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- Aad, G., et al.
(författare)
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- 2010
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swepub:Mat__t (refereegranskat)
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| 2. |
- Brownstein, Catherine A., et al.
(författare)
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An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge
- 2014
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Ingår i: Genome Biology. - : Springer Science and Business Media LLC. - 1465-6906 .- 1474-760X. ; 15:3, s. R53-
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Tidskriftsartikel (refereegranskat)abstract
- Background: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. Results: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. Conclusions: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.
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| 4. |
- Hotchkiss, Erin, et al.
(författare)
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Modeling priming effects on microbial consumption of dissolved organic carbon in rivers
- 2014
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Ingår i: Journal of Geophysical Research - Biogeosciences. - 2169-8953 .- 2169-8961. ; 119:5, s. 982-995
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Tidskriftsartikel (refereegranskat)abstract
- Rivers receive and process large quantities of terrestrial dissolved organic carbon (DOC). Biologically available (unstable) DOC leached from primary producers may stimulate (i.e., prime) the consumption of more stable terrestrially derived DOC by heterotrophic microbes. We measured microbial DOC consumption (i.e., decay rates) from contrasting C sources in 10 rivers in the western and Midwestern United States using short-term bioassays of river water, soil and algal leachates, glucose, and commercial humate. We added inorganic nutrients (ammonium and phosphorus) to a subset of bioassays. We also amended a subset of river, soil, and commercial humate bioassays with glucose or algal leachates to test the hypothesis that unstable DOC primes consumption of more stable DOC. We used prior measurements of source-specific DOC bioavailability, linked with a Bayesian process model, to estimate means and posterior probability distributions for source-specific DOC decay rates in multisource bioassays. Modeled priming effects ranged from a -130 to +370% change in more stable DOC decay when incubated with unstable DOC. Glucose increased modeled river DOC decay by an average of 87% among all rivers. Glucose and algal leachates increased soil leachate and commercial humate decay by an average of 25% above background rates. Inorganic nutrient additions did not have consistent effects on DOC decay, likely because most of the study rivers had high ambient background nutrients. Our results demonstrate that the priming effect can augment DOC decay in rivers. In addition, Bayesian models can be used to estimate mechanisms driving aquatic ecosystem processes that are difficult to measure directly.
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