PhenGenVar: A User-Friendly Genetic Variant Detection and Visualization Tool for Precision Medicine

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

PHDtools: A platform for pathogen detection and multi-dimensional genetic signatures decoding to realize pathogen genomics data analyses online

OBJECTIVES

Facing the emerging diseases, rapid identification of the pathogen and multi-dimensional characterization of the genomic features at both isolate-level and population-level through high-throughput sequencing data can provide invaluable information to guide the development of antiviral agents and strategies. However, a user-friendly program is in urgent need for clinical laboratories without bioinformatics background to decode the complex big genomics data.

METHODS

In this study, we developed an interactive online platform named PHDtools with a total of 15 functions to analyze metagenomics data to identify the potential pathogen and decode multi-dimensional genetic signatures including intra-/inter-host variations and lineage-level variations. The platform was applied to analyze the meta-genomic data of the samples collected from the 172 imported COVID-19 cases.

RESULTS

According to the analytical results of mNGS, 27 patients were found to have the co-infections of SARS-CoV-2 with either influenza virus (n = 9) or human picobirnavirus (n = 19). Enough coverages of all the assembled SARS-CoV-2 genomes provided the sub-lineages of Omicron variant, and the number of mutations in the non-structural genes and M gene was increased, as well as the intra-host variations occurred in E and M gene were under positive selection (Ka/Ks > 1). These findings of increased or changed mutations in the SARS-CoV-2 genome characterized the current adaptive evolution patterns of Omicron sub-lineages, and revealed the evolution speed of these sub-lineages might increase.

CONCLUSIONS

Consequently, the application of PHDtools has proved that this platform is accurate, user-friendly and convenient for clinical users who are deficient in bioinformatics, and the clinical monitor of SARS-CoV-2 genomes by PHDtools also highlighted the potential evolution features of current SARS-CoV-2 and indicated that the development of anti-SARS-CoV-2 agents and new-designed vaccines should incorporate the gene variations other than S gene.

From the reference human genome to human pangenome: Premise, promise and challenge

The Reference Human Genome remains the single most important resource for mapping genetic variations and assessing their impact. However, it is monophasic, incomplete and not representative of the variation that exists in the population. Given the extent of ethno-geographic diversity and the consequent diversity in clinical manifestations of these variations, population specific references were developed overtime. The dramatically plummeting cost of sequencing whole genomes and the advent of third generation long range sequencers allowing accurate, error free, telomere-to-telomere assemblies of human genomes present us with a unique and unprecedented opportunity to develop a more composite standard reference consisting of a collection of multiple genomes that capture the maximal variation existing in the population, with the deepest annotation possible, enabling a realistic, reliable and actionable estimation of clinical significance of specific variations. The Human Pangenome Project thus is a logical next step promising a more accurate and global representation of genomic variations. The pangenome effort must be reciprocally complemented with precise variant discovery tools and exhaustive annotation to ensure unambiguous clinical assessment of the variant in ethno-geographical context. Here we discuss a broad roadmap, the challenges and way forward in developing a universal pangenome reference including data visualization techniques and integration of prior knowledge base in the new graph based architecture and tools to submit, compare, query, annotate and retrieve relevant information from the pangenomes. The biggest challenge, however, will be the ethical, legal and social implications and the training of human resource to the new reference paradigm.