Annotation and query of tissue microarray data using the NCI Thesaurus

<p>Abstract</p> <p>Background</p> <p>The Stanford Tissue Microarray Database (TMAD) is a repository of data serving a consortium of pathologists and biomedical researchers. The tissue samples in TMAD are annotated with multiple free-text fields, specifying the pathological diagnoses for each sample. These text annotations are not structured according to any ontology, making future integration of this resource with other biological and clinical data difficult.</p> <p>Results</p> <p>We developed methods to map these annotations to the NCI thesaurus. Using the NCI-T we can effectively represent annotations for about 86% of the samples. We demonstrate how this mapping enables ontology driven integration and querying of tissue microarray data. We have deployed the mapping and ontology driven querying tools at the TMAD site for general use.</p> <p>Conclusion</p> <p>We have demonstrated that we can effectively map the diagnosis-related terms describing a sample in TMAD to the NCI-T. The NCI thesaurus terms have a wide coverage and provide terms for about 86% of the samples. In our opinion the NCI thesaurus can facilitate integration of this resource with other biological data.</p

Ontology-driven indexing of public datasets for translational bioinformatics

The volume of publicly available genomic scale data is increasing. Genomic datasets in public repositories are annotated with free-text fields describing the pathological state of the studied sample. These annotations are not mapped to concepts in any ontology, making it difficult to integrate these datasets across repositories. We have previously developed methods to map text-annotations of tissue microarrays to concepts in the NCI thesaurus and SNOMED-CT

Semantic Web repositories for genomics data using the eXframe platform

Background: With the advent of inexpensive assay technologies, there has been an unprecedented growth in genomics data as well as the number of databases in which it is stored. In these databases, sample annotation using ontologies and controlled vocabularies is becoming more common. However, the annotation is rarely available as Linked Data, in a machine-readable format, or for standardized queries using SPARQL. This makes large-scale reuse, or integration with other knowledge bases very difficult. Methods: To address this challenge, we have developed the second generation of our eXframe platform, a reusable framework for creating online repositories of genomics experiments. This second generation model now publishes Semantic Web data. To accomplish this, we created an experiment model that covers provenance, citations, external links, assays, biomaterials used in the experiment, and the data collected during the process. The elements of our model are mapped to classes and properties from various established biomedical ontologies. Resource Description Framework (RDF) data is automatically produced using these mappings and indexed in an RDF store with a built-in Sparql Protocol and RDF Query Language (SPARQL) endpoint. Conclusions: Using the open-source eXframe software, institutions and laboratories can create Semantic Web repositories of their experiments, integrate it with heterogeneous resources and make it interoperable with the vast Semantic Web of biomedical knowledge

Semantic web data warehousing for caGrid

The National Cancer Institute (NCI) is developing caGrid as a means for sharing cancer-related data and services. As more data sets become available on caGrid, we need effective ways of accessing and integrating this information. Although the data models exposed on caGrid are semantically well annotated, it is currently up to the caGrid client to infer relationships between the different models and their classes. In this paper, we present a Semantic Web-based data warehouse (Corvus) for creating relationships among caGrid models. This is accomplished through the transformation of semantically-annotated caBIG® Unified Modeling Language (UML) information models into Web Ontology Language (OWL) ontologies that preserve those semantics. We demonstrate the validity of the approach by Semantic Extraction, Transformation and Loading (SETL) of data from two caGrid data sources, caTissue and caArray, as well as alignment and query of those sources in Corvus. We argue that semantic integration is necessary for integration of data from distributed web services and that Corvus is a useful way of accomplishing this. Our approach is generalizable and of broad utility to researchers facing similar integration challenges

Evaluation of a Large-Scale Biomedical Data Annotation Initiative

Background: This study describes a large-scale manual re-annotation of data samples in the Gene Expression Omnibus (GEO), using variables and values derived from the National Cancer Institute thesaurus. A framework is described for creating an annotation scheme for various diseases that is flexible, comprehensive, and scalable. The annotation structure is evaluated by measuring coverage and agreement between annotators. Results: There were 12,500 samples annotated with approximately 30 variables, in each of six disease categories – breast cancer, colon cancer, inflammatory bowel disease (IBD), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and Type 1 diabetes mellitus (DM). The annotators provided excellent variable coverage, with known values for over 98% of three critical variables: disease state, tissue, and sample type. There was 89% strict inter-annotator agreement and 92% agreement when using semantic and partial similarity measures. Conclusion: We show that it is possible to perform manual re-annotation of a large repository in a reliable manner

Biomedical data retrieval utilizing textual data in a gene expression database by Richard Lu, MD.

