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BigQ: a NoSQL based framework to handle genomic variants in i2b2.

Gabetta M, Limongelli I, Rizzo E, Riva A, Segagni D, Bellazzi R - BMC Bioinformatics (2015)

Bottom Line: A visual programming i2b2 plugin allows retrieving variants belonging to the patients in a cohort by applying filters on genomic variant annotations.In this paper we describe a new i2b2 web service composed of an efficient and scalable document-based database that manages annotations of genomic variants and of a visual programming plug-in designed to dynamically perform queries on clinical and genetic data.The system therefore allows managing the fast growing volume of genomic variants and can be used to integrate heterogeneous genomic annotations.

View Article: PubMed Central - PubMed

Affiliation: Dipartimento di Ingegneria Industriale e dell'Informazione and Center for Health Technologies, Università di Pavia, Pavia, Italy. matteo.gabetta@unipv.it.

ABSTRACT

Background: Precision medicine requires the tight integration of clinical and molecular data. To this end, it is mandatory to define proper technological solutions able to manage the overwhelming amount of high throughput genomic data needed to test associations between genomic signatures and human phenotypes. The i2b2 Center (Informatics for Integrating Biology and the Bedside) has developed a widely internationally adopted framework to use existing clinical data for discovery research that can help the definition of precision medicine interventions when coupled with genetic data. i2b2 can be significantly advanced by designing efficient management solutions of Next Generation Sequencing data.

Results: We developed BigQ, an extension of the i2b2 framework, which integrates patient clinical phenotypes with genomic variant profiles generated by Next Generation Sequencing. A visual programming i2b2 plugin allows retrieving variants belonging to the patients in a cohort by applying filters on genomic variant annotations. We report an evaluation of the query performance of our system on more than 11 million variants, showing that the implemented solution scales linearly in terms of query time and disk space with the number of variants.

Conclusions: In this paper we describe a new i2b2 web service composed of an efficient and scalable document-based database that manages annotations of genomic variants and of a visual programming plug-in designed to dynamically perform queries on clinical and genetic data. The system therefore allows managing the fast growing volume of genomic variants and can be used to integrate heterogeneous genomic annotations.

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Query time performances. Query times (Q1, Q2 and Q3) plotted against the increasing numbers of individuals (i.e. variants) in the database
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Fig6: Query time performances. Query times (Q1, Q2 and Q3) plotted against the increasing numbers of individuals (i.e. variants) in the database

Mentions: For each test we measured the average time necessary to run all three queries. Figure 6 and Table 1 show the results obtained, indicating that the query time is independent of the size of the database in the case of Q1, while it linearly scales with the size of the database in Q2 and Q3. It is interesting to note that with the proposed computational infrastructure the query time is almost instantaneous for the user in the case of Q1 (about 0.06 s), while querying more than 11 million variants (500 exomes) takes about 34 s. Since query flexibility is not provided by CouchDB, we have implemented a strategy to combine together the results from simple queries. As a consequence, a complex query (e.g. Q3) involving more than one variant attribute results in a longer query time due to the number of views to be searched (one for each attribute) and the add and/or filter operations to be performed in backend.Fig. 6


BigQ: a NoSQL based framework to handle genomic variants in i2b2.

Gabetta M, Limongelli I, Rizzo E, Riva A, Segagni D, Bellazzi R - BMC Bioinformatics (2015)

Query time performances. Query times (Q1, Q2 and Q3) plotted against the increasing numbers of individuals (i.e. variants) in the database
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4696314&req=5

Fig6: Query time performances. Query times (Q1, Q2 and Q3) plotted against the increasing numbers of individuals (i.e. variants) in the database
Mentions: For each test we measured the average time necessary to run all three queries. Figure 6 and Table 1 show the results obtained, indicating that the query time is independent of the size of the database in the case of Q1, while it linearly scales with the size of the database in Q2 and Q3. It is interesting to note that with the proposed computational infrastructure the query time is almost instantaneous for the user in the case of Q1 (about 0.06 s), while querying more than 11 million variants (500 exomes) takes about 34 s. Since query flexibility is not provided by CouchDB, we have implemented a strategy to combine together the results from simple queries. As a consequence, a complex query (e.g. Q3) involving more than one variant attribute results in a longer query time due to the number of views to be searched (one for each attribute) and the add and/or filter operations to be performed in backend.Fig. 6

Bottom Line: A visual programming i2b2 plugin allows retrieving variants belonging to the patients in a cohort by applying filters on genomic variant annotations.In this paper we describe a new i2b2 web service composed of an efficient and scalable document-based database that manages annotations of genomic variants and of a visual programming plug-in designed to dynamically perform queries on clinical and genetic data.The system therefore allows managing the fast growing volume of genomic variants and can be used to integrate heterogeneous genomic annotations.

View Article: PubMed Central - PubMed

Affiliation: Dipartimento di Ingegneria Industriale e dell'Informazione and Center for Health Technologies, Università di Pavia, Pavia, Italy. matteo.gabetta@unipv.it.

ABSTRACT

Background: Precision medicine requires the tight integration of clinical and molecular data. To this end, it is mandatory to define proper technological solutions able to manage the overwhelming amount of high throughput genomic data needed to test associations between genomic signatures and human phenotypes. The i2b2 Center (Informatics for Integrating Biology and the Bedside) has developed a widely internationally adopted framework to use existing clinical data for discovery research that can help the definition of precision medicine interventions when coupled with genetic data. i2b2 can be significantly advanced by designing efficient management solutions of Next Generation Sequencing data.

Results: We developed BigQ, an extension of the i2b2 framework, which integrates patient clinical phenotypes with genomic variant profiles generated by Next Generation Sequencing. A visual programming i2b2 plugin allows retrieving variants belonging to the patients in a cohort by applying filters on genomic variant annotations. We report an evaluation of the query performance of our system on more than 11 million variants, showing that the implemented solution scales linearly in terms of query time and disk space with the number of variants.

Conclusions: In this paper we describe a new i2b2 web service composed of an efficient and scalable document-based database that manages annotations of genomic variants and of a visual programming plug-in designed to dynamically perform queries on clinical and genetic data. The system therefore allows managing the fast growing volume of genomic variants and can be used to integrate heterogeneous genomic annotations.

Show MeSH