Muddled genetic terms miss and mess the message

Vihinen, Mauno

doi:10.1016/j.tig.2015.05.008

Cited by 17 publications

(14 citation statements)

References 11 publications

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“…They vary widely in their concept and implementation [Karchin, 2009;Thusberg and Vihinen, 2009;Capriotti et al, 2012]. Most predictors are available for amino acid substitutions that are often erroneously called as missense variations [Vihinen, 2015a]. The methods utilize evolutionary information (alone or together with other features) from MSAs to estimate the conservation of the variant position.…”

Section: Predictors For Amino Acid Substitutionsmentioning

confidence: 99%

Variation Interpretation Predictors: Principles, Types, Performance, and Choice

2016

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Next-generation sequencing methods have revolutionized the speed of generating variation information. Sequence data have a plethora of applications and will increasingly be used for disease diagnosis. Interpretation of the identified variants is usually not possible with experimental methods. This has caused a bottleneck that many computational methods aim at addressing. Fast and efficient methods for explaining the significance and mechanisms of detected variants are required for efficient precision/personalized medicine. Computational prediction methods have been developed in three areas to address the issue. There are generic tolerance (pathogenicity) predictors for filtering harmful variants. Gene/protein/disease-specific tools are available for some applications. Mechanism and effect-specific computer programs aim at explaining the consequences of variations. Here, we discuss the different types of predictors and their applications. We review available variation databases and prediction methods useful for variation interpretation. We discuss how the performance of methods is assessed and summarize existing assessment studies. A brief introduction is provided to the principles of the methods developed for variation interpretation as well as guidelines for how to choose the optimal tools and where the field is heading in the future.

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Section: Predictors For Amino Acid Substitutionsmentioning

confidence: 99%

Variation Interpretation Predictors: Principles, Types, Performance, and Choice

2016

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“…Many existing terms either have several meanings depending on the context and/or they refer to individuals, whereas the PM is for the population level. Unambiguous terms are a necessity for systematics; for problems with muddled terminology in genetics see Vihinen ().…”

Section: Components Of Pathogenicitymentioning

confidence: 99%

How to Define Pathogenicity, Health, and Disease?

Vihinen

2016

Human Mutation

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Scientific and clinical communities produce ever increasing amounts of data and details about health and disease. Our ability to understand and utilize this information is limited because of imprecise language and lack of well-defined concepts. This problem involves also the principal concepts of health, disease, and pathogenicity. Here, a systematic model is presented for pathogenicity, as well as for health and disease. It has three components: extent, modulation, and severity, which jointly define the continuum of pathogenicity. The model is population based, and once implemented, it can be used for numerous purposes such as diagnosis, patient stratification, prognosis, finding phenotype-genotype correlations, or explaining adverse drug reactions. The new model has several benefits including health economy by allowing evidence-based personalized/precision medicine.

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“…Robust curation of LSDBs demands that variant reporting be accurate and that incorrect variant descriptions reports be fixed. This is especially important in the context of clinical reports where misreporting might result in disastrous outcomes for patients [Ogino et al, 2007;Richards et al, 2015;Vihinen, 2015]. Mutalyzer (https://mutalyzer.nl/) [Wildeman et al, 2008;den Dunnen et al, 2016] is a program suite which supports the checking of sequence variants according to the HGVS nomenclature.…”

Section: Variant Nomenclature Validationmentioning

confidence: 99%

LSDBs and How They Have Evolved

Dalgleish

2016

Human Mutation

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Locus specific databases (LSDBs) make a key contribution to our understanding of heritable and acquired human disorders, disease susceptibility, and adverse drug reactions. As data have accumulated in LSDBs, a greater reliance on their use has arisen in clinical practice. Even though LSDBs have existed in recognizable form for only a quarter of a century, their origin lies in the manual cataloging of data that began around 50 years ago. Analysis and recording of sequence variation in the globin genes, and the proteins which they encode, can confidently be said to be the foundation for what we now refer to as LSDBs. Their growth over the years has primarily been underpinned by software developments and the advent of the World Wide Web. However, it is also important to recognize the evolution of reporting standards and reference sequences, without which accurate and consistent reporting of sequence variants would be impossible. Nowadays, LSDBs exist for many human protein-coding genes and the focus of efforts has moved toward minor tidying up of the variant reporting nomenclature and processes for assuring the completeness, correctness, and consistency of the data. The next 25 years will doubtless witness further developments in the evolution of LSDBs.

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Muddled genetic terms miss and mess the message

Cited by 17 publications

References 11 publications

Variation Interpretation Predictors: Principles, Types, Performance, and Choice

Variation Interpretation Predictors: Principles, Types, Performance, and Choice

How to Define Pathogenicity, Health, and Disease?

LSDBs and How They Have Evolved

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