Federated ontology-based queries over cancer data

BACKGROUND: Personalised medicine provides patients with treatments that are specific to their genetic profiles. It requires efficient data sharing of disparate data types across a variety of scientific disciplines, such as molecular biology, pathology, radiology and clinical practice. Personalised medicine aims to offer the safest and most effective therapeutic strategy based on the gene variations of each subject. In particular, this is valid in oncology, where knowledge about genetic mutations has already led to new therapies. Current molecular biology techniques (microarrays, proteomics, epigenetic technology and improved DNA sequencing technology) enable better characterisation of cancer tumours. The vast amounts of data, however, coupled with the use of different terms - or semantic heterogeneity - in each discipline makes the retrieval and integration of information difficult. RESULTS: Existing software infrastructures for data-sharing in the cancer domain, such as caGrid, support access to distributed information. caGrid follows a service-oriented model-driven architecture. Each data source in caGrid is associated with metadata at increasing levels of abstraction, including syntactic, structural, reference and domain metadata. The domain metadata consists of ontology-based annotations associated with the structural information of each data source. However, caGrid's current querying functionality is given at the structural metadata level, without capitalising on the ontology-based annotations. This paper presents the design of and theoretical foundations for distributed ontology-based queries over cancer research data. Concept-based queries are reformulated to the target query language, where join conditions between multiple data sources are found by exploiting the semantic annotations. The system has been implemented, as a proof of concept, over the caGrid infrastructure. The approach is applicable to other model-driven architectures. A graphical user interface has been developed, supporting ontology-based queries over caGrid data sources. An extensive evaluation of the query reformulation technique is included. CONCLUSIONS: To support personalised medicine in oncology, it is crucial to retrieve and integrate molecular, pathology, radiology and clinical data in an efficient manner. The semantic heterogeneity of the data makes this a challenging task. Ontologies provide a formal framework to support querying and integration. This paper provides an ontology-based solution for querying distributed databases over service-oriented, model-driven infrastructures.     

caGrid: design and implementation of the core architecture of the cancer biomedical informatics grid

MOTIVATION: The complexity of cancer is prompting researchers to find new ways to synthesize information from diverse data sources and to carry out coordinated research efforts that span multiple institutions. There is a need for standard applications, common data models, and software infrastructure to enable more efficient access to and sharing of distributed computational resources in cancer research. To address this need the National Cancer Institute (NCI) has initiated a national-scale effort, called the cancer Biomedical Informatics Grid (caBIGtrade mark), to develop a federation of interoperable research information systems. RESULTS: At the heart of the caBIG approach to federated interoperability effort is a Grid middleware infrastructure, called caGrid. In this paper we describe the caGrid framework and its current implementation, caGrid version 0.5. caGrid is a model-driven and service-oriented architecture that synthesizes and extends a number of technologies to provide a standardized framework for the advertising, discovery, and invocation of data and analytical resources. We expect caGrid to greatly facilitate the launch and ongoing management of coordinated cancer research studies involving multiple institutions, to provide the ability to manage and securely share information and analytic resources, and to spur a new generation of research applications that empower researchers to take a more integrative, trans-domain approach to data mining and analysis. AVAILABILITY: The caGrid version 0.5 release can be downloaded from https://cabig.nci.nih.gov/workspaces/Architecture/caGrid/. The operational test bed Grid can be accessed through the client included in the release, or through the caGrid-browser web application http://cagrid-browser.nci.nih.gov.     

SRAdb: query and use public next-generation sequencing data from within R

Background

The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Illumina (Genome Analyzer, HiSeq, MiSeq, .etc), Roche 454 GS System, Applied Biosystems SOLiD System, Helicos Heliscope, PacBio RS, and others.

Results

SRAdb is an attempt to make queries of the metadata associated with SRA submission, study, sample, experiment and run more robust and precise, and make access to sequencing data in the SRA easier. We have parsed all the SRA metadata into a SQLite database that is routinely updated and can be easily distributed. The SRAdb R/Bioconductor package then utilizes this SQLite database for querying and accessing metadata. Full text search functionality makes querying metadata very flexible and powerful. Fastq files associated with query results can be downloaded easily for local analysis. The package also includes an interface from R to a popular genome browser, the Integrated Genomics Viewer.

