To be or not to be a protein coding mutation, that's the question!

Abstract

Accurate annotation of genetic variants-distinguishing whether they affect protein-coding or noncoding genomic regions-is crucial for evaluating their potential role in disease development. Prominent examples have been identified of variants that for many years had been considered to be coding missense or synonymous mutations targeting one gene, and that recently turned out to be noncoding variants, sometimes even modulating a shared regulatory region of multiple genes. These errors were caused by annotating to a canonical reference transcript, whereas an alternative transcript was in reality expressed in respect to which the mutations have a different annotation. Unfortunately, this practice of annotating genetic variants to a reference transcript, without verifying whether this transcript is expressed or whether the mutation causes a change of expressed transcript, is still widespread. However, the implementation of RNA sequencing and availability of these data in online portals allow to verify expressed transcripts in relevant tissues. Integration of DNA- and RNA-sequencing data, in which detected DNA mutations are annotated in respect to the transcripts that are expressed in the corresponding tissue or disease sample as detected by RNA sequencing, avoids misinterpretation of noncoding variants as coding and vice versa, thereby improving the functional interpretation of genetic variants.

Graphical Abstract

Figure 1.

Reannotation of coding to noncoding mutations based on expressed isoform analysis. (A) RNA-seq data from melanoma tumors were mapped against hg38 in Integrative Genomics Viewer (IGV). A representative RNA-seq read distribution plot of a wild type and a mutant tumor is shown. The genomic location of the IRF3/BCL2L12 promoter variants is indicated, as well as the relevant IRF3 and BCL2L12 transcripts. (B) IGV mapping of RNA-seq data from a representative BAP1 wild type and mutated tumor. The genomic location of the BAP1 variants is indicated, as well as wild-type BAP1 transcript and the splice variant generated by the mutant. RNA-seq BAM files of tumors shown in this figure were downloaded from NCI Genomics Data Commons after having obtained dbGaP Authorized Access to dataset phs003014 [NCI’s Datasets for General Research Use, study accession phs000178.v11.p8—The Cancer Genome Atlas (TCGA)].

Figure 2.

Challenges in annotating STK19 mutations. Short-read RNA-seq data from melanoma tumors were mapped against hg38 in IGV. A representative RNA-seq read distribution plot of a wild type and a mutant melanoma tumor is shown in the upper part of the panel. The genomic location of the STK19 hotspot mutation is indicated, as well as transcripts STK-201 and STK-218. The shown RNA-seq BAM files of melanoma tumors were downloaded from NCI Genomics Data Commons after having obtained dbGAP Authorized Access to dataset phs003014 (NCI’s Datasets for General Research Use, study accession phs000178.v11.p8—TCGA). In the middle, long-read RNA-seq data from Mel-ST melanocytes are shown. These data were described in [15] and are available for download at NCBI SRA (BioProject accession number: PRJNA1196620) and GEO (accession number: GSE290597).

Figure 3.

Recommended approach to avoid mutation misannotation. Flow chart summarizing our recommended approach to avoid misannotation of genetic variants. The gray interrupted arrow between “Unambiguous Annotation” and “Functional Testing” indicates that functional testing is optional to obtain accurate annotation when there is unambiguous annotation based on expressed transcripts. Such a testing can however still provide additional information of the impact of the mutation on gene expression and gene function.