New approaches to predict the effect of co-occurring variants on protein characteristics

NAVIP: Unraveling the influence of neighboring small sequence variants on functional impact prediction

Once a suitable reference sequence has been generated, intra-species variation is often assessed by re-sequencing. Variant calling processes can reveal all differences between strains, accessions, genotypes, or individuals. These variants can be enriched with predictions about their functional implications based on available structural annotations, i.e., gene models. Although these functional impact predictions on a per-variant basis are often accurate, some challenging cases require the simultaneous incorporation of multiple adjacent variants into this prediction process. Examples include neighboring variants which modify each other's functional impact. The Neighborhood-Aware Variant Impact Predictor (NAVIP) considers all variants within a given protein coding sequence when predicting the effect. As a proof of concept, variants between the Arabidopsis thaliana accessions Columbia-0 and Niederzenz-1 were annotated. NAVIP is freely available on GitHub (https://github.com/bpucker/NAVIP) and accessible through a web server (https://pbb-tools.de).

Variant Effect Prediction in the Age of Machine Learning

Over the years, many computational methods have been created for the analysis of the impact of single amino acid substitutions resulting from single-nucleotide variants in genome coding regions. Historically, all methods have been supervised and thus limited by the inadequate sizes of experimentally curated data sets and by the lack of a standardized definition of variant effect. The emergence of unsupervised, deep learning (DL)-based methods raised an important question: Can machines learn the language of life from the unannotated protein sequence data well enough to identify significant errors in the protein "sentences"? Our analysis suggests that some unsupervised methods perform as well or better than existing supervised methods. Unsupervised methods are also faster and can, thus, be useful in large-scale variant evaluations. For all other methods, however, their performance varies by both evaluation metrics and by the type of variant effect being predicted. We also note that the evaluation of method performance is still lacking on less-studied, nonhuman proteins where unsupervised methods hold the most promise.

In silico methods for predicting functional synonymous variants

Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous SNVs are previously considered to be "silent," but mounting evidence has revealed that these variants can cause RNA and protein changes and are implicated in over 85 human diseases and cancers. Recent improvements in computational platforms have led to the development of numerous machine-learning tools, which can be used to advance synonymous SNV research. In this review, we discuss tools that should be used to investigate synonymous variants. We provide supportive examples from seminal studies that demonstrate how these tools have driven new discoveries of functional synonymous SNVs.

Insights into Mechanisms of Pheochromocytomas and Paragangliomas Driven by Known or New Genetic Drivers

Simple Summary

Pheochromocytomas and paragangliomas are rare neuroendocrine tumors that are often hereditary. Although research has advanced considerably, significant gaps still persist in understanding risk factors, predicting metastatic potential and treating aggressive tumors. The study of rare mutations can provide new insights into how pheochromocytomas and paragangliomas develop. In this review, we provide examples of such rare events and how they can inform our understanding of the spectrum of mutations that can lead to these tumors and improve our ability to provide a genetic diagnosis.

Abstract

Pheochromocytomas and paragangliomas are rare tumors of neural crest origin. Their remarkable genetic diversity and high heritability have enabled discoveries of bona fide cancer driver genes with an impact on diagnosis and clinical management and have consistently shed light on new paradigms in cancer. In this review, we explore unique mechanisms of pheochromocytoma and paraganglioma initiation and management by drawing from recent examples involving rare mutations of hypoxia-related genes VHL, EPAS1 and SDHB, and of a poorly known susceptibility gene, TMEM127. These models expand our ability to predict variant pathogenicity, inform new functional domains, recognize environmental-gene connections, and highlight persistent therapeutic challenges for tumors with aggressive behavior.