ToxicR: A computational platform in R for computational toxicology and dose-response analyses.

The need to analyze the complex relationships observed in high-throughput toxicogenomic and other omic platforms has resulted in an explosion of methodological advances in computational toxicology. However, advancements in the literature often outpace the development of software researchers can implement in their pipelines, and existing software is frequently based on pre-specified workflows built from well-vetted assumptions that may not be optimal for novel research questions. Accordingly, there is a need for a stable platform and open-source codebase attached to a programming language that allows users to program new algorithms. To fill this gap, the Biostatistics and Computational Biology Branch of the National Institute of Environmental Health Sciences, in cooperation with the National Toxicology Program (NTP) and US Environmental Protection Agency (EPA), developed ToxicR, an open-source R programming package. The ToxicR platform implements many of the standard analyses used by the NTP and EPA, including dose-response analyses for continuous and dichotomous data that employ Bayesian, maximum likelihood, and model averaging methods, as well as many standard tests the NTP uses in rodent toxicology and carcinogenicity studies, such as the poly-K and Jonckheere trend tests. ToxicR is built on the same codebase as current versions of the EPA's Benchmark Dose software and NTP's BMDExpress software but has increased flexibility because it directly accesses this software. To demonstrate ToxicR, we developed a custom workflow to illustrate its capabilities for analyzing toxicogenomic data. The unique features of ToxicR will allow researchers in other fields to add modules, increasing its functionality in the future.

CEBS update: curated toxicology database with enhanced tools for data integration.

The Chemical Effects in Biological Systems database (CEBS) contains extensive toxicology study results and metadata from the Division of the National Toxicology Program (NTP) and other studies of environmental health interest. This resource grants public access to search and collate data from over 10 250 studies for 12 750 test articles (chemicals, environmental agents). CEBS has made considerable strides over the last 5 years to integrate growing internal data repositories into data warehouses and data marts to better serve the public with high quality curated datasets. This effort includes harmonizing legacy terms and metadata to current standards, mapping test articles to external identifiers, and aligning terms to OBO (Open Biological and Biomedical Ontology) Foundry ontologies. The data are made available through the CEBS Homepage (https://cebs.niehs.nih.gov/cebs/), guided search applications, flat files on FTP (file transfer protocol), and APIs (application programming interface) for user access and to provide a bridge for computational tools. The user interface is intuitive with a single search bar to query keywords related to study metadata, publications, and data availability. Results are consolidated to single pages for each test article with NTP conclusions, publications, individual studies, data collections, and links to related test articles and projects available together.

The TGx-28.65 biomarker online application for analysis of transcriptomics data to identify DNA damage-inducing chemicals in human cell cultures.

The TGx-28.65 biomarker is a 65-gene expression profile generated from testing 28 model chemicals (13 that cause DNA damage and 15 that do not) in human TK6 cells. It is used to predict whether a chemical induces DNA damage or not. We expanded availability to the biomarker by developing the online TGx-28.65 biomarker application for predicting the DNA damage inducing (DDI) potential of suspect toxicants tested in p53-proficient human cells and assessing putative mode(s) of action (MOA). Applications like this that analyse gene expression data to predict the hazard potential of test chemicals hold great promise for risk assessment paradigms. The TGx-28.65 biomarker interfaces with an analytical tool to predict the probability that a test chemical can directly or indirectly induce DNA damage. User submitted in vitro microarray data are compared to the 28-chemical x 65-gene signature profile and the probability that the data fit the profile for a DDI or a non-DDI (NDDI) chemical is calculated. The results are displayed in the Results Table, which includes the classification probability and hyperlinks to view heatmaps, hierarchical clustering, and principal component analyses of user-input data in the context of the reference profile. The heatmaps and cluster plots, along with the corresponding text data files of fold changes in gene expression and Euclidean distances can be downloaded. Review of the test chemical data in relationship to the biomarker allows rapid identification of key gene alterations associated with DNA damage as well as chemicals in the reference set that produced a similar response. Environ. Mol. Mutagen. 58:529-535, 2017. © 2017 Wiley Periodicals, Inc.

