CDAO-Store: Ontology-driven Data Integration for Phylogenetic Analysis

Chisham, Brandon; Wright, Ben; Le, Hieu Giang; Son, Tran Cao; Pontelli, Enrico

doi:10.1186/1471-2105-12-98

Cited by 20 publications

(13 citation statements)

References 19 publications

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“…While a large number of phylogenetic analysis tools and applications have been developed [2,5,8,21,38,58,60], very few query languages are available for phylogenetic databases. Many of these tools are focused on generating phylogenies or using phylogenies already collected in specific formats.…”

Section: Related Researchmentioning

confidence: 99%

“…Databases such as TreeBASE [55] and Phy-loExplorer [38] support custom APIs to search phylogenies in specific formats that are not actually based on tree query languages, and are often based on tree matching algorithms [42]. There has been previous efforts in developing query languages, declarative languages to be specific, for phylogenetic databases [8,18,22] based on the observations in [30,33] with varying degrees of success. The limitations of these languages are still forcing procedural extensions of languages such as Python and Java [9,15,36,48,50] for accessing and visualizing phylogenies and forcing the user to incur significant development costs.…”

Section: Related Researchmentioning

confidence: 99%

“…The interest in developing a flexible, expressive and efficient structure querying engine for phylogenetic databases has been gaining steady popularity [2,8,18,38,50,60,62].…”

Section: Introductionmentioning

confidence: 99%

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Pruning Forests to Find the Trees

Jamil

2016

Proceedings of the 28th International Conference on Scientific and Statistical Database Management

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The vast majority of phylogenetic databases do not support a declarative querying platform using which their contents can be flexibly and conveniently accessed. The template based query interfaces they support do not allow arbitrary speculative queries. While a small number of graph query languages such as XQuery, Cypher and GraphQL exist for computer savvy users, most are too general and complex to be useful for biologists, and too inefficient for large phylogeny querying. In this paper, we discuss a recently introduced visual query language, called PhyQL, that leverages phylogeny specific properties to support essential and powerful constructs for a large class of phylogentic queries. Its deductive reasoner based implementation offers opportunities for a wide range of pruning strategies to speed up processing using query specific optimization and thus making it suitable for large phylogeny querying. A hybrid optimization technique that exploits a set of indices and "graphlet" partitioning is discussed. A "fail soonest" strategy is used to avoid hopeless processing and is shown to produce dividends. CCS Concepts •Information systems → Query optimization; Query languages for non-relational engines; Query planning; Semi-structured data; Web interfaces; •Human-centered computing → Graphical user interfaces; Web-based interaction; Graph drawings; •Applied computing → Bioinformatics; Genomics; •Computing methodologies → Logic programming and answer set programming;

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Section: Related Researchmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%

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Pruning Forests to Find the Trees

Jamil

2016

Proceedings of the 28th International Conference on Scientific and Statistical Database Management

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“…CDAO-Store [15] is a computational tool that uses the Comparative Data Analysis Ontology to facilitate the storage and retrieval of phylogenic data. Borlawsky et al reports a proof of concept information retrieval tool called Research-IQ [16], which enables research to query heterogeneous datasets.…”

Section: Introductionmentioning

confidence: 99%

Computational framework to support integration of biomolecular and clinical data within a translational approach

