Institute for Integrative Systems Biology, Spanish National Research Council (CSIC), Paterna, Spain.
Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
Nat Methods. 2024 Jul;21(7):1349-1363. doi: 10.1038/s41592-024-02298-3. Epub 2024 Jun 7.
The Long-read RNA-Seq Genome Annotation Assessment Project Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. Using different protocols and sequencing platforms, the consortium generated over 427 million long-read sequences from complementary DNA and direct RNA datasets, encompassing human, mouse and manatee species. Developers utilized these data to address challenges in transcript isoform detection, quantification and de novo transcript detection. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. Incorporating additional orthogonal data and replicate samples is advised when aiming to detect rare and novel transcripts or using reference-free approaches. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.
长读 RNA-Seq 基因组注释评估项目联盟成立，旨在评估长读长序列在转录组分析中的有效性。该联盟使用不同的方案和测序平台，从 cDNA 和直接 RNA 数据集生成了超过 4.27 亿个长读序列，涵盖了人类、小鼠和海牛物种。开发人员利用这些数据解决了转录本异构体检测、定量和从头转录本检测方面的挑战。研究表明，具有更长、更准确序列的文库比具有更高读长的文库产生更准确的转录本，而更高的读长则提高了定量准确性。在注释良好的基因组中，基于参考序列的工具表现最佳。建议在检测稀有和新型转录本或使用无参考方法时，纳入额外的正交数据和重复样本。这项合作研究为当前的实践提供了基准，并为转录组分析中的未来方法开发提供了方向。
