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Phenotype-Driven Plasma Biobanking Strategies and Methods.

Bowton EA, Collier SP, Wang X, Sutcliffe CB, Van Driest SL, Couch LJ, Herrera M, Jerome RN, Slebos RJ, Alborn WE, Liebler DC, McNaughton CD, Mernaugh RL, Wells QS, Brown NJ, Roden DM, Pulley JM - J Pers Med (2015)

Bottom Line: BioVU, Vanderbilt's DNA biorepository linked to de-identified clinical EMRs, has proven fruitful in its capacity to extensively appeal to numerous areas of biomedical and clinical research, supporting the discovery of genotype-phenotype interactions.Expanding on experiences in BioVU creation and development, we have recently embarked on a parallel effort to collect plasma in addition to DNA from blood specimens leftover after routine clinical testing at Vanderbilt.This initiative offers expanded utility of BioVU by combining proteomic and metabolomic approaches with genomics and/or clinical outcomes, widening the breadth for potential research and subsequent future impact on clinical care.

View Article: PubMed Central - PubMed

Affiliation: Institute for Clinical and Translational Research, Vanderbilt University, Nashville, TN 37203, USA. erica.a.bowton@vanderbilt.edu.

ABSTRACT
Biobank development and integration with clinical data from electronic medical record (EMR) databases have enabled recent strides in genomic research and personalized medicine. BioVU, Vanderbilt's DNA biorepository linked to de-identified clinical EMRs, has proven fruitful in its capacity to extensively appeal to numerous areas of biomedical and clinical research, supporting the discovery of genotype-phenotype interactions. Expanding on experiences in BioVU creation and development, we have recently embarked on a parallel effort to collect plasma in addition to DNA from blood specimens leftover after routine clinical testing at Vanderbilt. This initiative offers expanded utility of BioVU by combining proteomic and metabolomic approaches with genomics and/or clinical outcomes, widening the breadth for potential research and subsequent future impact on clinical care. Here, we describe the considerations and components involved in implementing a plasma biobank program from a feasibility assessment through pilot sample collection.

No MeSH data available.


Schema of BioVU plasma bioinformatics processes and procedures. BioVU plasma collection requires the integration of three main data infrastructures. Daily plasma phenotypes are run against an identified database to identify eligible subjects who meet pre-defined clinical criteria. This information is then incorporated into the de-identified database, and only those subjects who already have a DNA sample banked are flagged for plasma collection.
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jpm-05-00140-f002: Schema of BioVU plasma bioinformatics processes and procedures. BioVU plasma collection requires the integration of three main data infrastructures. Daily plasma phenotypes are run against an identified database to identify eligible subjects who meet pre-defined clinical criteria. This information is then incorporated into the de-identified database, and only those subjects who already have a DNA sample banked are flagged for plasma collection.

Mentions: Existing BioVU bioinformatics processes and procedures were leveraged to support plasma biobanking expansion, including a process of sample scanning at intake to determine sample inclusion (Figure 2). For plasma, the integration of a separate, identified database became necessary to determine plasma sample eligibility based on detailed clinical phenotype criteria available at the time of sample availability. The SD database could not be used for this step, as the de-identification process of the SD includes date-shifting, uncoupling the dates of relevant phenotype data from the actual date of sample collection. At the time of data analysis, both sample collection date(s) and all EMR data are shifted by the same number of days, but this process is not complete at the time of plasma sample selection. To operationalize the plasma inclusion protocol, pseudocode algorithms classifying plasma phenotypes are run over the entirety of the identified patient population daily, and eligible samples are indicated with a flag in the BioVU database. This approach does require significant manpower to incorporate and maintain the separate database with data pulls from multiple sources. Phenotype development, iteration testing for validation and writing code often requires several hours or even full days of effort. Moreover, maintenance of the identified and de-identified EMR databases, including clinical data integration and operational development, is the responsibility of a group of computer scientists within the Integrated Data Analyst Systems core facility.


