A long-read RNA-seq approach to identify novel transcripts of very large genes
- Prech Uapinyoying1,2,3,6,
- Jeremy Goecks4,
- Susan M. Knoblach1,2,
- Karuna Panchapakesan1,
- Carsten G. Bonnemann1,3,
- Terence A. Partridge1,2,
- Jyoti K. Jaiswal1,2 and
- Eric P. Hoffman1,5
- 1Center for Genetic Medicine Research, Children's Research Institute, Children's National Health System, Washington, D.C. 20010, USA;
- 2Department of Genomics and Precision Medicine, The George Washington University School of Medicine and Health Sciences, Washington, D.C. 20052, USA;
- 3Neuromuscular and Neurogenetic Disorders of Childhood Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland 20892, USA;
- 4Computational Biology Program, Oregon Health and Science University, Portland, Oregon 97239, USA;
- 5Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, Binghamton University, Binghamton, New York 13902, USA
Abstract
RNA-seq is widely used for studying gene expression, but commonly used sequencing platforms produce short reads that only span up to two exon junctions per read. This makes it difficult to accurately determine the composition and phasing of exons within transcripts. Although long-read sequencing improves this issue, it is not amenable to precise quantitation, which limits its utility for differential expression studies. We used long-read isoform sequencing combined with a novel analysis approach to compare alternative splicing of large, repetitive structural genes in muscles. Analysis of muscle structural genes that produce medium (Nrap: 5 kb), large (Neb: 22 kb), and very large (Ttn: 106 kb) transcripts in cardiac muscle, and fast and slow skeletal muscles identified unannotated exons for each of these ubiquitous muscle genes. This also identified differential exon usage and phasing for these genes between the different muscle types. By mapping the in-phase transcript structures to known annotations, we also identified and quantified previously unannotated transcripts. Results were confirmed by endpoint PCR and Sanger sequencing, which revealed muscle-type-specific differential expression of these novel transcripts. The improved transcript identification and quantification shown by our approach removes previous impediments to studies aimed at quantitative differential expression of ultralong transcripts.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.259903.119.
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Freely available online through the Genome Research Open Access option.
- Received December 3, 2019.
- Accepted May 22, 2020.
This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.
