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Idep: an Integrated Web Application for Differential Expression and Pathway Analysis of RNA-Seq Data Steven Xijin Ge* , Eun Wo Son and Runan Yao

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Idep: an Integrated Web Application for Differential Expression and Pathway Analysis of RNA-Seq Data Steven Xijin Ge* , Eun Wo Son and Runan Yao Ge et al. BMC Bioinformatics (2018) 19:534 https://doi.org/10.1186/s12859-018-2486-6 METHODOLOGY ARTICLE Open Access iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data Steven Xijin Ge* , Eun Wo Son and Runan Yao Abstract Background: RNA-seq is widely used for transcriptomic profiling, but the bioinformatics analysis of resultant data can be time-consuming and challenging, especially for biologists. We aim to streamline the bioinformatic analyses of gene-level data by developing a user-friendly, interactive web application for exploratory data analysis, differential expression, and pathway analysis. Results: iDEP (integrated Differential Expression and Pathway analysis) seamlessly connects 63 R/Bioconductor packages, 2 web services, and comprehensive annotation and pathway databases for 220 plant and animal species. The workflow can be reproduced by downloading customized R code and related pathway files. As an example, we analyzed an RNA-Seq dataset of lung fibroblasts with Hoxa1 knockdown and revealed the possible roles of SP1 and E2F1 and their target genes, including microRNAs, in blocking G1/S transition. In another example, our analysis shows that in mouse B cells without functional p53, ionizing radiation activates the MYC pathway and its downstream genes involved in cell proliferation, ribosome biogenesis, and non-coding RNA metabolism. In wildtype B cells, radiation induces p53-mediated apoptosis and DNA repair while suppressing the target genes of MYC and E2F1, and leads to growth and cell cycle arrest. iDEP helps unveil the multifaceted functions of p53 and the possible involvement of several microRNAs such as miR-92a, miR-504, and miR-30a. In both examples, we validated known molecular pathways and generated novel, testable hypotheses. Conclusions: Combining comprehensive analytic functionalities with massive annotation databases, iDEP (http://ge-lab.org/idep/) enables biologists to easily translate transcriptomic and proteomic data into actionable insights. Keywords: RNA-seq, Bioinformatics, Web application, Differential gene expression, pathway analysis Background scattered annotation databases with diverse types of gene RNA sequencing (RNA-Seq) [1] has become a routine IDs. To mitigate these issues, we aim to develop an ap- technique for genome-wide expression analysis. At plication that can greatly reduce the time and effort re- increasingly reduced cost, library construction and se- quired for researchers to analyze RNA-Seq data. quencing can often be carried out following standard RNA-Seq data analysis often starts with quality control, protocols. For many researchers, especially those with- pre-processing, mapping and summarizing of raw sequen- out bioinformatics experience, the bottleneck to fully cing reads. We assume these steps were completed, using leverage the power of the technique is how to translate either the traditional Tuxedo Suite [2, 3] or alternatives expression profiles into actionable insights. A typical such as the faster, alignment-free quantification methods analytic workflow involves many steps, each requiring [4, 5]. These tools can be used in stand-alone mood or different tools. It can be time-consuming to learn, tune through platforms like GenePattern [6], Galaxy [7], and and connect these tools correctly. Another hurdle is the CyVerse [8]. After read mapping, we often obtain a matrix of gene-level read counts or normalized expression levels * Correspondence: [email protected] Department of Mathematics and Statistics, South Dakota State University, (Fragments Per Kilobase Million, or FPKM). For such Box 2225, Brookings, SD 57007, USA tabular data, like DNA microarray data, R is a powerful © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Ge et al. BMC Bioinformatics (2018) 19:534 Page 2 of 24 tool for visualization and statistical analysis. In addition, EDA and pathway analysis, (4) access to web services many dedicated R and Bioconductor [9] packages have such as KEGG [21] and STRING-db [24] via application been developed to identify differentially expressed genes programming interface (API), and (5) improved reprodu- (DEGs) and altered pathways. Some of the packages, cibility by generating R scripts for stand-alone analysis. such as DESeq2 [10], are developed specifically for the We used iDEP to analyze two example datasets and gen- statistical modeling of read counts, and are widely used. erate all the figures and tables in this paper except Table 1 But these packages can be time-consuming, or even out and Fig. 1. We first extensively analyzed a simple RNA-Seq of