Phenotype-Driven Plasma Biobanking Strategies and Methods.

Bowton EA, Collier SP, Wang X, Sutcliffe CB, Van Driest SL, Couch LJ, Herrera M, Jerome RN, Slebos RJ, Alborn WE, Liebler DC, McNaughton CD, Mernaugh RL, Wells QS, Brown NJ, Roden DM, Pulley JM - J Pers Med (2015)

Schema of BioVU plasma bioinformatics processes and procedures. BioVU plasma collection requires the integration of three main data infrastructures. Daily plasma phenotypes are run against an identified database to identify eligible subjects who meet pre-defined clinical criteria. This information is then incorporated into the de-identified database, and only those subjects who already have a DNA sample banked are flagged for plasma collection.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493492&req=5

jpm-05-00140-f002: Schema of BioVU plasma bioinformatics processes and procedures. BioVU plasma collection requires the integration of three main data infrastructures. Daily plasma phenotypes are run against an identified database to identify eligible subjects who meet pre-defined clinical criteria. This information is then incorporated into the de-identified database, and only those subjects who already have a DNA sample banked are flagged for plasma collection.
Mentions: Existing BioVU bioinformatics processes and procedures were leveraged to support plasma biobanking expansion, including a process of sample scanning at intake to determine sample inclusion (Figure 2). For plasma, the integration of a separate, identified database became necessary to determine plasma sample eligibility based on detailed clinical phenotype criteria available at the time of sample availability. The SD database could not be used for this step, as the de-identification process of the SD includes date-shifting, uncoupling the dates of relevant phenotype data from the actual date of sample collection. At the time of data analysis, both sample collection date(s) and all EMR data are shifted by the same number of days, but this process is not complete at the time of plasma sample selection. To operationalize the plasma inclusion protocol, pseudocode algorithms classifying plasma phenotypes are run over the entirety of the identified patient population daily, and eligible samples are indicated with a flag in the BioVU database. This approach does require significant manpower to incorporate and maintain the separate database with data pulls from multiple sources. Phenotype development, iteration testing for validation and writing code often requires several hours or even full days of effort. Moreover, maintenance of the identified and de-identified EMR databases, including clinical data integration and operational development, is the responsibility of a group of computer scientists within the Integrated Data Analyst Systems core facility.

Bottom Line: BioVU, Vanderbilt's DNA biorepository linked to de-identified clinical EMRs, has proven fruitful in its capacity to extensively appeal to numerous areas of biomedical and clinical research, supporting the discovery of genotype-phenotype interactions.Expanding on experiences in BioVU creation and development, we have recently embarked on a parallel effort to collect plasma in addition to DNA from blood specimens leftover after routine clinical testing at Vanderbilt.This initiative offers expanded utility of BioVU by combining proteomic and metabolomic approaches with genomics and/or clinical outcomes, widening the breadth for potential research and subsequent future impact on clinical care.

View Article: PubMed Central - PubMed

Affiliation: Institute for Clinical and Translational Research, Vanderbilt University, Nashville, TN 37203, USA. erica.a.bowton@vanderbilt.edu.

ABSTRACT
Biobank development and integration with clinical data from electronic medical record (EMR) databases have enabled recent strides in genomic research and personalized medicine. BioVU, Vanderbilt's DNA biorepository linked to de-identified clinical EMRs, has proven fruitful in its capacity to extensively appeal to numerous areas of biomedical and clinical research, supporting the discovery of genotype-phenotype interactions. Expanding on experiences in BioVU creation and development, we have recently embarked on a parallel effort to collect plasma in addition to DNA from blood specimens leftover after routine clinical testing at Vanderbilt. This initiative offers expanded utility of BioVU by combining proteomic and metabolomic approaches with genomics and/or clinical outcomes, widening the breadth for potential research and subsequent future impact on clinical care. Here, we describe the considerations and components involved in implementing a plasma biobank program from a feasibility assessment through pilot sample collection.

No MeSH data available.