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

## TL;DR

This paper discusses how misannotating genetic variants as protein-coding or noncoding can lead to errors in understanding their role in disease.

## Contribution

The paper introduces a method using RNA sequencing data to accurately annotate genetic variants based on expressed transcripts in specific tissues.

## Key findings

- Misannotations occur when variants are annotated to canonical transcripts instead of expressed ones.
- RNA sequencing data can reveal which transcripts are expressed in specific tissues.
- Integrating DNA and RNA sequencing improves the functional interpretation of genetic variants.

## 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