Conclusions

SRAdb Bioconductor package provides a convenient and integrated framework to query and access SRA metadata quickly and powerfully from within R.     

Validation of a model-based inverse kinematics approach based on wearable inertial sensors

Abstract

Wearable inertial measurement units (IMUs) are a promising solution to human motion estimation. Using IMUs 3D orientations, a model-driven inverse kinematics methodology to estimate joint angles is presented. Estimated joint angles were validated against encoder-measured kinematics (robot) and against marker-based kinematics (passive mechanism). Results are promising, with RMS angular errors respectively lower than 3 and 6?deg over a minimum range of motion of 50?deg (robot) and 160?deg (passive mechanism). Moreover, a noise robustness analysis revealed that the model-driven approach reduces the effects of experimental noises, making the proposed technique particularly suitable for application in human motion analysis.     

Metabolic response of yellow mealworm larvae to two alternative rearing substrates

Introduction

Bitter melon (Momordica charantia, Cucurbitaceae) is a popular edible medicinal plant, which has been used as a botanical dietary supplement for the treatment of diabetes and obesity in Chinese folk medicine. Previously, our team has proved that cucurbitanes triterpenoid were involved in bitter melon’s anti-diabetic effects as well as on increasing energy expenditure. The triterpenoids composition can however be influenced by changes of varieties or habitats.

Objectives

To clarify the significance of bioactive metabolites diversity among different bitter melons and to provide a guideline for selection of bitter melon varieties, an exploratory study was carried out using a UHPLC-HRMS based metabolomic study to identify chemotypes.

Methods

Metabolites of 55 seed samples of bitter melon collected in different parts of China were profiled by UHPLC-HRMS. The profiling data were analysed with multivariate (MVA) statistical methods. Principle component analysis (PCA) and hierarchical cluster analysis (HCA) were applied for sample differentiation. Marker compounds were identified by comparing spectroscopic data with isolated compounds, and additional triterpenes were putatively identified by propagating annotations through a molecular network (MN) generated from UHPLC-HRMS & MS/MS metabolite profiling.

Results

PCA and HCA provided a good discrimination between bitter melon samples from various origins in China. This study revealed for the first time the existence of two chemotypes of bitter melon. Marker compounds of those two chemotypes were identified at different MSI levels. The combined results of MN and MVA demonstrated that the two chemotypes mainly differ in their richness in cucurbitane versus oleanane triterpenoid glycosides (CTGs vs. OTGs).

Conclusion

Our finding revealed a clear chemotype distribution of bioactive components across bitter melon varieties. While bioactivities of individual CTGs and OTGs still need to be investigated in more depth, our results could help in future the selection of bitter melon varieties with optimised metabolites profile for an improved management of diabetes with this popular edible Chinese folk medicine.

     

Further mosses from Himalayan Pakistan

Abstract

Twenty three taxa are listed from Himalayan Pakistan, with ecological data; eight of these appear to be previously unrecorded for Pakistan. Some taxonomic annotations are given.     

Genomes as geography: using GIS technology to build interactive genome feature maps

Background  

Many commonly used genome browsers display sequence annotations and related attributes as horizontal data tracks that can be toggled on and off according to user preferences. Most genome browsers use only simple keyword searches and limit the display of detailed annotations to one chromosomal region of the genome at a time. We have employed concepts, methodologies, and tools that were developed for the display of geographic data to develop a Genome Spatial Information System (GenoSIS) for displaying genomes spatially, and interacting with genome annotations and related attribute data. In contrast to the paradigm of horizontally stacked data tracks used by most genome browsers, GenoSIS uses the concept of registered spatial layers composed of spatial objects for integrated display of diverse data. In addition to basic keyword searches, GenoSIS supports complex queries, including spatial queries, and dynamically generates genome maps. Our adaptation of the geographic information system (GIS) model in a genome context supports spatial representation of genome features at multiple scales with a versatile and expressive query capability beyond that supported by existing genome browsers.     

KaBOB: ontology-based semantic integration of biomedical databases

Background

The ability to query many independent biological databases using a common ontology-based semantic model would facilitate deeper integration and more effective utilization of these diverse and rapidly growing resources. Despite ongoing work moving toward shared data formats and linked identifiers, significant problems persist in semantic data integration in order to establish shared identity and shared meaning across heterogeneous biomedical data sources.

