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      Sangerbox: A comprehensive, interaction‐friendly clinical bioinformatics analysis platform

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          Abstract

          In recent decades, with the continuous development of high‐throughput sequencing technology, data volume in medical research has increased, at the same time, almost all clinical researchers have their own independent omics data, which provided a better condition for data mining and a deeper understanding of gene functions. However, for these large amounts of data, many common and cutting‐edge effective bioinformatics research methods still cannot be widely used. This has encouraged the establishment of many analytical platforms, a portion of databases or platforms were designed to solve the special analysis needs of users, for instance, MG RAST, IMG/M, Qiita, BIGSdb, and TRAPR were developed for specific omics research, and some databases or servers provide solutions for special problems solutions. Metascape was designed to only provide functional annotations of genes as well as function enrichment analysis; BioNumerics and RidomSeqSphere+ perform multilocus sequence typing; CARD provides only antimicrobial resistance annotations. Additionally, some web services are outdated, and inefficient interaction often fails to meet the needs of researchers, such as our previous versions of the platform. Therefore, the demand to complete massive data processing tasks urgently requires a comprehensive bioinformatics analysis platform. Hence, we have developed a website platform, Sangerbox 3.0 ( http://vip.sangerbox.com/), a web‐based tool platform. On a user‐friendly interface that also supports differential analysis, the platform provides interactive customizable analysis tools, including various kinds of correlation analyses, pathway enrichment analysis, weighted correlation network analysis, and other common tools and functions, users only need to upload their own corresponding data into Sangerbox 3.0, select required parameters, submit, and wait for the results after the task has been completed. We have also established a new interactive plotting system that allows users to adjust the parameters in the image; moreover, optimized plotting performance enables users to adjust large‐capacity vector maps on the web site. At the same time, we have integrated GEO, TCGA, ICGC, and other databases and processed data in batches, greatly reducing the difficulty to obtain data and improving the efficiency of bioimformatics study for users. Finally, we also provide users with rich sources of bioinformatics analysis courses, offering a platform for researchers to share and exchange knowledge.

          Abstract

          Sangerbox with a user‐friendly interface supports differential analysis, correlation analyses, pathway enrichment analysis, weighted correlation network analysis, and so on. A new interactive plotting system that allows users to adjust the parameters in the image. It has organized GEO, TCGA, ICGC, and other databases; a rapid batch processing reduces the difficulty in data acquirement, greatly improving the efficiency.

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          Most cited references24

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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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
                lishuang@cqu.edu.cn
                song761231@sina.com
                Journal
                Imeta
                Imeta
                10.1002/(ISSN)2770-596X
                IMT2
                iMeta
                John Wiley and Sons Inc. (Hoboken )
                2770-5986
                2770-596X
                08 July 2022
                September 2022
                : 1
                : 3 ( doiID: 10.1002/imt2.v1.3 )
                : e36
                Affiliations
                [ 1 ] Bioinformatics R&D Department Hangzhou Mugu Technology Co., Ltd Hangzhou China
                [ 2 ] Department of Cardiovascular Medicine Shanghai University of Medicine & Health Sciences Affiliated Zhoupu Hospital Shanghai China
                [ 3 ] Cardiovascular Center The Fourth Affiliated Hospital of Harbin Medical University Harbin Heilongjiang China
                [ 4 ] College of Basic Medical Harbin Medical University Harbin Heilongjiang China
                [ 5 ] Renal Division, Department of Internal Medicine Xinhua Hospital Affiliated to Shanghai Jiao Tong University of Medicine Shanghai China
                [ 6 ] Oncology Research Center Beidahuang Industry Group General Hospital Harbin Heilongjiang China
                Author notes
                [*] [* ] Correspondence Xiang Song, Department of Cardiovascular Medicine, Shanghai University of Medicine & Health Sciences Affiliated Zhoupu Hospital, Shanghai, China.

                Email: song761231@ 123456sina.com

                Shuang Li, Hangzhou Mugu Technology Co., Ltd, Hangzhou, China.

                Email: lishuang@ 123456cqu.edu.cn

                Author information
                http://orcid.org/0000-0002-1575-2307
                Article
                IMT236
                10.1002/imt2.36
                10989974
                38868713
                b62a90c4-2d48-46ac-8fbf-507d51fbf5a1
                © 2022 The Authors. iMeta published by John Wiley & Sons Australia, Ltd on behalf of iMeta Science.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 15 May 2022
                : 30 April 2022
                : 02 June 2022
                Page count
                Figures: 4, Tables: 0, Pages: 6, Words: 3136
                Funding
                Funded by: Shanghai Pudong New District Zhoupu Hospital , doi 10.13039/100016070;
                Award ID: ZP‐XK‐2021B‐1
                Funded by: Health and Family Planning Committee of Pudong New Area , doi 10.13039/501100011488;
                Award ID: PWRI2021‐08
                Categories
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                2.0
                September 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.4.0 mode:remove_FC converted:25.03.2024

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