Methods and tools for assessing the impact of genetic variations: The 2017 Annual Scientific Meeting of the Human Genome Variation Society

A Comprehensive Review of Genetic Variations in Collagen-Encoding Genes and Their Implications in Intervertebral Disc Degeneration

This comprehensive review examines the intricate relationship between genetic variations in collagen-encoding genes and their implications in intervertebral disc degeneration (IVDD). Intervertebral disc degeneration is a prevalent spinal condition characterized by structural and functional changes in intervertebral discs (IVDs), and understanding its genetic underpinnings is crucial for advancing diagnostic and therapeutic strategies. The review begins by exploring the background and importance of collagen in IVDs, emphasizing its role in providing structural integrity. It then delves into the significance of genetic variations within collagen-encoding genes, categorizing and discussing their potential impact on disc health. The methods employed in studying these variations, such as genome-wide association studies (GWASs) and next-generation sequencing (NGS), are also reviewed. The subsequent sections analyze existing literature to establish associations between genetic variations and IVDD, unraveling molecular mechanisms linking genetic factors to disc degeneration. The review concludes with a summary of key findings, implications for future research and clinical practice, and a reflection on the importance of understanding genetic variations in collagen-encoding genes to diagnose and treat IVDD. The insights gleaned from this review contribute to our understanding of IVDD and hold promise for the development of personalized interventions based on individual genetic profiles.

Computational Tools for Splicing Defect Prediction in Breast/Ovarian Cancer Genes: How Efficient Are They at Predicting RNA Alterations?

In silico tools for splicing defect prediction have a key role to assess the impact of variants of uncertain significance. Our aim was to evaluate the performance of a set of commonly used splicing in silico tools comparing the predictions against RNA in vitro results. This was done for natural splice sites of clinically relevant genes in hereditary breast/ovarian cancer (HBOC) and Lynch syndrome. A study divided into two stages was used to evaluate SSF-like, MaxEntScan, NNSplice, HSF, SPANR, and dbscSNV tools. A discovery dataset of 99 variants with unequivocal results of RNA in vitro studies, located in the 10 exonic and 20 intronic nucleotides adjacent to exon-intron boundaries of BRCA1, BRCA2, MLH1, MSH2, MSH6, PMS2, ATM, BRIP1, CDH1, PALB2, PTEN, RAD51D, STK11, and TP53, was collected from four Spanish cancer genetic laboratories. The best stand-alone predictors or combinations were validated with a set of 346 variants in the same genes with clear splicing outcomes reported in the literature. Sensitivity, specificity, accuracy, negative predictive value (NPV) and Mathews Coefficient Correlation (MCC) scores were used to measure the performance. The discovery stage showed that HSF and SSF-like were the most accurate for variants at the donor and acceptor region, respectively. The further combination analysis revealed that HSF, HSF+SSF-like or HSF+SSF-like+MES achieved a high performance for predicting the disruption of donor sites, and SSF-like or a sequential combination of MES and SSF-like for predicting disruption of acceptor sites. The performance confirmation of these last results with the validation dataset, indicated that the highest sensitivity, accuracy, and NPV (99.44%, 99.44%, and 96.88, respectively) were attained with HSF+SSF-like or HSF+SSF-like+MES for donor sites and SSF-like (92.63%, 92.65%, and 84.44, respectively) for acceptor sites. We provide recommendations for combining algorithms to conduct in silico splicing analysis that achieved a high performance. The high NPV obtained allows to select the variants in which the study by in vitro RNA analysis is mandatory against those with a negligible probability of being spliceogenic. Our study also shows that the performance of each specific predictor varies depending on whether the natural splicing sites are donors or acceptors.

Bioinformatics-based tools in drug discovery: the cartography from single gene to integrative biological networks

Originally developed for the analysis of biological sequences, bioinformatics has advanced into one of the most widely recognized domains in the scientific community. Despite this technological evolution, there is still an urgent need for nontoxic and efficient drugs. The onus now falls on the 'omics domain to meet this need by implementing bioinformatics techniques that will allow for the introduction of pioneering approaches in the rational drug design process. Here, we categorize an updated list of informatics tools and explore the capabilities of integrative bioinformatics in disease control. We believe that our review will serve as a comprehensive guide toward bioinformatics-oriented disease and drug discovery research.