RNA-Seq Perspectives to Improve Clinical Diagnosis

Abstract

In recent years, high-throughput next-generation sequencing technology has allowed a rapid increase in diagnostic capacity and precision through different bioinformatics processing algorithms, tools, and pipelines. The identification, annotation, and classification of sequence variants within different target regions are now considered a gold standard in clinical genetic diagnosis. However, this procedure lacks the ability to link regulatory events such as differential splicing to diseases. RNA-seq is necessary in clinical routine in order to interpret and detect among others splicing events and splicing variants, as it would increase the diagnostic rate by up to 10-35%. The transcriptome has a very dynamic nature, varying according to tissue type, cellular conditions, and environmental factors that may affect regulatory events such as splicing and the expression of genes or their isoforms. RNA-seq offers a robust technical analysis of this complexity, but it requires a profound knowledge of computational/statistical tools that may need to be adjusted depending on the disease under study. In this article we will cover RNA-seq analyses best practices applied to clinical routine, bioinformatics procedures, and present challenges of this approach.

Keywords: DEG (differentially expressed genes); RNA-Seq - RNA sequencing; alternative splicing (AS); bioinformatics; clinical routine; tissue-specific expression; transcriptomics; variants of uncertain significance (VUS).

Figure 1

RNA-seq alternative splicing events and mapped reads. Different alternative splicing events can be detected using RNA-seq. Spliced mapped reads anchor differently if alternative splicing event occurs. Constitutive spliced RNA-seq mapped reads are represented in gray and alternatively spliced RNA-seq mapped reads are represented in red.