This study presents a method for direct RNA sequencing using an array of nanopores, which allows for the direct sequencing of RNA without the need for reverse transcription or PCR amplification. The method uses a nanopore-based sensing platform that can sequence DNA directly without enzymatic synthesis. RNA strands are passed through a protein nanopore embedded in a hydrophobic membrane, and the sequence-specific changes in ionic current are used to determine the RNA sequence. The system is capable of detecting native nucleotides, including modified ones, and produces full-length, strand-specific RNA sequences. This approach offers several advantages over traditional RNA-seq methods, including the absence of amplification and reverse transcription biases, the ability to detect nucleotide analogues, and the generation of full-length, strand-specific RNA sequences. The method is also capable of sequencing long RNA strands, which is essential for the study of splice variants. The study demonstrates the ability of the nanopore-based platform to sequence RNA directly, with applications in various biological contexts, including the sequencing of yeast transcripts and human rhinovirus. The results show that the method can accurately detect RNA sequences and modifications, with high accuracy and minimal bias. The study also highlights the potential of this method for improving the ease and speed of RNA analysis while yielding richer biological information. The method is performed using a consumable chip on a commercially available handheld device, and is compatible with real-time data analysis. The study also discusses the potential for further improvements in the method, including the optimization of the motor protein and the exploration of alternative cDNA synthesis approaches. The results indicate that direct RNA sequencing using nanopores is a promising new approach for analyzing RNA samples, with the potential to provide a deeper understanding of transcriptomes than has been possible with indirect, short read sequence data.This study presents a method for direct RNA sequencing using an array of nanopores, which allows for the direct sequencing of RNA without the need for reverse transcription or PCR amplification. The method uses a nanopore-based sensing platform that can sequence DNA directly without enzymatic synthesis. RNA strands are passed through a protein nanopore embedded in a hydrophobic membrane, and the sequence-specific changes in ionic current are used to determine the RNA sequence. The system is capable of detecting native nucleotides, including modified ones, and produces full-length, strand-specific RNA sequences. This approach offers several advantages over traditional RNA-seq methods, including the absence of amplification and reverse transcription biases, the ability to detect nucleotide analogues, and the generation of full-length, strand-specific RNA sequences. The method is also capable of sequencing long RNA strands, which is essential for the study of splice variants. The study demonstrates the ability of the nanopore-based platform to sequence RNA directly, with applications in various biological contexts, including the sequencing of yeast transcripts and human rhinovirus. The results show that the method can accurately detect RNA sequences and modifications, with high accuracy and minimal bias. The study also highlights the potential of this method for improving the ease and speed of RNA analysis while yielding richer biological information. The method is performed using a consumable chip on a commercially available handheld device, and is compatible with real-time data analysis. The study also discusses the potential for further improvements in the method, including the optimization of the motor protein and the exploration of alternative cDNA synthesis approaches. The results indicate that direct RNA sequencing using nanopores is a promising new approach for analyzing RNA samples, with the potential to provide a deeper understanding of transcriptomes than has been possible with indirect, short read sequence data.