RNA splicing analysis in genomic medicine

Abstract

High-throughput next-generation sequencing technologies have led to a rapid increase in the number of sequence variants identified in clinical practice via diagnostic genetic tests. Current bioinformatic analysis pipelines fail to take adequate account of the possible splicing effects of such variants, particularly where variants fall outwith canonical splice site sequences, and consequently the pathogenicity of such variants may often be missed. The regulation of splicing is highly complex and as a result, in silico prediction tools lack sufficient sensitivity and specificity for reliable use. Variants of all kinds can be linked to aberrant splicing in disease and the need for correct identification and diagnosis grows ever more crucial as novel splice-switching antisense oligonucleotide therapies start to enter clinical usage. RT-PCR provides a useful targeted assay of the splicing effects of identified variants, while minigene assays, massive parallel reporter assays and animal models can also be used for more detailed study of a particular splicing system, given enough time and resources. However, RNA-sequencing (RNA-seq) has the potential to be used as a rapid diagnostic tool in genomic medicine. By utilising data science approaches and machine learning, it may prove possible to finally understand and interpret the 'splicing code' and apply this knowledge in human disease diagnostics.

Analysis of transcript-deleterious variants in Mendelian disorders: implications for RNA-based diagnostics

“…These include variants that reduce the abundance of the transcript, e.g., nonsense-mediated decay (NMD), as well as those that create aberrant splicing. Early experience with RNA-Seq (massively parallel sequencing of RNA) suggests its potential to reveal variants that have been missed at the sequencing stage as well as those that have been missed at the interpretation stage [10,11,[19][20][21]. It is also clear from these studies, however, that there are unique computational challenges to this technology, and although several computational tools have been developed, there is a growing need for a deeper understanding of the nature of transcript-deleterious variants to inform better tools.…”

Dual Fluorescence Splicing Reporter Minigene Identifies an Antisense Oligonucleotide to Skip Exon v8 of the CD44 Gene

“…Minigene has been the golden standard to analyze splicing outcome in vitro [20,25]. In the systems, the splicing product is analyzed by RT-PCR amplification of the resultant mRNA.…”

“…Exon skippable ASOs or small molecular chemicals have been screened mainly by minigene splicing assays [18,19]. On the other hand, the assay has been commonly used in a cell-based in vitro approach for splicing studies of genomic nucleotide changes [20,21]. In this way, a minigene harboring a genomic segment encompassing the target sequence of interest is constructed and transfected into cultured cells.…”

RNA-Seq Perspectives to Improve Clinical Diagnosis

“…In addition, it is necessary to define the laboratory and bioinformatics parameters and tools that allow monitoring and controlling this process. For example, from a laboratory point of view, assessment of the quality and quantity of extracted RNA, or the library preparation strategy and its possible relationship with technical bias for the NGS process are some of the most important parameters to consider (Wai et al, 2019). To control this bias, different mathematical methods, such as principal component analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (tSNE) based on expression have been proposed (Dey et al, 2017).…”

“…In this respect, there are different obstacles for bioinformatics analysis of RNA-seq data. Among them are the mapping process and the possible effect of different factors on the identification of variants, such as the presence of neighboring SNPs and small indels in the unbiased identification of ASE (Wood et al, 2015; Byron et al, 2016), junction events (Williams et al, 2014), or the isoform assembly process, where the length of reads, library preparation strategy, the initial coverage, and GC content of the transcripts could affect the accuracy of the transcript identification process (Mantere et al, 2019;Wai et al, 2019).…”