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      ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization

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          Abstract

          Summary

          Microbiome research is now moving beyond the compositional analysis of microbial taxa in a sample. Increasing evidence from large human microbiome studies suggests that functional consequences of changes in the intestinal microbiome may provide more power for studying their impact on inflammation and immune responses. Although 16S rRNA analysis is one of the most popular and a cost-effective method to profile the microbial compositions, marker-gene sequencing cannot provide direct information about the functional genes that are present in the genomes of community members. Bioinformatic tools have been developed to predict microbiome function with 16S rRNA gene data. Among them, PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) has become one of the most popular functional profile prediction tools, which generates community-wide pathway abundances. However, no state-of-art inference tools are available to test the differences in pathway abundances between comparison groups. We have developed ggpicrust2, an R package, for analyzing functional profiles derived from 16S rRNA sequencing. This powerful tool enables researchers to conduct extensive differential abundance analyses and generate visually appealing visualizations that effectively highlight functional signals. With ggpicrust2, users can obtain publishable results and gain deeper insights into the functional composition of their microbial communities.

          Availability and implementation

          The package is open-source under the MIT and file license and is available at CRAN and https://github.com/cafferychen777/ggpicrust2. Its shiny web is available at https://a95dps-caffery-chen.shinyapps.io/ggpicrust2_shiny/.

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          Most cited references29

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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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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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                Role: Associate Editor
                Journal
                Bioinformatics
                Bioinformatics
                bioinformatics
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                August 2023
                01 August 2023
                01 August 2023
                : 39
                : 8
                : btad470
                Affiliations
                Department of Biostatistics, Southern Medical University , Guangzhou 510515, China
                Department of Biostatistics, Southern Medical University , Guangzhou 510515, China
                Department of Mathematical Sciences, University of Cincinnati , Cincinnati, OH 45221, United States
                Department of Pathology, School of Medicine, Case Western Reserve University , Cleveland, OH 44106, United States
                Department of Pathology, School of Medicine, Case Western Reserve University , Cleveland, OH 44106, United States
                Case Digestive Health Research Institute, Case Western Reserve University , Cleveland, OH 44016, United States
                Department of Population and Quantitative Health Sciences, Case Western Reserve University , Cleveland, OH 44106, United States
                Case Comprehensive Cancer Center , Cleveland, OH 44106, United States
                Author notes
                Corresponding author. Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, United States. E-mail: lxz716@ 123456case.edu (L.Z.)
                Author information
                https://orcid.org/0009-0009-9151-2901
                https://orcid.org/0000-0002-1571-1548
                https://orcid.org/0000-0001-5986-2260
                Article
                btad470
                10.1093/bioinformatics/btad470
                10425198
                37527009
                a883d169-7180-4004-b520-a369a750ef5f
                © The Author(s) 2023. Published by Oxford University Press.

                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
                : 06 April 2023
                : 13 July 2023
                : 24 July 2023
                : 30 July 2023
                : 14 August 2023
                Page count
                Pages: 5
                Funding
                Funded by: National Institutes of Health gr;
                Award ID: 5P30AG072959-02
                Award ID: 3R01DK042191-30S1
                Categories
                Applications Note
                Genome Analysis
                AcademicSubjects/SCI01060

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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