Enhancing novel isoform discovery: leveraging nanopore long-read sequencing and machine learning approaches

• Published in: Briefings in functional genomics 2024-12, Vol.23 (6), p.683
• Main Authors: Santucci, Kristina, Cheng, Yuning, Xu, Si-Mei, Janitz, Michael
• Format: Article
• Language: English
• Subjects: Computational Biology - methods; High-Throughput Nucleotide Sequencing - methods; Humans; Machine Learning; Nanopore Sequencing - methods; Nanopores; Protein Isoforms - genetics; Sequence Analysis, RNA - methods; Software; Transcriptome - genetics
• ISSN: 2041-2649; 2041-2657; 2041-2657
• DOI: 10.1093/bfgp/elae031

Long-read sequencing technologies can capture entire RNA transcripts in a single sequencing read, reducing the ambiguity in constructing and quantifying transcript models in comparison to more common and earlier methods, such as short-read sequencing. Recent improvements in the accuracy of long-read sequencing technologies have expanded the scope for novel splice isoform detection and have also enabled a far more accurate reconstruction of complex splicing patterns and transcriptomes. Additionally, the incorporation and advancements of machine learning and deep learning algorithms in bioinformatic software have significantly improved the reliability of long-read sequencing transcriptomic studies. However, there is a lack of consensus on what bioinformatic tools and pipelines produce the most precise and consistent results. Thus, this review aims to discuss and compare the performance of available methods for novel isoform discovery with long-read sequencing technologies, with 25 tools being presented. Furthermore, this review intends to demonstrate the need for developing standard analytical pipelines, tools, and transcript model conventions for novel isoform discovery and transcriptomic studies.