Illuminating the dark side of the human transcriptome with long read transcript sequencing

作者：Richard I Kuo, Yuanyuan Cheng, Runxuan Zhang, John W. Brown, Jacqueline Smith, Alan L. Archibald, David W. Burt · 发表于：BMC Genomics · 年份：2020 · DOI：10.1186/s12864-020-07123-7 · 被引用次数：200 · 研究领域：Genomics and Phylogenetic Studies、Single-cell and spatial transcriptomics、Cancer-related molecular mechanisms research

BACKGROUND: The human transcriptome annotation is regarded as one of the most complete of any eukaryotic species. However, limitations in sequencing technologies have biased the annotation toward multi-exonic protein coding genes. Accurate high-throughput long read transcript sequencing can now provide additional evidence for rare transcripts and genes such as mono-exonic and non-coding genes that were previously either undetectable or impossible to differentiate from sequencing noise. RESULTS: We developed the Transcriptome Annotation by Modular Algorithms (TAMA) software to leverage the power of long read transcript sequencing and address the issues with current data processing pipelines. TAMA achieved high sensitivity and precision for gene and transcript model predictions in both reference guided and unguided approaches in our benchmark tests using simulated Pacific Biosciences (PacBio) and Nanopore sequencing data and real PacBio datasets. By analyzing PacBio Sequel II Iso-Seq sequencing data of the Universal Human Reference RNA (UHRR) using TAMA and other commonly used tools, we found that the convention of using alignment identity to measure error correction performance does not reflect actual gain in accuracy of predicted transcript models. In addition, inter-read error correction can cause major changes to read mapping, resulting in potentially over 6 K erroneous gene model predictions in the Iso-Seq based human genome annotation. Using TAMA's genome assembly based e...