Difference between revisions of "Template:Article of the week"

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(Updated article of the week text.)
(Updated article of the week text.)
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'''"[[Journal:Principles of metadata organization at the ENCODE data coordination center|Principles of metadata organization at the ENCODE data coordination center]]"'''
'''"[[Journal:Information technology support for clinical genetic testing within an academic medical center|Information technology support for clinical genetic testing within an academic medical center]]"'''


The Encyclopedia of DNA Elements (ENCODE) Data Coordinating Center (DCC) is responsible for organizing, describing and providing access to the diverse data generated by the ENCODE project. The description of these data, known as metadata, includes the biological sample used as input, the protocols and assays performed on these samples, the data files generated from the results and the computational methods used to analyze the data. Here, we outline the principles and philosophy used to define the ENCODE metadata in order to create a metadata standard that can be applied to diverse assays and multiple genomic projects. In addition, we present how the data are validated and used by the ENCODE DCC in creating the ENCODE Portal. ('''[[Journal:Principles of metadata organization at the ENCODE data coordination center|Full article...]]''')<br />
Academic medical centers require many interconnected systems to fully support genetic testing processes. We provide an overview of the end-to-end support that has been established surrounding a genetic testing laboratory within our environment, including both [[laboratory]] and clinician-facing infrastructure. We explain key functions that we have found useful in the supporting systems. We also consider ways that this infrastructure could be enhanced to enable deeper assessment of genetic test results in both the laboratory and clinic. ('''[[Journal:Information technology support for clinical genetic testing within an academic medical center|Full article...]]''')<br />
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''Recently featured'':  
''Recently featured'':  
: ▪ [[Journal:Principles of metadata organization at the ENCODE data coordination center|Principles of metadata organization at the ENCODE data coordination center]]
: ▪ [[Journal:Integrated systems for NGS data management and analysis: Open issues and available solutions|Integrated systems for NGS data management and analysis: Open issues and available solutions]]
: ▪ [[Journal:Integrated systems for NGS data management and analysis: Open issues and available solutions|Integrated systems for NGS data management and analysis: Open issues and available solutions]]
: ▪ [[Journal:Practical approaches for mining frequent patterns in molecular datasets|Practical approaches for mining frequent patterns in molecular datasets]]
: ▪ [[Journal:Practical approaches for mining frequent patterns in molecular datasets|Practical approaches for mining frequent patterns in molecular datasets]]
: ▪ [[Journal:Improving the creation and reporting of structured findings during digital pathology review|Improving the creation and reporting of structured findings during digital pathology review]]

Revision as of 20:31, 13 November 2016

Fig1 Aronson JournalOfPersMed2016 6-1.png

"Information technology support for clinical genetic testing within an academic medical center"

Academic medical centers require many interconnected systems to fully support genetic testing processes. We provide an overview of the end-to-end support that has been established surrounding a genetic testing laboratory within our environment, including both laboratory and clinician-facing infrastructure. We explain key functions that we have found useful in the supporting systems. We also consider ways that this infrastructure could be enhanced to enable deeper assessment of genetic test results in both the laboratory and clinic. (Full article...)

Recently featured:

▪ Principles of metadata organization at the ENCODE data coordination center
▪ Integrated systems for NGS data management and analysis: Open issues and available solutions
▪ Practical approaches for mining frequent patterns in molecular datasets
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