CEBS: a comprehensive annotated database of toxicological data.

The Chemical Effects in Biological Systems database (CEBS) is a comprehensive and unique toxicology resource that compiles individual and summary animal data from the National Toxicology Program (NTP) testing program and other depositors into a single electronic repository. CEBS has undergone significant updates in recent years and currently contains over 11 000 test articles (exposure agents) and over 8000 studies including all available NTP carcinogenicity, short-term toxicity and genetic toxicity studies. Study data provided to CEBS are manually curated, accessioned and subject to quality assurance review prior to release to ensure high quality. The CEBS database has two main components: data collection and data delivery. To accommodate the breadth of data produced by NTP, the CEBS data collection component is an integrated relational design that allows the flexibility to capture any type of electronic data (to date). The data delivery component of the database comprises a series of dedicated user interface tables containing pre-processed data that support each component of the user interface. The user interface has been updated to include a series of nine Guided Search tools that allow access to NTP summary and conclusion data and larger non-NTP datasets. The CEBS database can be accessed online at http://www.niehs.nih.gov/research/resources/databases/cebs/.

The Ontology for Biomedical Investigations.

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl.

Mining the Archives: A Cross-Platform Analysis of Gene Expression Profiles in Archival Formalin-Fixed Paraffin-Embedded Tissues.

Formalin-fixed paraffin-embedded (FFPE) tissue samples represent a potentially invaluable resource for transcriptomic research. However, use of FFPE samples in genomic studies has been limited by technical challenges resulting from nucleic acid degradation. Here we evaluated gene expression profiles derived from fresh-frozen (FRO) and FFPE mouse liver tissues preserved in formalin for different amounts of time using 2 DNA microarray protocols and 2 whole-transcriptome sequencing (RNA-seq) library preparation methodologies. The ribo-depletion protocol outperformed the other methods by having the highest correlations of differentially expressed genes (DEGs), and best overlap of pathways, between FRO and FFPE groups. The effect of sample time in formalin (18 h or 3 weeks) on gene expression profiles indicated that test article treatment, not preservation method, was the main driver of gene expression profiles. Meta- and pathway analyses indicated that biological responses were generally consistent for 18 h and 3 week FFPE samples compared with FRO samples. However, clear erosion of signal intensity with time in formalin was evident, and DEG numbers differed by platform and preservation method. Lastly, we investigated the effect of time in paraffin on genomic profiles. Ribo-depletion RNA-seq analysis of 8-, 19-, and 26-year-old control blocks resulted in comparable quality metrics, including expected distributions of mapped reads to exonic, untranslated region, intronic, and ribosomal fractions of the transcriptome. Overall, our results indicate that FFPE samples are appropriate for use in genomic studies in which frozen samples are not available, and that ribo-depletion RNA-seq is the preferred method for this type of analysis in archival and long-aged FFPE samples.

NIEHS/FDA CLARITY-BPA research program update.

Bisphenol A (BPA) is a chemical used in the production of numerous consumer products resulting in potential daily human exposure to this chemical. The FDA previously evaluated the body of BPA toxicology data and determined that BPA is safe at current exposure levels. Although consistent with the assessment of some other regulatory agencies around the world, this determination of BPA safety continues to be debated in scientific and popular publications, resulting in conflicting messages to the public. Thus, the National Toxicology Program (NTP), National Institute of Environmental Health Sciences (NIEHS), and U.S. Food and Drug Administration (FDA) developed a consortium-based research program to link more effectively a variety of hypothesis-based research investigations and guideline-compliant safety testing with BPA. This collaboration is known as the Consortium Linking Academic and Regulatory Insights on BPA Toxicity (CLARITY-BPA). This paper provides a detailed description of the conduct of the study and a midterm update on progress of the CLARITY-BPA research program.

Profiling of the Tox21 10K compound library for agonists and antagonists of the estrogen receptor alpha signaling pathway.

