![]() ![]() ![]() A survey of best practices for RNA-seq data analysis. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Principles and challenges of genomewide DNA methylation analysis. Comprehensive analysis of DNA methylation data with RnBeads. Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR. Computational approaches to identify functional genetic variants in cancer genomes. Guidelines for investigating causality of sequence variants in human disease. Quantitative proteomics: challenges and opportunities in basic and applied research. Visualization and differential analysis of protein expression data using R. limma powers differential expression analyses for RNA-sequencing and microarray studies. Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. Merico, D., Isserlin, R., Stueker, O., Emili, A. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. g:Profiler-a web server for functional interpretation of gene lists (2016 update). Pathway and network analysis of more than 2,500 whole cancer genomes. Impact of outdated gene annotations on pathway enrichment analysis. Wadi, L., Meyer, M., Weiser, J., Stein, L. Pathway and network analysis of cancer genomes. Integration of biological networks and gene expression data using Cytoscape. Integrated genomic analyses of ovarian carcinoma. The Cancer Genome Atlas Research Network. Prognostically relevant gene signatures of high-grade serous ovarian carcinoma. Mutational landscape and significance across 12 major cancer types. Convergence of genes and cellular pathways dysregulated in autism spectrum disorders. Functional impact of global rare copy number variation in autism spectrum disorders. Epigenomic alterations define lethal CIMP-positive ependymomas of infancy. Big data: astronomical or genomical? PLoS Biol. Initial impact of the sequencing of the human genome. The complete protocol can be performed in ~4.5 h and is designed for use by biologists with no prior bioinformatics training. The protocol describes innovative visualization techniques, provides comprehensive background and troubleshooting guidelines, and uses freely available and frequently updated software, including g:Profiler, Gene Set Enrichment Analysis (GSEA), Cytoscape and EnrichmentMap. We describe how to use this protocol with published examples of differentially expressed genes and mutated cancer genes however, the principles can be applied to diverse types of omics data. The protocol comprises three major steps: definition of a gene list from omics data, determination of statistically enriched pathways, and visualization and interpretation of the results. We explain the procedures of pathway enrichment analysis and present a practical step-by-step guide to help interpret gene lists resulting from RNA-seq and genome-sequencing experiments. This method identifies biological pathways that are enriched in a gene list more than would be expected by chance. Pathway enrichment analysis helps researchers gain mechanistic insight into gene lists generated from genome-scale (omics) experiments. ![]()
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