Systematic evaluation of RNA-Seq preparation protocol performance

Abstract

Background RNA-Seq is currently the most widely used tool to analyze whole-transcriptome profiles. There are numerous commercial kits available to facilitate preparing RNA-Seq libraries; however, it is still not clear how some of these kits perform in terms of: 1) ribosomal RNA removal; 2) read coverage or recovery of exonic vs. intronic sequences; 3) identification of differentially expressed genes (DEGs); and 4) detection of long non-coding RNA (lncRNA). In RNA-Seq analysis, understanding the strengths and limitations of commonly used RNA-Seq library preparation protocols is important, as this technology remains costly and time-consuming. Results In this study, we present a comprehensive evaluation of four RNA-Seq kits. We used three standard input protocols: Illumina TruSeq Stranded Total RNA and mRNA kits, a modified NuGEN Ovation v2 kit, and the TaKaRa SMARTer Ultra Low RNA Kit v3. Our evaluation of these kits included quality control measures such as overall reproducibility, 5′ and 3′ end-bias, and the identification of DEGs, lncRNAs, and alternatively spliced transcripts. Overall, we found that the two Illumina kits were most similar in terms of recovering DEGs, and the Illumina, modified NuGEN, and TaKaRa kits allowed identification of a similar set of DEGs. However, we also discovered that the Illumina, NuGEN and TaKaRa kits each enriched for different sets of genes. Conclusions At the manufacturers’ recommended input RNA levels, all the RNA-Seq library preparation protocols evaluated were suitable for distinguishing between experimental groups, and the TruSeq Stranded mRNA kit was universally applicable to studies focusing on protein-coding gene profiles. The TruSeq protocols tended to capture genes with higher expression and GC content, whereas the modified NuGEN protocol tended to capture longer genes. The SMARTer Ultra Low RNA Kit may be a good choice at the low RNA input level, although it was inferior to the TruSeq mRNA kit at standard input level in terms of rRNA removal, exonic mapping rates and recovered DEGs. Therefore, the choice of RNA-Seq library preparation kit can profoundly affect data outcomes. Consequently, it is a pivotal parameter to consider when designing an RNA-Seq experiment. Electronic supplementary material The online version of this article (10.1186/s12864-019-5953-1) contains supplementary material, which is available to authorized users.

Age Distribution of Multiple Functionally Relevant Subsets of CD4+ T Cells in Human Blood Using a Standardized and Validated 14-Color EuroFlow Immune Monitoring Tube

“…Total RNA was extracted from each purified CD4+ T-cell population using the NucleoSpin R RNA XS kit (Macherey-Nagel, Düren, Germany), transcribed into cDNA and amplified using a quantitative real-time polymerase chain reaction (qPCR). Selection of conventional qPCR (vs. RNA-seq) was based on the following criteria: (i) most RNA-seq protocols recommend an input of 1 µg of RNA (40), therefore requiring the collection of significantly higher numbers of cells, which could not be easily obtained in our settings for each of the cell populations investigated (median of 15,000 cells per sorted CD4+ T-cell subset; range: 3,000-50,000), considering that the TABLE 1 | Antibody combinations tested during the "design-test-evaluation-redesign" cycles and the resulting combinations of markers sequentially tested from the first version (version 1) to the final version (version 3) of the complete EuroFlow immune monitoring (IMM) TCD4 tube.…”

De novo profiling of long non-coding RNAs involved in MC-LR–induced liver injury in whitefish: discovery and perspectives

“…The lack of standardized methodologies for discovery of lncRNAs poses a challenge for the analysis and interpretation of RNA sequencing data. The outcome of pipelines designed to discover lncRNAs in RNA-Seq data strongly depends on factors which precede in silico analysis, such as RNA isolation or the method of preparing sequencing libraries [46]. Therefore, methods designed for enriching polyadenylated protein-coding mRNAs may not be optimal for recovering lncRNAs that are present at low levels.…”

“…Because the main aim of our RNA-Seq experiment was to analyze the pro les of protein-coding genes, Illumina's TruSeq Stranded mRNA protocol was chosen (Brzuzan et al, in preparation). However, this protocol can also be used for lncRNA discovery [46]. In fact, the majority of biologically functional lncRNAs reported to date are polyadenylated [48], thus approaches based on enriching polyadenylated transcripts to discover functional lncRNAs are common in pipelines based on annotated genomes, as well as those based on de novo assembled transcriptomes.…”

Quality control assessment of the RNA-Seq data generated from liver and pituitary transcriptome of Hereford bulls using StrandNGS software

“…The QC of RNA-seq data before and after alignment is an essential prerequisite in any NGS experiments because of experimental biases in nucleotide composition, library preparation issues, PCR biases -all of which influence the RNA-seq analysis [2][3][4][5][6].Therefore, before any RNA-seq analysis or sequence alignment is done, a read-level analysis of RNA-seq data must be performed in all NGS experiments as the first essential step to start the bioinformatics analysis. In literatures, several bioinformatics tools that are publically available to conduct the QC analysis on raw FastQ files viz., Musket [18], HiTEC [19] and SHREC [20] and FastQC package developed by the Babraham Institute bioinformatics group (https:// www.bioinformatics.babraham.ac.uk/projects/fastqc/).…”

“…Advancement in next-generation genome sequencing (NGS) technologies have greatly improved our ability to detect wide range of novel genomic and transcriptomic discoveries in the field of biomedical and veterinary science researches [1]. However, advancement in NGS technologies has also created significant challenges in bioinformatics and experimental design [2], particularly the major challenges is systematic evaluation of quality control of the RNA-seq data [3]. Monitoring and surveying QC metrics of NGS transcriptomic data provides unique and independent evaluations of RNA-seq data quality from differing perspectives [4].…”