Direct sequencing of single molecules through nanopores allows for accurate quantification and full-length characterization of native RNA or complementary DNA (cDNA) without amplification. Both nanopore-based native RNA and cDNA approaches involve complex transcriptome procedures at a lower cost. However, there are several differences between the two approaches. In this study, we perform matched native RNA sequencing and cDNA sequencing to enable relevant comparisons and evaluation. Using Saccharomyces cerevisiae, a eukaryotic model organism widely used in industrial biotechnology, two different growing conditions are considered for comparison, including the poly-A messenger RNA isolated from yeast cells grown in minimum media under respirofermentative conditions supplemented with glucose (glucose growth conditions) and from cells that had shifted to ethanol as a carbon source (ethanol growth conditions). Library preparation for direct RNA sequencing is shorter than that for direct cDNA sequencing. The sequence characteristics of the two methods were different, such as sequence yields, quality score of reads, read length distribution, and mapped on reference ability of reads. However, differential gene expression analyses derived from the two approaches are comparable. The unique feature of direct RNA sequencing is RNA modification; we found that the RNA modification at the 5′ end of a transcript was underestimated due to the 3′ bias behavior of the direct RNA sequencing. Our comprehensive evaluation from this work could help researchers make informed choices when selecting an appropriate long-read sequencing method for understanding gene functions, pathways, and detailed functional characterization.

RNA-Seq (short for RNA sequencing) is a next-generation sequencing (NGS) technique used to quantify and identify RNA molecules in a biological sample RNA-Seq (short for RNA sequencing) is a next-generation sequencing (NGS) technique used to quantify and identify RNA molecules in a biological sample, providing a snapshot of the transcriptome at a specific time. It enables transcriptome-wide analysis by sequencing cDNA derived from RNA. Modern workflows often incorporate pseudoalignment tools (such as Kallisto and Salmon) and cloud-based processi Th… Massively parallel single molecule direct RNA-Seq has been explored as an alternative to traditional RNA-Seq, in which RNA-to-cDNA conversion, ligation, amplification, and other sample manipulation steps may introduce biases and artefacts. Technology platforms that perform single-molecule real-time RNA-Seq include Oxford Nanopore Technologies (ONT) Nanopore sequencing. Sequencing RNA in its native form preserves modifications like methylation, allowing them to be investigated directly and simultaneously. Another benefit of single-molecule direct RNA-Seq is that transcripts can be covered in full length, allowing for higher confidence isoform detection and quantification compared to short-read sequencing. Traditionally, single-molecule RNA-Seq methods have higher error rates compared to short-read sequencing, but newer methods like ONT direct RNA-Seq have a reduced error rate. Recent uses of ONT direct RNA-Seq for differential expression in human cell populations have demonstrated that this technology can overcome many limitations of short and long cDNA sequencing.

ise of long-read technologies now opens the possibility to overcome those limitations and biases. Long-read RNA-seq captures a full-length transcript within a single read, thereby allowing accurate transcript annotation and enabling a comprehensive view of the transcriptome. Sequencing prokaryotic transcriptomes using the long-read technology reveals complex operon structures, which provide an important resource for functional annotation ( Yan et al., 2018 ). Currently, the most widely used platforms are Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT). With the read lengths achieved with PacBio (∼15 kb) and with ONT (>30 kb), both surpass lengths of most transcripts ( Harrow et al., 2012 ; Oikonomopoulos et al., 2020 ; Udaondo et al., 2021 ). However, with ONT, if native RNA can be directly sequenced RNA (dRNA-seq) without PCR amplification, then amplification biases are eliminated ( Workman et al., 2019 ). The direct sequencing feature permits detection of RNA base modifications, such as N6-methyladenine (m6A), which has been linked to human obesity and cancer ( Mortazavi et al., 2008 ). In addition, ONT is a more cost-effective method than PacBio in terms of machine cost and number of bases per 1,000 USD ( Byrne et al., 2019 ). Using dRNA-seq ( Jenjaroenpun et al., 2018 ), we recently showed a transcriptional landscape analysis of the Saccharomyces cerevisiae strain, CEN.PK113-7D, a yeast strain that is used extensively in academic and industrial research. We determined transcriptomic profiling under two different growth conditions (diauxic growth). Approximately 70% of the reads corresponded to full-length transcripts. Some full-length transcripts over 5 kb were also detected and mapped. In addition, identification of polyadenylated non-coding RNAs (i.e., ribosomal RNA, telomerase RNA, and long non-coding RNA) is allowed using this sequencing protocol ( Jenjaroenpun et al., 2018 ). After releasing the dRNA-seq approach, a direct cDNA sequen

Single cell RNA sequencing (scRNA-seq) emerged to characterize gene expression differences between individual cells derived from a complex tissue, allowing a higher resolution look at mRNA abundance than bulk RNA-seq. However, most scRNA-seq methodologies have been coupled to short read sequencing platforms that offer either sparse information for an mRNA spread across the entire transcript or 3’ end tags to quantify gene counts. Neither can reveal contiguous mRNA sequences and the associated isoform-specific open reading frames which often encode proteins with different functional properties. We show that single cell platforms, including the DropSeq and 10X systems, combined with PacBio SMRT Sequencing, can generate full-length cDNA isoforms that can be confidently assigned to individual single cells. We show that, with the recent sequencing chemistry on the PacBio Sequel II system, the average read lengths have exceed 60-100 kb, allowing generation of highly accurate circular consensus reads (CCS). The accurate CCS reads allow precise identification of cell barcodes and UMIs located between the polyadenylation signal and the 3’ cDNA primers, eliminating the need for further error correction. We show that the cell barcodes identified on a matching single cell library of PacBio and Illumina show almost 100% concordance for the top-ranking cells. To characterize the full-length transcripts, we developed the tool SQANTI2 that is an extended version of the original SQANTI software (Tardaquila et al . 2017). SQANTI2 classifies full-length transcripts against a reference annotation (ex: GENCODE) and utilizes additional information such as FANTOM5 cage peak data, polyA motif signals, public RNA-seq junction data (ex: Intropolis), and matching Illumina expression data. Transcripts are classified as full-splice matches (perfect intron chain matches to a reference), incomplete-splice matches (possible 5’ degradation), novel in catalog (novel isoforms using only donot/acceptor sites), novel not in catalog (novel isoforms with at least one novel donor/acceptor site), antisense, intergenic, etc. Such classification becomes important for both bulk RNA-seq analysis, where identification of differentially expressed isoforms or novel splice patterns may indicate disease specificity, and for single cell RNA-seq analysis, where we can begin to look at cell type specific isoform expression. We demonstrate SQANTI2 on multiple single cell data and show that, combining it with another software suite, Cupcake, full-length isoform data can be analyzed with existing single cell tools such as Seurat.