reach for researchers without coding experience. dataset involving small interfering RNA (siRNA)-mediated Several web applications have been developed to Hoxa1 knockdown in human lung fibroblasts [3]. We iden- analyze summarized expression data (Table 1). START tified the down-regulation of cell-cycle genes, in agreement App (Shiny Transcriptome Analysis Resource Tool) is a with previous studies. Our analyses also reveal the possible Shiny app that performs hierarchical clustering, principal roles of E2F1 and its target genes, including microRNAs, in component analysis (PCA), gene-level boxplots, and dif- blocking G1/S transition, and the upregulation of genes ferential gene expression [11]. Another similar tool, De- related to cytokines, lysosome, and neuronal parts. The sec- gust [12] can perform differential expression analysis ond dataset was derived from an experiment with a factor- using EdgeR [13] or limma-voom [14] and interactively ial design to study the effect of ionizing radiation (IR) on plot the results. Other tools include Sleuth [15] and Shi- mouse B cells with and without functional p53 [25]. In nyNGS [16]. Non-Shiny applications were also devel- addition to correctly identifying p53 pathway and the en- oped to take advantage of the R code base. This includes richment of p53 target genes, we also found the DEIVA [17] and VisRseq [18]. Beyond differential ex- p53-independent effects, including the regulation of ribo- pression, several tools incorporate some capacity of some biogenesis and non-coding RNA metabolism, and ac- pathway analysis. For quantified expression data, ASAP tivation of c-MYC. These examples show that users can (Automated Single-cell Analysis Pipeline) [19] can carry gain insights into both molecular pathways and gene regu- out normalization, filtering, clustering, and enrichment latory mechanisms. analysis based on Gene Ontology (GO) [20] and KEGG [21] databases. With EXPath Tool [22], users can per- Results form pathway search, GO enrichment and co-expression We developed an easy-to-use web application for in-depth analysis. Several other Shiny-based tools, such as IRIS analysis of gene expression data. iDEP (integrated Differ- [23], are also being developed. The development of these ential Expression and Pathway analysis) encompasses tools in the last few years facilitated the interpretation of many useful R and Bioconductor packages, vast annota- RNA-Seq data. tion databases, and related web services. The input is a In this study, we seek to develop a web application gene-level expression matrix obtained from RNA-seq, with substantially enhanced functionality with (1) auto- DNA microarray, or other platforms. Main functionalities matic gene ID conversion with broad coverage, (2) com- include (1) pre-processing, (2) exploratory data analysis prehensive gene annotation and pathway database for (EDA), (3) differential expression, and (4) pathway analysis both plant and animals, (3) several methods for in-depth and visualization. Table 1 Comparison of applications for analyzing RNA-Seq START App Degust ShinyNGS DEIVA VisRseq ASAP EXPath Tool IRIS iDEP Heatmap O O O O O O PCA/MDS O O O O O O O Clustering of genes O O O O O Volcano/MA Plot O O O O O O O Single gene plot O O O O Diff. gene expression O O O O O O O Co-expression OO Stand-alone R code O O O O Pathway analysis KEGG KEGG human& mouse O O Gene ID conversion O API to KEGG, STRING-db O Complex models OO Note: “O” indicates the functionality is included in a tool. Ge et al. BMC Bioinformatics (2018) 19:534 Page 3 of 24 Fig. 1 iDEP workflow and functional modules Leveraging many existing R packages (see Fig. 1) and Pre-processing and EDA the power of the Shiny framework, we developed iDEP After uploading the read count data, iDEP correctly recog- to enable users to easily formulate new hypotheses from nized Homo sapiens as the likely species, based on the transcriptomic datasets. We also batch downloaded a number of matched genes IDs. After ID conversion and massive amount of gene annotation information for 220 the default filter (0.5 counts per million, or CPM, in at species (See Additional file 1: Table S1) from Ensembl least one sample), 13,819 genes left. A bar plot of total [26, 27] Ensembl Plants [28], and Ensembl Metazoa. In read counts per library is generated (Fig. 2a), showing addition, comprehensive pathway databases for human some small variation in library sizes. We chose the regu- (Table 2), mouse [29], and arabidopsis [30] were also larized log (rlog) transformation implemented in the compiled from many sources to support in-depth path- DESeq2 package, as it effectively reduces mean-dependent way analyses. variance (Additional file 3: Figure S2). Distribution of the Our goal was to develop an intuitive, graphical, and transformed data is shown in Fig. 2b-c. Variation among robust tool so that researchers without bioinformatics replicates is small (Fig. 2d).
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