Dataset Information

Evaluation and application of RNA-Seq by MinION.

ABSTRACT: The current RNA-Seq method analyses fragments of mRNAs, from which it is occasionally difficult to reconstruct the entire transcript structure. Here, we performed and evaluated the recent procedure for full-length cDNA sequencing using the Nanopore sequencer MinION. We applied MinION RNA-Seq for various applications, which would not always be easy using the usual RNA-Seq by Illumina. First, we examined and found that even though the sequencing accuracy was still limited to 92.3%, practically useful RNA-Seq analysis is possible. Particularly, taking advantage of the long-read nature of MinION, we demonstrate the identification of splicing patterns and their combinations as a form of full-length cDNAs without losing precise information concerning their expression levels. Transcripts of fusion genes in cancer cells can also be identified and characterized. Furthermore, the full-length cDNA information can be used for phasing of the SNPs detected by WES on the transcripts, providing essential information to identify allele-specific transcriptional events. We constructed a catalogue of full-length cDNAs in seven major organs for two particular individuals and identified allele-specific transcription and splicing. Finally, we demonstrate that single-cell sequencing is also possible. RNA-Seq on the MinION platform should provide a novel approach that is complementary to the current RNA-Seq.

SUBMITTER: Seki M

PROVIDER: S-EPMC6379022 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Evaluation and application of RNA-Seq by MinION.

Seki Masahide M Katsumata Eri E Suzuki Ayako A Sereewattanawoot Sarun S Sakamoto Yoshitaka Y Mizushima-Sugano Junko J Sugano Sumio S Kohno Takashi T Frith Martin C MC Tsuchihara Katsuya K Suzuki Yutaka Y

DNA research : an international journal for rapid publication of reports on genes and genomes 20190201 1

The current RNA-Seq method analyses fragments of mRNAs, from which it is occasionally difficult to reconstruct the entire transcript structure. Here, we performed and evaluated the recent procedure for full-length cDNA sequencing using the Nanopore sequencer MinION. We applied MinION RNA-Seq for various applications, which would not always be easy using the usual RNA-Seq by Illumina. First, we examined and found that even though the sequencing accuracy was still limited to 92.3%, practically use ...[more]

PMID: 30462165

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Project description:RNA-seq is a high-throughput sequencing technology widely used for gene transcript discovery and quantification under different biological or biomedical conditions. A fundamental research question in most RNA-seq experiments is the identification of differentially expressed genes among experimental conditions or sample groups. Numerous statistical methods for RNA-seq differential analysis have been proposed since the emergence of the RNA-seq assay. To evaluate popular differential analysis methods used in the open source R and Bioconductor packages, we conducted multiple simulation studies to compare the performance of eight RNA-seq differential analysis methods used in RNA-seq data analysis (edgeR, DESeq, DESeq2, baySeq, EBSeq, NOISeq, SAMSeq, Voom). The comparisons were across different scenarios with either equal or unequal library sizes, different distribution assumptions and sample sizes. We measured performance using false discovery rate (FDR) control, power, and stability. No significant differences were observed for FDR control, power, or stability across methods, whether with equal or unequal library sizes. For RNA-seq count data with negative binomial distribution, when sample size is 3 in each group, EBSeq performed better than the other methods as indicated by FDR control, power, and stability. When sample sizes increase to 6 or 12 in each group, DESeq2 performed slightly better than other methods. All methods have improved performance when sample size increases to 12 in each group except DESeq. For RNA-seq count data with log-normal distribution, both DESeq and DESeq2 methods performed better than other methods in terms of FDR control, power, and stability across all sample sizes. Real RNA-seq experimental data were also used to compare the total number of discoveries and stability of discoveries for each method. For RNA-seq data analysis, the EBSeq method is recommended for studies with sample size as small as 3 in each group, and the DESeq2 method is recommended for sample size of 6 or higher in each group when the data follow the negative binomial distribution. Both DESeq and DESeq2 methods are recommended when the data follow the log-normal distribution.

Project description:BackgroundRNA-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.ResultsIn 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.ConclusionsAt 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.

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Evaluation and application of RNA-Seq by MinION.

Publications

Evaluation and application of RNA-Seq by MinION.

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