Abstract
With the advent of large-scale next-generation sequencing initiatives, there is an increasing importance to interpret and understand the potential phenotypic influence of identified genetic variation and its significance in the human genome. Bioinformatics analyses can provide useful information to assist with variant interpretation. This review provides an overview of tools/resources currently available, and how they can help predict the impact of genetic variation at the deoxyribonucleic acid, ribonucleic acid, and protein level.
Highlights
Clinical, diagnostic and research groups working in the field of hemostasis and thrombosis generate considerable data concerning genetic variation
This information has derived from targeted analysis of genes linked to a specific disease phenotype (e.g. investigating von Willebrand factor (VWF) in patients diagnosed with von Willebrand disease).[1]
Additional data derives from genomewide association studies (GWAS) aimed at identifying genetic loci that may influence plasma protein levels[2,3] or that are associated with a specific phenotype, e.g. coronary artery disease.[4,5]
Summary
Diagnostic and research groups working in the field of hemostasis and thrombosis generate considerable data concerning genetic variation This information has derived from targeted analysis of genes linked to a specific disease phenotype (e.g. investigating von Willebrand factor (VWF) in patients diagnosed with von Willebrand disease).[1] Additional data derives from genomewide association studies (GWAS) aimed at identifying genetic loci that may influence plasma protein levels[2,3] or that are associated with a specific phenotype, e.g. coronary artery disease.[4,5] The advent of generation sequencing (NGS) has increased the amount of genetic information obtained from targeted analysis[6,7,8] and is generating a wealth of information on genetic variation throughout the human genome.[9,10]. This review aims to provide an overview of the many free in silico tools and resources currently available online that can help clinicians / scientists predict the potential impact of genetic variants at the DNA, RNA and protein level, and assist with variant classification
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