A data and text mining pipeline to annotate human mitochondrial variants with functional and clinical information

Abstract

Background Human mitochondrial DNA has an important role in the cellular energy production through oxidative phosphorylation. Therefore, this process may be the cause and have an effect on mitochondrial DNA mutability, functional alteration, and disease onset related to a wide range of different clinical expressions and phenotypes. Although a large part of the observed variations is fixed in a population and hence expected to be benign, the estimation of the degree of the pathogenicity of any possible human mitochondrial DNA variant is clinically pivotal. Methods In this scenario, the establishment of standard criteria based on functional studies is required. In this context, a “data and text mining” pipeline is proposed here, developed using the programming language R, capable of extracting information regarding mitochondrial DNA functional studies and related clinical assessments from the literature, thus improving the annotation of human mitochondrial variants reported in the HmtVar database. Results The data mining pipeline has produced a list of 1,073 Pubmed IDs (PMIDs) from which the text mining pipeline has retrieved information on 932 human mitochondrial variants regarding experimental validation and clinical features. Conclusions The application of the pipeline will contribute to supporting the interpretation of pathogenicity of human mitochondrial variants by facilitating diagnosis to clinicians and researchers faced with this task.

Specifications of the ACMG/AMP standards and guidelines for mitochondrial DNA variant interpretation

“…Data are gathered from HmtVar's twin database, HmtDB, described above, and integrated with pathogenicity predictions and information from several mtDNA‐specific online resources. A pathogenicity prediction can then be generated for both nonsynonymous mRNA and tRNA variants (Diroma, Lubisco, & Attimonelli, 2016; Santorsola et al, 2016) and is enhanced by a literature mining pipeline (Vitale, Preste, Palmisano, & Attimonelli, 2020). As of July 2020, HmtVar includes 34,297 variants in protein‐coding genes, 4547 variants in mt‐tRNAs, 803 variants in mt‐rRNAs, and 1329 variants in the regulatory region.…”