Integration and Querying of Genomic and Proteomic Semantic Annotations for Biomedical Knowledge Extraction

Masseroli, Marco; Canakoglu, Arif; Ceri, Stefano

doi:10.1109/tcbb.2015.2453944

Cited by 23 publications

(10 citation statements)

References 36 publications

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“…The data management backend has been produced in the last 2 years, capitalizing on several previous years of experience in the use of bio-ontologies for specific research projects (e.g. SOS-GeM (41) and GPKB (42)). The repository currently integrates about 40 million metadata items from five sources, described by 39 attributes over eight connected tables of the core schema and enriched with terms from eight different ontologies, which have been reduced to the same knowledge schema.…”

Section: Resultsmentioning

confidence: 99%

GenoSurf: metadata driven semantic search system for integrated genomic datasets

et al. 2019

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Many valuable resources developed by world-wide research institutions and consortia describe genomic datasets that are both open and available for secondary research, but their metadata search interfaces are heterogeneous, not interoperable and sometimes with very limited capabilities. We implemented GenoSurf, a multi-ontology semantic search system providing access to a consolidated collection of metadata attributes found in the most relevant genomic datasets; values of 10 attributes are semantically enriched by making use of the most suited available ontologies. The user of GenoSurf provides as input the search terms, sets the desired level of ontological enrichment and obtains as output the identity of matching data files at the various sources. Search is facilitated by drop-down lists of matching values; aggregate counts describing resulting files are updated in real time while the search terms are progressively added. In addition to the consolidated attributes, users can perform keyword-based searches on the original (raw) metadata, which are also imported; GenoSurf supports the interplay of attribute-based and keyword-based search through well-defined interfaces. Currently, GenoSurf integrates about 40 million metadata of several major valuable data sources, including three providers of clinical and experimental data (TCGA, ENCODE and Roadmap Epigenomics) and two sources of annotation data (GENCODE and RefSeq); it can be used as a standalone resource for targeting the genomic datasets at their original sources (identified with their accession IDs and URLs), or as part of an integrated query answering system for performing complex queries over genomic regions and metadata.

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Section: Resultsmentioning

confidence: 99%

GenoSurf: metadata driven semantic search system for integrated genomic datasets

et al. 2019

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“…A common approach in integrated data management is data warehousing, consisting of a-priori integration and reconciliation of data extracted from multiple sources, such as in EnsMart/BioMart [13,34]. Along this direction, [22] describes a warehouse for integrating genomic and proteomic information using generalization hierarchies and a modular, multilevel global schema to overcome differences among data sources. ER modeling (and UML class diagrams) were used in [5]; models describe protein structures and genomic sequences, with rather complex concepts aiming at completely representing the underlying biology.…”

Section: Related Workmentioning

confidence: 99%

Conceptual Modeling for Genomics: Building an Integrated Repository of Open Data

Bernasconi

Ceri

Campi

et al. 2017

Lecture Notes in Computer Science

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Many repositories of open data for genomics, collected by worldwide consortia, are important enablers of biological research; moreover, all experimental datasets leading to publications in genomics must be deposited to public repositories and made available to the research community. These datasets are typically used by biologists for validating or enriching their experiments; their content is documented by metadata. However, emphasis on data sharing is not matched by accuracy in data documentation; metadata are not standardized across the sources and often unstructured and incomplete. In this paper, we propose a conceptual model of genomic metadata, whose purpose is to query the underlying data sources for locating relevant experimental datasets. First, we analyze the most typical metadata attributes of genomic sources and define their semantic properties. Then, we use a top-down method for building a global-as-view integrated schema, by abstracting the most important conceptual properties of genomic sources. Finally, we describe the validation of the conceptual model by mapping it to three well-known data sources: TCGA, ENCODE, and Gene Expression Omnibus.

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“…We ran our prediction tests on the datasets of Gene Ontology annotations of Homo sapiens genes available in the Genomic and Proteomic Data Warehouse (GPDW) [29], [30], an integrated data resource publicly and freely available from Politecnico di Milano at http://www.bioinformatics.deib.polimi.it/GPKB/ which includes multiple versions of annotation datasets. We applied our prediction pipeline on the annotations of the July 2009 GPDW version and then validated the predicted annotations by looking for them in the March 2013 GPDW version [31].…”

Section: Prediction Pipeline and Datasetsmentioning

confidence: 99%

“…We applied our prediction pipeline on the annotations of the July 2009 GPDW version and then validated the predicted annotations by looking for them in the March 2013 GPDW version [31]. Despite the March 2013 not being the most updated GPDW version, we used it because it is one of the most stable and accurate versions recently delivered [29]. We chose the Homo sapiens gene annotations to the three GO sub-ontologies (Biological Process, Molecular Function, Cellular Component) because they include representative numbers of genes and GO terms.…”

Section: Prediction Pipeline and Datasetsmentioning

confidence: 99%

Novelty Indicator for Enhanced Prioritization of Predicted Gene Ontology Annotations

Chicco

Palluzzi

Masseroli

2018

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Biomolecular controlled annotations have become pivotal in computational biology, because they allow scientists to analyze large amounts of biological data to better understand test results, and to infer new knowledge. Yet, biomolecular annotation databases are incomplete by definition, like our knowledge of biology, and might contain errors and inconsistent information. In this context, machine-learning algorithms able to predict and prioritize new annotations are both effective and efficient, especially if compared with time-consuming trials of biological validation. To limit the possibility that these techniques predict obvious and trivial high-level features, and to help prioritize their results, we introduce a new element that can improve accuracy and relevance of the results of an annotation prediction and prioritization pipeline. We propose a novelty indicator able to state the level of "originality" of the annotations predicted for a specific gene to Gene Ontology (GO) terms. This indicator, joint with our previously introduced prediction steps, helps by prioritizing the most novel interesting annotations predicted. We performed an accurate biological functional analysis of the prioritized annotations predicted with high accuracy by our indicator and previously proposed methods. The relevance of our biological findings proves effectiveness and trustworthiness of our indicator and of its prioritization of predicted annotations.

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Integration and Querying of Genomic and Proteomic Semantic Annotations for Biomedical Knowledge Extraction

Cited by 23 publications

References 36 publications

GenoSurf: metadata driven semantic search system for integrated genomic datasets

GenoSurf: metadata driven semantic search system for integrated genomic datasets

Conceptual Modeling for Genomics: Building an Integrated Repository of Open Data

Novelty Indicator for Enhanced Prioritization of Predicted Gene Ontology Annotations

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