/GeneCloudOmics.
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Affiliation(s)
- Mohamed Helmy
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Department of Computer Science, Lakehead University, Thunder Bay, ON, Canada
| | - Rahul Agrawal
- Department of Geology and Geophysics, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, India
| | - Javed Ali
- Department of Geology and Geophysics, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, India
| | - Mohamed Soudy
- Proteomics and Metabolomics Unit, Children Cancer Hospital (CCHE-57357), Cairo, Egypt
| | - Thuy Tien Bui
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Kumar Selvarajoo
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Synthetic Biology for Clinical and Technological Innovation (SynCTI), National University of Singapore (NUS), Singapore, Singapore
- *Correspondence: Kumar Selvarajoo,
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Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, Fu X, Liu S, Bo X, Yu G. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (N Y) 2021; 2:100141. [PMID: 34557778 PMCID: PMC8454663 DOI: 10.1016/j.xinn.2021.100141] [Citation(s) in RCA: 2474] [Impact Index Per Article: 824.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 06/29/2021] [Indexed: 12/15/2022] Open
Abstract
Functional enrichment analysis is pivotal for interpreting high-throughput omics data in life science. It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible. To meet these requirements, we present here an updated version of our popular Bioconductor package, clusterProfiler 4.0. This package has been enhanced considerably compared with its original version published 9 years ago. The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases. It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization. Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists. We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms. clusterProfiler supports exploring functional characteristics of both coding and non-coding genomics data for thousands of species with up-to-date gene annotation It provides a universal interface for gene functional annotation from a variety of sources and thus can be applied in diverse scenarios It provides a tidy interface to access, manipulate, and visualize enrichment results to help users achieve efficient data interpretation Datasets obtained from multiple treatments and time points can be analyzed and compared in a single run, easily revealing functional consensus and differences among distinct conditions
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Affiliation(s)
- Tianzhi Wu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Erqiang Hu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Shuangbin Xu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Meijun Chen
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Pingfan Guo
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Zehan Dai
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Tingze Feng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Lang Zhou
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Wenli Tang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Li Zhan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Xiaocong Fu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Shanshan Liu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Xiaochen Bo
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Guangchuang Yu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.,Guangdong Provincial Key Laboratory of Proteomics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.,Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China
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