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1.
Bioinformatics ; 36(8): 2628-2629, 2020 04 15.
Article in English | MEDLINE | ID: mdl-31882993

ABSTRACT

MOTIVATION: Gene lists are routinely produced from various omic studies. Enrichment analysis can link these gene lists with underlying molecular pathways and functional categories such as gene ontology (GO) and other databases. RESULTS: To complement existing tools, we developed ShinyGO based on a large annotation database derived from Ensembl and STRING-db for 59 plant, 256 animal, 115 archeal and 1678 bacterial species. ShinyGO's novel features include graphical visualization of enrichment results and gene characteristics, and application program interface access to KEGG and STRING for the retrieval of pathway diagrams and protein-protein interaction networks. ShinyGO is an intuitive, graphical web application that can help researchers gain actionable insights from gene-sets. AVAILABILITY AND IMPLEMENTATION: http://ge-lab.org/go/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Subject(s)
Databases, Genetic , Software , Animals , Computational Biology , Databases, Factual , Gene Ontology , Internet , Probability
2.
BMC Bioinformatics ; 19(1): 534, 2018 Dec 19.
Article in English | MEDLINE | ID: mdl-30567491

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.


Subject(s)
Computational Biology/methods , Gene Expression Profiling , Gene Expression Regulation , High-Throughput Nucleotide Sequencing/methods , Sequence Analysis, RNA/methods , Software , Animals , B-Lymphocytes/cytology , B-Lymphocytes/metabolism , Cell Proliferation , Cells, Cultured , Fibroblasts/cytology , Fibroblasts/metabolism , Homeodomain Proteins/antagonists & inhibitors , Humans , Lung/cytology , Lung/metabolism , Mice , RNA, Small Interfering/genetics , Transcription Factors/antagonists & inhibitors , Transcriptome , Tumor Suppressor Protein p53/genetics , Tumor Suppressor Protein p53/metabolism
3.
BMC Genomics ; 18(1): 200, 2017 02 23.
Article in English | MEDLINE | ID: mdl-28231763

ABSTRACT

BACKGROUND: Instead of testing predefined hypotheses, the goal of exploratory data analysis (EDA) is to find what data can tell us. Following this strategy, we re-analyzed a large body of genomic data to study the complex gene regulation in mouse pre-implantation development (PD). RESULTS: Starting with a single-cell RNA-seq dataset consisting of 259 mouse embryonic cells derived from zygote to blastocyst stages, we reconstructed the temporal and spatial gene expression pattern during PD. The dynamics of gene expression can be partially explained by the enrichment of transposable elements in gene promoters and the similarity of expression profiles with those of corresponding transposons. Long Terminal Repeats (LTRs) are associated with transient, strong induction of many nearby genes at the 2-4 cell stages, probably by providing binding sites for Obox and other homeobox factors. B1 and B2 SINEs (Short Interspersed Nuclear Elements) are correlated with the upregulation of thousands of nearby genes during zygotic genome activation. Such enhancer-like effects are also found for human Alu and bovine tRNA SINEs. SINEs also seem to be predictive of gene expression in embryonic stem cells (ESCs), raising the possibility that they may also be involved in regulating pluripotency. We also identified many potential transcription factors underlying PD and discussed the evolutionary necessity of transposons in enhancing genetic diversity, especially for species with longer generation time. CONCLUSIONS: Together with other recent studies, our results provide further evidence that many transposable elements may play a role in establishing the expression landscape in early embryos. It also demonstrates that exploratory bioinformatics investigation can pinpoint developmental pathways for further study, and serve as a strategy to generate novel insights from big genomic data.


Subject(s)
Computational Biology , DNA, Intergenic , Embryonic Development/genetics , Gene Expression Regulation, Developmental , Animals , Base Sequence , Cluster Analysis , Computational Biology/methods , CpG Islands , DNA Methylation , DNA Transposable Elements , Embryonic Stem Cells/metabolism , Gene Expression Profiling , Genome , Genomics/methods , Mice , Nucleotide Motifs , Organ Specificity/genetics , Promoter Regions, Genetic , Retroelements , Transcriptome , Zygote/metabolism
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