Visualization and analysis of RNA-Seq assembly graphs

• Published in: Nucleic acids research Vol. 47; no. 14; pp. 7262 - 7275
• Main Authors: Nazarie, Fahmi W, Shih, Barbara, Angus, Tim, Barnett, Mark W, Chen, Sz-Hau, Summers, Kim M, Klein, Karsten, Faulkner, Geoffrey J, Saini, Harpreet K, Watson, Mick, Dongen, Stijn van, Enright, Anton J, Freeman, Tom C
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 22-08-2019
• Subjects: Alternative Splicing; Computational Biology; Computer Graphics; Gene Expression Profiling - methods; Genome, Human - genetics; Humans; Models, Genetic; Models, Molecular; Nucleic Acid Conformation; RNA Isoforms - chemistry; RNA Isoforms - genetics; RNA Isoforms - metabolism; RNA, Messenger - chemistry; RNA, Messenger - genetics; RNA, Messenger - metabolism; Sequence Analysis, RNA - methods
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gkz599

Abstract
RNA-Seq is a powerful transcriptome profiling technology enabling transcript discovery and quantification. Whilst most commonly used for gene-level quantification, the data can be used for the analysis of transcript isoforms. However, when the underlying transcript assemblies are complex, current visualization approaches can be limiting, with splicing events a challenge to interpret. Here, we report on the development of a graph-based visualization method as a complementary approach to understanding transcript diversity from short-read RNA-Seq data. Following the mapping of reads to a reference genome, a read-to-read comparison is performed on all reads mapping to a given gene, producing a weighted similarity matrix between reads. This is used to produce an RNA assembly graph, where nodes represent reads and edges similarity scores between them. The resulting graphs are visualized in 3D space to better appreciate their sometimes large and complex topology, with other information being overlaid on to nodes, e.g. transcript models. Here we demonstrate the utility of this approach, including the unusual structure of these graphs and how they can be used to identify issues in assembly, repetitive sequences within transcripts and splice variants. We believe this approach has the potential to significantly improve our understanding of transcript complexity.