MitoAge: a database for comparative analysis of mitochondrial DNA, with a special focus on animal longevity

Toren, Dmitri; Barzilay, Thomer; Tăcutu, Robi; Lehmann, Gilad; Muradian, Khachik K.; Fraifeld, Vadim E.

doi:10.1093/nar/gkv1187

Cited by 25 publications

(14 citation statements)

References 21 publications

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“…For example, one large-scale study using data from AnAge investigated how multiple ecological and mode-of-life traits affect lifespan ( 19 ). The data from AnAge have also been incorporated into the Comparative Cellular and Molecular Biology of Longevity Database ( http://genomics.brocku.ca/ccmbl/ ) ( 20 ), the MitoAge database ( http://www.mitoage.info ) ( 21 ), the Encyclopedia of Life ( http://eol.org/ ), and the Animal Diversity Web ( http://animaldiversity.ummz.umich.edu ), demonstrating the versatility of this resource.…”

Section: Database Contentmentioning

confidence: 99%

Human Ageing Genomic Resources: new and updated databases

Tăcutu

Thornton

Johnson

et al. 2017

Nucleic Acids Research

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In spite of a growing body of research and data, human ageing remains a poorly understood process. Over 10 years ago we developed the Human Ageing Genomic Resources (HAGR), a collection of databases and tools for studying the biology and genetics of ageing. Here, we present HAGR’s main functionalities, highlighting new additions and improvements. HAGR consists of six core databases: (i) the GenAge database of ageing-related genes, in turn composed of a dataset of >300 human ageing-related genes and a dataset with >2000 genes associated with ageing or longevity in model organisms; (ii) the AnAge database of animal ageing and longevity, featuring >4000 species; (iii) the GenDR database with >200 genes associated with the life-extending effects of dietary restriction; (iv) the LongevityMap database of human genetic association studies of longevity with >500 entries; (v) the DrugAge database with >400 ageing or longevity-associated drugs or compounds; (vi) the CellAge database with >200 genes associated with cell senescence. All our databases are manually curated by experts and regularly updated to ensure a high quality data. Cross-links across our databases and to external resources help researchers locate and integrate relevant information. HAGR is freely available online (http://genomics.senescence.info/).

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Section: Database Contentmentioning

confidence: 99%

Human Ageing Genomic Resources: new and updated databases

Tăcutu

Thornton

Johnson

et al. 2017

Nucleic Acids Research

Self Cite

542

572

View full text Add to dashboard Cite

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“…A database containing mtDNA data integrated with longevity records, and also includes tools for analysis of mtDNA (with a focus on this in relation to longevity). This is significant because mitochondria are believed to play a major role in ageing and age related diseases, as it has been shown that there are correlations between the mtDNA sequence and the maximum lifespan of mammals (Toren et al, 2016). Therefore, this tool may be of use in the research of longevity.…”

Section: Online Mitochondrial Resourcesmentioning

confidence: 99%

“…As discussed previously, resources such as MitoCarta 2.0 (Calvo et al, 2016), MITOMAP (Lott et al, 2013), and PhyloTree mt (Van Oven and Kayser, 2009), are databases which do not include analytical tools, and so may be the most appropriate resource for users who require a reference database only. Resources such as HmtDB (Rubino et al, 2012), Mitobreak (Damas et al, 2014), MSeqDr (MSeqDR Consortium, 2019), and MitoAge (Toren et al, 2016), include analytical tools and so would be more appropriate for users who wish to analyse data.…”

