Dataset Information

Humanizing Big Data: Recognizing the Human Aspect of Big Data.

ABSTRACT: The term "big data" refers broadly to large volumes of data, often gathered from several sources, that are then analyzed, for example, for predictive analytics. Combining and mining genetic data from varied sources including clinical genetic testing, for example, electronic health records, what might be termed as "recreational" genetic testing such as ancestry testing, as well as research studies, provide one type of "big data." Challenges and cautions in analyzing big data include recognizing the lack of systematic collection of the source data, the variety of assay technologies used, the potential variation in classification and interpretation of genetic variants. While advanced technologies such as microarrays and, more recently, next-generation sequencing, that enable testing an individual's DNA for thousands of genes and variants simultaneously are briefly discussed, attention is focused more closely on challenges to analysis of the massive data generated by these genomic technologies. The main theme of this review is to evaluate challenges associated with big data in general and specifically to bring the sophisticated technology of genetic/genomic testing down to the individual level, keeping in mind the human aspect of the data source and considering where the impact of the data will be translated and applied. Considerations in this "humanizing" process include providing adequate counseling and consent for genetic testing in all settings, as well as understanding the strengths and limitations of assays and their interpretation.

SUBMITTER: Helzlsouer K

PROVIDER: S-EPMC7082327 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Humanizing Big Data: Recognizing the Human Aspect of Big Data.

Helzlsouer Kathy K Meerzaman Daoud D Taplin Stephen S Dunn Barbara K BK

Frontiers in oncology 20200313

The term "big data" refers broadly to large volumes of data, often gathered from several sources, that are then analyzed, for example, for predictive analytics. Combining and mining genetic data from varied sources including clinical genetic testing, for example, electronic health records, what might be termed as "recreational" genetic testing such as ancestry testing, as well as research studies, provide one type of "big data." Challenges and cautions in analyzing big data include recognizing t ...[more]

PMID: 32231993

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Project description:BackgroundWe study Phylotree, a comprehensive representation of the phylogeny of global human mitochondrial DNA (mtDNA) variations, to better understand the mtDNA substitution mechanism and its most influential factors. We consider a substitution model, where a set of genetic features may predict the rate at which mtDNA substitutions occur. To find an appropriate model, an exhaustive analysis on the effect of multiple factors on the substitution rate is performed through Negative Binomial and Poisson regressions. We examine three different inclusion options for each categorical factor: omission, inclusion as an explanatory variable, and by-value partitioning. The examined factors include genes, codon position, a CpG indicator, directionality, nucleotide, amino acid, codon, and context (neighboring nucleotides), in addition to other site based factors. Partitioning a model by a factor's value results in several sub-models (one for each value), where the likelihoods of the sub-models can be combined to form a score for the entire model. Eventually, the leading models are considered as viable candidates for explaining mtDNA substitution rates.ResultsInitially, we introduce a novel clustering technique on genes, based on three similarity tests between pairs of genes, supporting previous results regarding gene functionalities in the mtDNA. These clusters are then used as a factor in our models. We present leading models for the protein coding genes, rRNA and tRNA genes and the control region, showing it is disadvantageous to separate the models of transitions/transversions, or synonymous/non-synonymous substitutions. We identify a context effect that cannot be attributed solely to protein level constraints or CpG pairs. For protein-coding genes, we show that the substitution model should be partitioned into sub-models according to the codon position and input codon; additionally we confirm that gene identity and cluster have no significant effect once the above factors are accounted for.ConclusionsWe leverage the large, high-confidence Phylotree mtDNA phylogeny to develop a new statistical approach. We model the substitution rates using regressions, allowing consideration of many factors simultaneously. This admits the use of model selection tools helping to identify the set of factors best explaining the mutational dynamics when considered in tandem.

Dataset Information

Humanizing Big Data: Recognizing the Human Aspect of Big Data.

Publications

Humanizing Big Data: Recognizing the Human Aspect of Big Data.

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