Molecular Profiling of RNA Tumors Using High-Throughput RNA Sequencing: From Raw Data to Systems Level Analyses

Abraham da Silveira, Willian; Hazard, E. Starr; Chung, Dongjun; Hardiman, Gary

Abstract

RNAseq is a powerful technique enabling global profiles of transcriptomes in healthy and diseased states. In this chapter we review pipelines to analyze the data generated by sequencing RNA, from raw data to a system level analysis. We first give an overview of workflow to generate mapped reads from FASTQ files, including quality control of FASTQ, filtering and trimming of reads, and alignment of reads to a genome. Then, we compare and contrast three popular options to determine differentially expressed (DE) transcripts (The Tuxedo Pipeline, DESeq2, and Limma/voom). Finally, we examine four tool sets to extrapolate biological meaning from the list of DE genes (Genecards, The Human Protein Atlas, GSEA, and ToppGene). We emphasize the need to ask a concise scientific question and to clearly under stand the strengths and limitations of the methods.

Citation

Abraham da Silveira, W., Hazard, E. S., Chung, D., & Hardiman, G. (2019). Molecular Profiling of RNA Tumors Using High-Throughput RNA Sequencing: From Raw Data to Systems Level Analyses. Methods in Molecular Biology, 185-204. https://doi.org/10.1007/978-1-4939-9004-7_13

• Journal Article Type: Article
• Online Publication Date: Jan 17, 2019
• Publication Date: Jan 17, 2019
• Deposit Date: Oct 25, 2024
• Journal: Methods in Molecular Biology
• Print ISSN: 1064-3745
• Pages: 185-204
• Series Title: Methods in Molecular Biology
• Series Number: 1908
• DOI: https://doi.org/10.1007/978-1-4939-9004-7_13
• PMID: 30649729