et al. 2013

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BackgroundThe use of the knowledge produced by sciences to promote human health is the main goal of translational medicine. To make it feasible we need computational methods to handle the large amount of information that arises from bench to bedside and to deal with its heterogeneity. A computational challenge that must be faced is to promote the integration of clinical, socio-demographic and biological data. In this effort, ontologies play an essential role as a powerful artifact for knowledge representation. Chado is a modular ontology-oriented database model that gained popularity due to its robustness and flexibility as a generic platform to store biological data; however it lacks supporting representation of clinical and socio-demographic information.ResultsWe have implemented an extension of Chado – the Clinical Module - to allow the representation of this kind of information. Our approach consists of a framework for data integration through the use of a common reference ontology. The design of this framework has four levels: data level, to store the data; semantic level, to integrate and standardize the data by the use of ontologies; application level, to manage clinical databases, ontologies and data integration process; and web interface level, to allow interaction between the user and the system. The clinical module was built based on the Entity-Attribute-Value (EAV) model. We also proposed a methodology to migrate data from legacy clinical databases to the integrative framework. A Chado instance was initialized using a relational database management system. The Clinical Module was implemented and the framework was loaded using data from a factual clinical research database. Clinical and demographic data as well as biomaterial data were obtained from patients with tumors of head and neck. We implemented the IPTrans tool that is a complete environment for data migration, which comprises: the construction of a model to describe the legacy clinical data, based on an ontology; the Extraction, Transformation and Load (ETL) process to extract the data from the source clinical database and load it in the Clinical Module of Chado; the development of a web tool and a Bridge Layer to adapt the web tool to Chado, as well as other applications.ConclusionsOpen-source computational solutions currently available for translational science does not have a model to represent biomolecular information and also are not integrated with the existing bioinformatics tools. On the other hand, existing genomic data models do not represent clinical patient data. A framework was developed to support translational research by integrating biomolecular information coming from different “omics” technologies with patient’s clinical and socio-demographic data. This framework should present some features: flexibility, compression and robustness. The experiments accomplished from a use case demonstrated that the proposed system meets requirements of flexibility and robustness, leading to the desired integration. The Clinical Module can be ...

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“…2006, Page, R.D.M. 2007, Anwar, N. and Hunt, E. 2009, Ranwez, V., Clairon, N., et al 2009) and topological querying or browsing (Shan, H., Herbert, K.G., et al 2002, Wang, J.T., Shan, H., et al 2003, Wang, J.T., Shan, H., et al 2005, Chevenet, F., Brun, C., et al 2006, Chen, D., Burleigh, J.G., et al 2008, Hossain, S., Islam, M., et al 2008, Chisham, B., Wright, B., et al 2011, Le, T., Nguyen, H., et al 2012). Finally, TreeBASE, supplemented with data from Dryad (White, H., Carrier, S., et al 2008) served as the core source of data for Open Tree of Life, a platform for fusing phylogenetic trees with taxonomic classifications to produce a comprehensive tree of life (Hinchliff, C.E., Smith, S.A., et al 2015).…”

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confidence: 99%

TreeBASEdmp: A Toolkit for Phyloinformatic Research

Piel

Vos

2018

Preprint

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Over the last 20 years, TreeBASE has acquired a substantial body of phylogenetic data, including more than 20,000 published phylogenies. Given latency issues and limited options when it comes to querying the database remotely, a simplified and consolidated version of the database, here called TreeBASEdmp, is made available for download, allowing biologists to design custom analyses of the data on their local computers.The database is indexed to support searching for phylogenetic topologies using nested sets and closure tables. Here we propose a new approach to find broadly-defined phylogenetic patterns, a method we call Generic Topological Querying, which allows the user to find hypotheses of relationship without being constrained to use particular sets of specific taxa.Additionally, we normalize as many leaf nodes as possible to an equivalent species rank identifier to assist in supertree synthesis. Our example script rapidly assembles sets of trees and generates a matrix representation of them for subsequent supertree generation.Growth in phylogenetic data is akin to Moore's Law, being the product of faster computers, speedier DNA sequencers, and more efficient algorithms. The result is near-exponential growth in phylogenetic knowledge (Sanderson, M.J., Baldwin, B., et al. 1993, Pagel, M. 1997, Pagel, M. 1999, Page, R.D.M. 2005, Parr, C.S., Guralnick, R., et al. 2012, with the promise that subsequent synthesis and meta-analysis of these data will bring forth a "new age of discovery," among other benefits (Donoghue, M.

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CDAO-Store: Ontology-driven Data Integration for Phylogenetic Analysis

Cited by 20 publications

References 19 publications

Pruning Forests to Find the Trees

Pruning Forests to Find the Trees

Computational framework to support integration of biomolecular and clinical data within a translational approach

TreeBASEdmp: A Toolkit for Phyloinformatic Research

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