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 68-74).Background: The commoditization of high-throughput gene expression sequencing and microarrays has led to a proliferation in both the amount of genomic and clinical data that is available. Descriptive textual information deposited with gene expression data in the Gene Expression Omnibus (GEO) is an underutilized resource because the textual information is unstructured and difficult to query. Rendering this information in a structured format utilizing standard medical terms would facilitate better searching and data reuse. Such a procedure would significantly increase the clinical utility of biomedical data repositories. Methods: The thesis is divided into two sections. The first section compares how well four medical terminologies were able to represent textual information deposited in GEO. The second section implements free-text search and faceted search and evaluates how well they are able to answer clinical queries with varying levels of complexity. Part I: 120 samples were randomly extracted from samples deposited in the GEO database from six clinical domains-breast cancer, colon cancer, rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), type I diabetes mellitus (IDDM), and asthma. These samples were previously annotated manually and structured textual information was obtained in a tag:value format. Data was mapped to four different controlled terminologies: NCI Thesaurus, MeSH, SNOMED-CT, and ICD- 10. The samples were assigned a score on a three-point scale that was based on how well the terminology was able to represent descriptive textual information. Part II: Faceted and free-text search tools were implemented, with 300 GEO samples included for querying. Eight natural language search questions were selected randomly from scientific journals. Academic researchers were recruited and asked to use the faceted and free-text search tools to locate samples matching the question criteria. Precision, recall, F-score, and search time were compared and analyzed for both free-text and faceted search. Results: The results show that the NCI Thesaurus consistently ranked as the most comprehensive terminology across all domains while ICD-10 consistently ranked as the least comprehensive. Using NCI Thesaurus to augment the faceted search tool, each researcher was able to reach 100% precision and recall (F-score 1.0) for each of the eight search questions. Using free-text search, test users averaged 22.8% precision, 60.7% recall, and an F-score of 0.282. The mean search time per question using faceted search and free-text search were 116.7 seconds, and 138.4 seconds, respectively. The difference between search time was not statistically significant (p=0. 734). However, paired t-test analysis showed a statistically signficant difference between the two search strategies with respect to precision (p=O.001), recall (p=O.042), and F-score (p<0. 001). Conclusion: This work demonstrates that biomedical terms included in a gene expression database can be adequately expressed using the NCI Thesaurus. It also shows that faceted searching using a controlled terminology is superior to conventional free-text searching when answering queries of varying levels of complexity.S.M

Microarray meta-analysis database (M2DB): a uniformly pre-processed, quality controlled, and manually curated human clinical microarray database

<p>Abstract</p> <p>Background</p> <p>Over the past decade, gene expression microarray studies have greatly expanded our knowledge of genetic mechanisms of human diseases. Meta-analysis of substantial amounts of accumulated data, by integrating valuable information from multiple studies, is becoming more important in microarray research. However, collecting data of special interest from public microarray repositories often present major practical problems. Moreover, including low-quality data may significantly reduce meta-analysis efficiency.</p> <p>Results</p> <p>M²DB is a human curated microarray database designed for easy querying, based on clinical information and for interactive retrieval of either raw or uniformly pre-processed data, along with a set of quality-control metrics. The database contains more than 10,000 previously published Affymetrix GeneChip arrays, performed using human clinical specimens. M²DB allows online querying according to a flexible combination of five clinical annotations describing disease state and sampling location. These annotations were manually curated by controlled vocabularies, based on information obtained from GEO, ArrayExpress, and published papers. For array-based assessment control, the online query provides sets of QC metrics, generated using three available QC algorithms. Arrays with poor data quality can easily be excluded from the query interface. The query provides values from two algorithms for gene-based filtering, and raw data and three kinds of pre-processed data for downloading.</p> <p>Conclusion</p> <p>M²DB utilizes a user-friendly interface for QC parameters, sample clinical annotations, and data formats to help users obtain clinical metadata. This database provides a lower entry threshold and an integrated process of meta-analysis. We hope that this research will promote further evolution of microarray meta-analysis.</p