Next-Generation Sequencing (NGS) studies are already proving to be a powerful tool in the field of human health and precision medicine. NGS is facilitating the identification of genetic variations that impact diagnosis, treatment, and disease understanding affecting many millions of people worldwide. NGS is not limited to human genomics but does also have a major impact in the fields of plant, animal, and pathogen research.
NGS studies such as whole-exome sequencing, whole-genome sequencing, and RNA-seq (transcriptome) together with multi-omics approaches, have become increasingly more feasible given the availability of technology and the significant reduction in costs in recent years. One of the substantial challenges presented with NGS studies is that very large numbers of variants (tens of thousands for example) are often identified. This can make harnessing the potential of NGS difficult when it comes to analyzing the data.
Due to a large amount of data, a frequently employed approach in NGS studies is to identify a manageable subset of genomic variations that can be used to spread light on the biological underpinning of the phenotypes of interest. This list is obtained through variant filtering and prioritization. Variant filtering aims to identify high-quality variant calls removing false positives, whereas variant prioritization aims to identify phenotype-associated or causal variants. For both variant filtering and prioritization steps, several public and/or private annotation databases are typically consulted in a single study.
This research topic addresses the challenge of how to integrate the vast array of annotation resources available in the filtering and prioritization steps of NGS studies, taking into account the diversity of potential applications, ranging from routine diagnoses of genetic diseases to prioritization of novel variants in genes with unknown function. Computational, artificial intelligence, machine learning, statistical modeling, and simpler approaches can all offer potential solutions for this integration depending on the context.
This Research Topic welcomes Original Research, Review, and Perspective articles in the following (but not limited) research areas:
• Annotations (e.g., minor allele frequency, haplotypes, pedigree, impact on protein function, phylogenetic conservation, prior evidence of pathogenicity, etc.) and novel annotation tools
•Gene ranking strategies for variant prioritization
• Artificial intelligence, including machine learning, for the integration of annotation resources in NGS studies
• Statistical models for the integration of annotation resources in NGS studies
• Comparison of approaches for the integration of annotation resources in NGS studies
• Novel integrated annotation workflows or novel applications of existing methods to real or synthetic data for variant filtering and/or prioritization
Next-Generation Sequencing (NGS) studies are already proving to be a powerful tool in the field of human health and precision medicine. NGS is facilitating the identification of genetic variations that impact diagnosis, treatment, and disease understanding affecting many millions of people worldwide. NGS is not limited to human genomics but does also have a major impact in the fields of plant, animal, and pathogen research.
NGS studies such as whole-exome sequencing, whole-genome sequencing, and RNA-seq (transcriptome) together with multi-omics approaches, have become increasingly more feasible given the availability of technology and the significant reduction in costs in recent years. One of the substantial challenges presented with NGS studies is that very large numbers of variants (tens of thousands for example) are often identified. This can make harnessing the potential of NGS difficult when it comes to analyzing the data.
Due to a large amount of data, a frequently employed approach in NGS studies is to identify a manageable subset of genomic variations that can be used to spread light on the biological underpinning of the phenotypes of interest. This list is obtained through variant filtering and prioritization. Variant filtering aims to identify high-quality variant calls removing false positives, whereas variant prioritization aims to identify phenotype-associated or causal variants. For both variant filtering and prioritization steps, several public and/or private annotation databases are typically consulted in a single study.
This research topic addresses the challenge of how to integrate the vast array of annotation resources available in the filtering and prioritization steps of NGS studies, taking into account the diversity of potential applications, ranging from routine diagnoses of genetic diseases to prioritization of novel variants in genes with unknown function. Computational, artificial intelligence, machine learning, statistical modeling, and simpler approaches can all offer potential solutions for this integration depending on the context.
This Research Topic welcomes Original Research, Review, and Perspective articles in the following (but not limited) research areas:
• Annotations (e.g., minor allele frequency, haplotypes, pedigree, impact on protein function, phylogenetic conservation, prior evidence of pathogenicity, etc.) and novel annotation tools
•Gene ranking strategies for variant prioritization
• Artificial intelligence, including machine learning, for the integration of annotation resources in NGS studies
• Statistical models for the integration of annotation resources in NGS studies
• Comparison of approaches for the integration of annotation resources in NGS studies
• Novel integrated annotation workflows or novel applications of existing methods to real or synthetic data for variant filtering and/or prioritization