Results

We present five processes for semantic data integration that, when applied collectively, solve seven key problems. These processes include making explicit the differences between biomedical concepts and database records, aggregating sets of identifiers denoting the same biomedical concepts across data sources, and using declaratively represented forward-chaining rules to take information that is variably represented in source databases and integrating it into a consistent biomedical representation. We demonstrate these processes and solutions by presenting KaBOB (the Knowledge Base Of Biomedicine), a knowledge base of semantically integrated data from 18 prominent biomedical databases using common representations grounded in Open Biomedical Ontologies. An instance of KaBOB with data about humans and seven major model organisms can be built using on the order of 500 million RDF triples. All source code for building KaBOB is available under an open-source license.

Conclusions

KaBOB is an integrated knowledge base of biomedical data representationally based in prominent, actively maintained Open Biomedical Ontologies, thus enabling queries of the underlying data in terms of biomedical concepts (e.g., genes and gene products, interactions and processes) rather than features of source-specific data schemas or file formats. KaBOB resolves many of the issues that routinely plague biomedical researchers intending to work with data from multiple data sources and provides a platform for ongoing data integration and development and for formal reasoning over a wealth of integrated biomedical data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0559-3) contains supplementary material, which is available to authorized users.     

The DCCD: A digital data infrastructure for tree-ring research

Existing on-line databases for dendrochronology are not flexible in terms of user permissions, tree-ring data formats, metadata administration and language. This is why we developed the Digital Collaboratory for Cultural Dendrochronology (DCCD). This TRiDaS-based multi-lingual database allows users to control data access, to perform queries, to upload and download (meta)data in a variety of digital formats, and to edit metadata on line. The content of the DCCD conforms to EU best practices regarding the long-term preservation of digital research data.     

A metadata-driven approach to loading and querying heterogeneous scientific data

The Ecological Metadata Language is an effective specification for describing data for long-term storage and interpretation. When used in conjunction with a metadata repository such as Metacat, and a metadata editing tool such as Morpho, the Ecological Metadata Language allows a large community of researchers to access and to share their data. Although the Ecological Metadata Language/Morpho/Metacat toolkit provides a rich data documentation mechanism, current methods for retrieving metadata-described data can be laborious and time consuming. Moreover, the structural and semantic heterogeneity of ecological data sets makes the development of custom solutions for integrating and querying these data prohibitively costly for large-scale synthesis. The Data Manager Library leverages the Ecological Metadata Language to provide automated data processing features that allow efficient data access, querying, and manipulation without custom development. The library can be used for many data management tasks and was designed to be immediately useful as well as extensible and easy to incorporate within existing applications. In this paper we describe the motivation for developing the Data Manager Library, provide an overview of its implementation, illustrate ideas for potential use by describing several planned and existing deployments, and describe future work to extend the library.     

GEOmetadb: powerful alternative search engine for the Gene Expression Omnibus

The NCBI Gene Expression Omnibus (GEO) represents the largest public repository of microarray data. However, finding data in GEO can be challenging. We have developed GEOmetadb in an attempt to make querying the GEO metadata both easier and more powerful. All GEO metadata records as well as the relationships between them are parsed and stored in a local MySQL database. A powerful, flexible web search interface with several convenient utilities provides query capabilities not available via NCBI tools. In addition, a Bioconductor package, GEOmetadb that utilizes a SQLite export of the entire GEOmetadb database is also available, rendering the entire GEO database accessible with full power of SQL-based queries from within R. AVAILABILITY: The web interface and SQLite databases available at http://gbnci.abcc.ncifcrf.gov/geo/. The Bioconductor package is available via the Bioconductor project. The corresponding MATLAB implementation is also available at the same website.     

