The majority of human genes are alternatively spliced, making it possible for most genes to generate multiple proteins. The process of alternative splicing is highly regulated in a developmental-stage and tissue-specific manner. Perturbations in the regulation of these events can lead to disease in humans. Alternative splicing has been shown to play a role in human cancer, muscular dystrophy, Alzheimer’s, and many other diseases. Understanding these diseases requires knowing the full complement of mRNA isoforms. Microarrays and high-throughput cDNA sequencing have become highly successful tools for studying transcriptomes, however these technologies only provide small fragments of transcripts and building complete transcript isoforms has been very challenging. We have developed the Iso-Seq technique, which is capable of sequencing full-length, single-molecule cDNA sequences. The method employs SMRT Sequencing to generate individual molecules with average read lengths of more than 10 kb and some as long as 40 kb. As most transcripts are from 1 to 10 kb, we can sequence through entire RNA molecules, requiring no fragmentation or post-sequencing assembly. Jointly with the sequencing method, we developed a computational pipeline that polishes these full-length transcript sequences into high-quality, non-redundant transcript consensus sequences. Iso-Seq sequencing enables unambiguous identification of alternative splicing events, alternative transcriptional start and poly-A sites, and transcripts from gene fusion events. Knowledge of the complete set of isoforms from a sample of interest is key for accurate quantification of isoform abundance when using any technology for transcriptome studies. Here we characterize the full-length transcriptome of normal human tissues, paired tumor/normal samples from breast cancer, and a brain sample from a patient with Alzheimer’s using deep Iso-Seq sequencing. We highlight numerous discoveries of novel alternatively spliced isoforms, gene-fusions events, and previously unannotated genes that will improve our understanding of human diseases.
Organization: PacBio
Year: 2015
