RNA Sequencing and Analysis

Abstract

RNA sequencing (RNA-Seq) uses the capabilities of high-throughput sequencing methods to provide insight into the transcriptome of a cell. Compared to previous Sanger sequencing- and microarray-based methods, RNA-Seq provides far higher coverage and greater resolution of the dynamic nature of the transcriptome. Beyond quantifying gene expression, the data generated by RNA-Seq facilitate the discovery of novel transcripts, identification of alternatively spliced genes, and detection of allele-specific expression. Recent advances in the RNA-Seq workflow, from sample preparation to library construction to data analysis, have enabled researchers to further elucidate the functional complexity of the transcription. In addition to polyadenylated messenger RNA (mRNA) transcripts, RNA-Seq can be applied to investigate different populations of RNA, including total RNA, pre-mRNA, and noncoding RNA, such as microRNA and long ncRNA. This article provides an introduction to RNA-Seq methods, including applications, experimental design, and technical challenges.

Clinical metagenomics

“…Clinical mNGS typically focuses on microbial reads; however, there is a complementary role for the analysis of gene expression in studying human host responses to infection 65 (fIG. 1C).…”

Pathogenesis of Keratoconus: The intriguing therapeutic potential of Prolactin-inducible protein

“…Anticipating the hormone-KC connection, we recently utilized a whole-transcriptome high-throughput sequencing (RNA-seq), known to provide the most extensive evaluation with a wide dynamic range and higher sensitivity (Kukurba and Montgomery, 2015). During our preliminary studies we used RNA-seq to assess gene expression profiles of HKCs and HCFs.…”

Placental transcriptome in development and pathology: expression, function, and methods of analysis

“…Recent publications of protocols for library construction and analysis are available. 141,142 A library is sequenced and the resulting data files contain nucleotide base calls (A, C, G, or T) and quality scores for each base. An advantage of RNA sequencing is that it is a direct measure of the transcripts present; as such, RNA-seq has no background expression and demonstrates a higher dynamic range of expression quantification.…”

“…Comparison of microarray and RNA-sequencing methods of transcriptome analysisLibrary construction, quality control, and sequencing can take up to 1 wk, depending on format and read depth; faster work flows are possible with automation Data analysis, alignment Not applicable to microarray files FASTQ files or similar raw read files supplied by service center require multiple processing steps that include quality filtering, removing primer sequences, aligning, and calculating expression differences141,142,158 ; critical to all sequence-based analysis is alignment to reference sequences (genome or transcriptome); alignment times have been greatly reduced from <1 to >900 million reads/h143,144 ; Galaxy online software can decrease processing time by using cloud-based parallel computing (http://galaxyproject.org/) Note: There can be institutional review board (IRB) and ethical restrictions on using cloud-based systems to analyze human patient data Raw data file provided by service center can be used with manufacturer software and other third-party programs; statistical programing software R and Bioconductor repository is popular for bioinformatics; R libraries such as oligo, affy, and limma and can process data set of few dozen arrays into gene expression table with fold changes and P values in 5 min using laptop or desktop computer 159Computational times are similar to microarrays, but different methods need to be applied (Table 3); R libraries such as DESeq and EdgeR 160-162 are used to normalize expression values and calculate differential expression; alternatively, R libraries such as voom transform count data for use by microarray methods such as limma 163 Splicing Specific array platforms can test expression of individual exons Can identify specific splice junctions as well as aberrant splicing resulting from genomic translocations Both human and other species (parasites or pathogens) could be detected in same sample if processed correctly from total RNA MicroRNAs Can quantify mRNA and microRNAs at same time if represented on array platform, but not ideal; complete analysis requires specific arrays for each Requires specific library preparation method for mRNAs and microRNAs, but could be sequenced concurrently Must use ribosomal RNA depleted total RNA; optimal performance achieved if exon sequencing used (equivalent to microarray) 130 lncRNAs, long noncoding RNAs; mRNA, messenger RNA; FFPE, formalin-fixed paraffin-embedded. Cox.…”