The Price of Whole-Genome Sequencing May be Decreasing, But Who Will be Sequenced?

Marshall, Deborah A.; MacDonald, Karen V.; Robinson, Jill O.; Barcellos, Lisa F.; Gianfrancesco, Milena; Helm, Monica; McGuire, Amy L.; Green, Robert C; Douglas, Michael P.; Goldman, Michael A.; Phillips, Kathryn A.

doi:10.2217/pme-2016-0075

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…However, recent advances in the genomics field over the past quarter-century have led to considerable reductions in the sequencing costs. Since these methods are becoming frontlines in medical laboratories, it may be projected that this reduction to be continued in order to influence the scale and scope of research projects investigating genomic aspects of bacterial communities (Marshall et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Metatranscriptomic Analysis Reveals Active Bacterial Communities in Diabetic Foot Infections

et al. 2020

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Despite the extended view of the composition of diabetic foot infections (DFIs), little is known about which transcriptionally active bacterial communities are pertinent to infection, and if any differences are associated with increased infection severity. We applied a RNA sequencing approach to analyze the composition, function, and pathogenicity of the active bacterial communities in DFIs. Taxonomic profiling of bacterial transcripts revealed the presence of 14 bacterial phyla in DFIs. The abundance of the Spiroplasma, Vibrio, and Mycoplasma were significantly different in different infection severities (P < 0.05). Mild and severe stages of infections were dominated by Staphylococcus aureus and Porphyromonas asaccharolytica, respectively. A total of 132 metabolic pathways were identified of which ribosome and thiamin being among the most highly transcribed pathways. Moreover, a total of 131 antibiotic resistance genes, primarily involved in the multidrug efflux pumps/exporters, were identified. Furthermore, iron acquisition systems (synthesize and regulation of siderophores) and pathways involved in the synthesis and regulation of cell-surface components associated with adhesion, colonization, and movement of bacterial cells were the most common virulence factors. These virulence factors may help bacteria compete for scares resources and survive the host wound proteases. Characterization of transcriptionally active bacterial communities can help to provide an understanding of the role of key pathogens in the development of DFIs. Such information can be clinically useful allowing replacement of DFIs empirical therapy with targeted treatment.

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Section: Discussionmentioning

confidence: 99%

Metatranscriptomic Analysis Reveals Active Bacterial Communities in Diabetic Foot Infections

et al. 2020

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“…Author details 1 Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, WA, USA. 2 Institute of Translational Health Sciences, University of Washington, Seattle, WA, USA.…”

Section: Availability and Requirementsmentioning

confidence: 99%

“…Genetic testing is an essential tool to assist patients and clinicians to better understand the risk of hereditary disease. The cost of genetic testing has decreased and the number of genes routinely evaluated has increased in recent years, due to massively parallel sequencing methods and new discoveries [1,2]. Patients now have increased access to genetic information that can be important for their and their family's health.…”

Section: Introductionmentioning

confidence: 99%

ShareDNA: a smartphone app to facilitate family communication of genetic results

et al. 2021

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Background Genetic testing allows patients and clinicians to understand the risk of hereditary diseases. By testing early, individuals can make informed medical decisions about management which may minimize the risk of developing certain diseases. Importantly, genetic test results may also be applicable to patients’ biological relatives; thus, these results could also lead to minimizing their risk of disease. However, sharing genetic test results between patients and their relatives is scarce. The most frequently reported problems are that patients cannot clearly explain this information and relatives misinterpret the results. Smartphone apps in the healthcare field are a possible solution as they allow patients to accurately share sensitive information to others, while providing educational material to support understanding the information. However, these apps may not provide security to protect patients’ identifiable information. We developed ShareDNA, a smartphone app that (1) allows patients to securely share their genetic test results with others, (2) provides information on how to interpret these results, and (3) minimizes the amount of patient information needed to use the service. Results We recruited thirteen participants to test the usability of our app and provide feedback. We found overall that participants were comfortable with using this app and could easily learn each app function when filling out our questionnaire. Additionally, based on vocalized impressions of the usefulness of the app, participants indicated that the user-interface could be more intuitive and that we needed to add more text within the app to explain why ShareDNA is a secure service. Conclusions ShareDNA is a free smartphone app that allows patients to share their genetic test results with others, including their biological relatives. Sharing these results along with educational material will enable relatives to share accurate information and discuss their possible risk for disease with their clinical providers. As a result, appropriate testing in relatives could be improved.

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“…DNA sequencing has increased in throughput and decreased in cost (Gordon et al, 2020 ). This gives epigenetic ageing the potential to become a routine method in wildlife management (Frelinger, 2015 ; Marshall et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Calibrating epigenetic clocks with training data error

Mayne,

Berry,

Jarman

2023

Evolutionary Applications

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Animal age data are valuable for management of wildlife populations. Yet, for most species, there is no practical method for determining the age of unknown individuals. However, epigenetic clocks, a molecular‐based method, are capable of age prediction by sampling specific tissue types and measuring DNA methylation levels at specific loci. Developing an epigenetic clock requires a large number of samples from animals of known ages. For most species, there are no individuals whose exact ages are known, making epigenetic clock calibration inaccurate or impossible. For many epigenetic clocks, calibration samples with inaccurate age estimates introduce a degree of error to epigenetic clock calibration. In this study, we investigated how much error in the training data set of an epigenetic clock can be tolerated before it resulted in an unacceptable increase in error for age prediction. Using four publicly available data sets, we artificially increased the training data age error by iterations of 1% and then tested the model against an independent set of known ages. A small effect size increase (Cohen's d >0.2) was detected when the error in age was higher than 22%. The effect size increased linearly with age error. This threshold was independent of sample size. Downstream applications for age data may have a more important role in deciding how much error can be tolerated for age prediction. If highly precise age estimates are required, then it may be futile to embark on the development of an epigenetic clock when there is no accurately aged calibration population to work with. However, for other problems, such as determining the relative age order of pairs of individuals, a lower‐quality calibration data set may be adequate.

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The Price of Whole-Genome Sequencing May be Decreasing, But Who Will be Sequenced?

Cited by 8 publications

References 33 publications

Metatranscriptomic Analysis Reveals Active Bacterial Communities in Diabetic Foot Infections

Metatranscriptomic Analysis Reveals Active Bacterial Communities in Diabetic Foot Infections

ShareDNA: a smartphone app to facilitate family communication of genetic results

Calibrating epigenetic clocks with training data error

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