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      OmicsSuite: a customized and pipelined suite for analysis and visualization of multi-omics big data

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          Abstract

          With the advancements in high-throughput sequencing technologies such as Illumina, PacBio, and 10X Genomics platforms, and gas/liquid chromatography-mass spectrometry, large volumes of biological data in multiple formats can now be obtained through multi-omics analysis. Bioinformatics is constantly evolving and seeking breakthroughs to solve multi-omics problems; however, it is challenging for most experimental biologists to analyse data using command-line interfaces, coding, and scripting. Based on experience with multi-omics, we have developed OmicsSuite, a desktop suite that comprehensively integrates statistics and multi-omics analysis and visualization. The suite has 175 sub-applications in 12 categories, including Sequence, Statistics, Algorithm, Genomics, Transcriptomics, Enrichment, Proteomics, Metabolomics, Clinical, Microorganism, Single Cell, and Table Operation. We created the user interface with Sequence View, Table View, and intelligent components based on JavaFX and the popular Shiny framework. The multi-omics analysis functions were developed based on BioJava and 300+ packages provided by the R CRAN and Bioconductor communities, and it encompasses over 3000 adjustable parameter interfaces. OmicsSuite can directly read multi-omics raw data in FastA, FastQ, Mutation Annotation Format, mzML, Matrix, and HDF5 formats, and the programs emphasize data transfer directions and pipeline analysis functions. OmicsSuite can produce pre-publication images and tables, allowing users to focus on biological aspects. OmicsSuite offers multi-omics step-by-step workflows that can be easily applied to horticultural plant breeding and molecular mechanism studies in plants. It enables researchers to freely explore the molecular information contained in multi-omics big data (Source: https://github.com/OmicsSuite/, Website: https://omicssuite.github.io, v1.3.9).

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          Most cited references59

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              WGCNA: an R package for weighted correlation network analysis

              Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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                Author and article information

                Contributors
                Journal
                Hortic Res
                Hortic Res
                hr
                Horticulture Research
                Oxford University Press
                2662-6810
                2052-7276
                November 2023
                28 September 2023
                28 September 2023
                : 10
                : 11
                : uhad195
                Affiliations
                State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University , Xiamen 361102, Fujian, China
                Hospital of Stomatology, Guanghua School of Stomatology, Guangdong Provincial Key Laboratory of Stomatology, Sun Yat-Sen University , Guangzhou 510055, Guangdong, China
                College of Fisheries, Guangdong Ocean University , Zhanjiang 524088, Guangdong, China
                State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University , Xiamen 361102, Fujian, China
                State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University , Xiamen 361102, Fujian, China
                State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University , Xiamen 361102, Fujian, China
                State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University , Xiamen 361102, Fujian, China
                Fujian Institute for Sustainable Oceans, Xiamen University , Xiamen 361102, Fujian, China
                Author notes

                Ben-Ben Miao, Wei Dong contributed equally to this work.

                Article
                uhad195
                10.1093/hr/uhad195
                10673651
                7a1f0965-925e-4aca-b3d0-41d0711c91cc
                © The Author(s) 2023. Published by Oxford University Press on behalf of Nanjing Agricultural University.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 August 2023
                : 20 September 2023
                : 01 November 2023
                Page count
                Pages: 14
                Funding
                Funded by: Fundamental Research Funds for the Central Universities, DOI 10.13039/501100012226;
                Award ID: 2072022
                Funded by: Earmarked Fund for CARS;
                Award ID: CARS-49
                Funded by: Hainan Province Science and Technology Special Fund;
                Award ID: ZDYF2022XDNY234
                Funded by: National Natural Science Foundation of China, DOI 10.13039/501100001809;
                Award ID: 32102775
                Categories
                AcademicSubjects/SCI01210
                AcademicSubjects/SCI01140
                Article

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