Section: Applications Of the Resources Discussedmentioning

confidence: 99%

“Mitochondrial Toolbox” – A Review of Online Resources to Explore Mitochondrial Genomics

et al. 2020

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Mitochondria play a significant role in many biological systems. There is emerging evidence that differences in the mitochondrial genome may contribute to multiple common diseases, leading to an increasing number of studies exploring mitochondrial genomics. There is often a large amount of complex data generated (for example via next generation sequencing), which requires optimised bioinformatics tools to efficiently and effectively generate robust outcomes from these large datasets. Twenty-four online resources dedicated to mitochondrial genomics were reviewed. This 'mitochondrial toolbox' summary resource will enable researchers to rapidly identify the resource(s) most suitable for their needs. These resources fulfil a variety of functions, with some being highly specialised. No single tool will provide all users with the resources they require; therefore, the most suitable tool will vary between users depending on the nature of the work they aim to carry out. Genetics resources are well established for phylogeny and DNA sequence changes, but further epigenetic and gene expression resources need to be developed for mitochondrial genomics.

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“…Thanks to the accessibility of these data, most comparative analyses of actuarial senescence have relied on maximum longevity and looked for traits associated with difference in maximum longevity among species (e.g. Ma & Gladyshev, 2017;Minias & Podlaszczuk, 2017;Toren et al, 2016;Tricola et al, 2018).…”

Section: Metrics Of Longevitymentioning

confidence: 99%

Assessing ageing patterns for comparative analyses of mortality curves: Going beyond the use of maximum longevity

Ronget

Gaillard

2019

Functional Ecology

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1. An increasing number of studies have investigated the diversity of actuarial senescence (i.e. the increase of mortality with age) and mortality patterns in the wild. Most of these studies used maximum longevity as a metric, and only some of them were based on the analysis of mortality or survival curves. However, maximum longevity is not a reliable metric to assess mortality patterns. Two types of metrics can be distinguished: the metrics of pace that include biological times and describe how long individuals live and the metrics of shape that are dimensionless and describe the form of mortality patterns for a given pace.2. We review the use of pace metrics in comparative analyses of mortality curves and actuarial senescence performed so far, with a special focus on longevity metrics. We demonstrate that multiple statistical issues are associated with the use of maximum longevity, although this metric is the most commonly used to analyse actuarial senescence patterns in research fields such as genomics of ageing. We thus argue for using alternative metrics of longevity.3. We then propose different metrics of shape and point out the relevance of using several metrics in future comparative analyses of mortality. In particular, two shape metrics can be easily computed from observed distributions of ages at death. We illustrate our approach based on both pace and shape metrics by performing a comparative analysis of actuarial senescence and mortality across 30 species of mammals. The results strongly support the relevance of using shape metrics based on the distribution of the ages at death to assess reliably patterns of mortality. In particular, we found that the pace and the shape of ageing, although statistically independent, could be associated in mammals.4. We conclude that the metrics defined from the distribution of ages at death provide a complementary approach to mortality or survival curve analyses and, by offering straightforward standardization, provide a promising tool for future comparative analyses of actuarial senescence and mortality across the tree of life. K E Y W O R D Sageing, comparative analysis, demography, longevity, senescence 66 | Functional Ecology RONGET aNd GaILLaRd. Senescence:A general process involving the decrease of individual performance with increasing age.Actuarial senescence: The increase of mortality with age. Rate of actuarial senescence:The magnitude of increase of mortality rate with age.Onset of actuarial senescence: The age when actuarial senescence begins.Longevity: A metric measuring the lifetime of a given species, population or individual.Age-specific mortality rate: The probability to die at each age.Hazard of mortality (also called force of mortality µ): The instantaneous rate of mortality usually preferred over mortality rate in demographic analysis. Cumulative survival:The percentage of remaining individuals at each age from birth (100%) to one year beyond the oldest individual (0%). Gompertz model of mortality:A model commonly used that describes the mortality ...

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MitoAge: a database for comparative analysis of mitochondrial DNA, with a special focus on animal longevity

Cited by 25 publications

References 21 publications

Human Ageing Genomic Resources: new and updated databases

Human Ageing Genomic Resources: new and updated databases

“Mitochondrial Toolbox” – A Review of Online Resources to Explore Mitochondrial Genomics

Assessing ageing patterns for comparative analyses of mortality curves: Going beyond the use of maximum longevity

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