A precise and scalable method for querying genes in chromosomal banding regions based on cytogenetic annotations

Motivation: Staining the human metaphase chromosomes revealscharacteristic banding patterns known as cytogenetic bands orcytobands. Using technologies based on metaphase chromosomes,researchers have accumulated much knowledge about the correlationsbetween human diseases and specific cytoband aberrations, indicatingthe presence of disease-associated genes in those bands. Withthe progress of human genome project and techniques such asfluorescent in situ hybridization, many genes have been assignedto the cytobands and annotated in public databases, making itpossible to find all genes in the disease-related cytobandsthrough database queries. However, finding genes in cytobandsremains an imprecise process, partly due to the insufficiencyof current methods for cytoband queries, especially for thosebased on cytogenetic annotations. Results: By transforming the cytoband annotations into numericalsegments, a new query method is developed that is able to accuratelydefine any cytogenetic ranges in human chromosomes. A querysystem (designated cytoband query sys CQS) is implemented usingcytogenetic annotations in the public domain. Judged by a performancetest, CQS executed as accurately as expected using cytogeneticannotations from NCBI Map Viewer. The new method is scalableand can be applied to genomes from other species. Availability: The CQS is freely accessible over the Internetat http://moris.csie.ncku.edu.tw/cqs/ Contact: clh9{at}mail.ncku.edu.tw Supplementary information: http://moris.csie.ncku.edu.tw/cqs/     

The Elliott Site (14Ge303): A Preliminary Report

Abstract

Initial work at the Elliott site, Geary County, Kansas, reveals three cultural components: Archaic (Munkers Creek?), Woodland, and Smoky Hill, spatially distributed over the site in five clusters. The Woodland cluster (14GE312) was tested and is reported here. The data suggest a tentative Schultz focus affiliation.     

Grey-headed Woodpecker,Picus canus,in north-eastern Turkey

Abstract

A new record of the Grey-headed Woodpecker in north-eastern Turkey shows, together with published results, that the species apparently is distributed in low density over the whole Black Sea region of Turkey.     

Modeling Critical Infrastructure Interdependency: The Case of the Mexico City Metro Transport System

ABSTRACT

“Critical infrastructures” are exceedingly complex and highly interconnected. In order to gain a full understanding and comprehensive awareness of the risks associated with critical infrastructures it becomes essential to consider in a coherent way all the aspects that may cause a failure of such systems. That is, there is a need for a systemic approach to interdependencies among critical infrastructures. The article presents the application of a systemic safety management system (SSMS) model to interdependency modeling for the case of the Mexico City Metro transport network. The model has highlighted that interdependencies occur vertically and horizontally. Horizontal interdependency occurs at every level of recursion and can be: operational, managerial, and environmental. Vertical interdependency, on the other hand, occurs between two levels of recursion only. The SSMS model has shown the potential to be used to model interdependencies among critical infrastructures. It is hoped that the approach presented may help to gain a better understanding of critical infrastructure interdependency.     

Analyzing large biological datasets with association networks

Due to advances in high-throughput biotechnologies biological information is being collected in databases at an amazing rate, requiring novel computational approaches that process collected data into new knowledge in a timely manner. In this study, we propose a computational framework for discovering modular structure, relationships and regularities in complex data. The framework utilizes a semantic-preserving vocabulary to convert records of biological annotations of an object, such as an organism, gene, chemical or sequence, into networks (Anets) of the associated annotations. An association between a pair of annotations in an Anet is determined by the similarity of their co-occurrence pattern with all other annotations in the data. This feature captures associations between annotations that do not necessarily co-occur with each other and facilitates discovery of the most significant relationships in the collected data through clustering and visualization of the Anet. To demonstrate this approach, we applied the framework to the analysis of metadata from the Genomes OnLine Database and produced a biological map of sequenced prokaryotic organisms with three major clusters of metadata that represent pathogens, environmental isolates and plant symbionts.     

PEATmoss (Physcomitrella Expression Atlas Tool): a unified gene expression atlas for the model plant Physcomitrella patens

Physcomitrella patens is a bryophyte model plant that is often used to study plant evolution and development. Its resources are of great importance for comparative genomics and evo‐devo approaches. However, expression data from Physcomitrella patens were so far generated using different gene annotation versions and three different platforms: CombiMatrix and NimbleGen expression microarrays and RNA sequencing. The currently available P. patens expression data are distributed across three tools with different visualization methods to access the data. Here, we introduce an interactive expression atlas, Physcomitrella Expression Atlas Tool (PEATmoss), that unifies publicly available expression data for P. patens and provides multiple visualization methods to query the data in a single web‐based tool. Moreover, PEATmoss includes 35 expression experiments not previously available in any other expression atlas. To facilitate gene expression queries across different gene annotation versions, and to access P. patens annotations and related resources, a lookup database and web tool linked to PEATmoss was implemented. PEATmoss can be accessed at https://peatmoss.online.uni-marburg.de