The U.S. Tox21 program has screened a library of approximately 10,000 (10K) environmental chemicals and drugs in three independent runs for estrogen receptor alpha (ERα) agonist and antagonist activity using two types of ER reporter gene cell lines, one with an endogenous full length ERα (ER-luc; BG1 cell line) and the other with a transfected partial receptor consisting of the ligand binding domain (ER-bla; ERα ß-lactamase cell line), in a quantitative high-throughput screening (qHTS) format. The ability of the two assays to correctly identify ERα agonists and antagonists was evaluated using a set of 39 reference compounds with known ERα activity. Although both assays demonstrated adequate (i.e. >80%) predictivity, the ER-luc assay was more sensitive and the ER-bla assay more specific. The qHTS assay results were compared with results from previously published ERα binding assay data and showed >80% consistency. Actives identified from both the ER-bla and ER-luc assays were analyzed for structure-activity relationships (SARs) revealing known and potentially novel ERα active structure classes. The results demonstrate the feasibility of qHTS to identify environmental chemicals with the potential to interact with the ERα signaling pathway and the two different assay formats improve the confidence in correctly identifying these chemicals.

Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine.

Translating whole-exome sequencing (WES) for prospective clinical use may have an impact on the care of patients with cancer; however, multiple innovations are necessary for clinical implementation. These include rapid and robust WES of DNA derived from formalin-fixed, paraffin-embedded tumor tissue, analytical output similar to data from frozen samples and clinical interpretation of WES data for prospective use. Here, we describe a prospective clinical WES platform for archival formalin-fixed, paraffin-embedded tumor samples. The platform employs computational methods for effective clinical analysis and interpretation of WES data. When applied retrospectively to 511 exomes, the interpretative framework revealed a 'long tail' of somatic alterations in clinically important genes. Prospective application of this approach identified clinically relevant alterations in 15 out of 16 patients. In one patient, previously undetected findings guided clinical trial enrollment, leading to an objective clinical response. Overall, this methodology may inform the widespread implementation of precision cancer medicine.

Transcriptional and epigenetic dynamics during specification of human embryonic stem cells.

Differentiation of human embryonic stem cells (hESCs) provides a unique opportunity to study the regulatory mechanisms that facilitate cellular transitions in a human context. To that end, we performed comprehensive transcriptional and epigenetic profiling of populations derived through directed differentiation of hESCs representing each of the three embryonic germ layers. Integration of whole-genome bisulfite sequencing, chromatin immunoprecipitation sequencing, and RNA sequencing reveals unique events associated with specification toward each lineage. Lineage-specific dynamic alterations in DNA methylation and H3K4me1 are evident at putative distal regulatory elements that are frequently bound by pluripotency factors in the undifferentiated hESCs. In addition, we identified germ-layer-specific H3K27me3 enrichment at sites exhibiting high DNA methylation in the undifferentiated state. A better understanding of these initial specification events will facilitate identification of deficiencies in current approaches, leading to more faithful differentiation strategies as well as providing insights into the rewiring of human regulatory programs during cellular transitions.

Discovery and characterization of artifactual mutations in deep coverage targeted capture sequencing data due to oxidative DNA damage during sample preparation.

As researchers begin probing deep coverage sequencing data for increasingly rare mutations and subclonal events, the fidelity of next generation sequencing (NGS) laboratory methods will become increasingly critical. Although error rates for sequencing and polymerase chain reaction (PCR) are well documented, the effects that DNA extraction and other library preparation steps could have on downstream sequence integrity have not been thoroughly evaluated. Here, we describe the discovery of novel C > A/G > T transversion artifacts found at low allelic fractions in targeted capture data. Characteristics such as sequencer read orientation and presence in both tumor and normal samples strongly indicated a non-biological mechanism. We identified the source as oxidation of DNA during acoustic shearing in samples containing reactive contaminants from the extraction process. We show generation of 8-oxoguanine (8-oxoG) lesions during DNA shearing, present analysis tools to detect oxidation in sequencing data and suggest methods to reduce DNA oxidation through the introduction of antioxidants. Further, informatics methods are presented to confidently filter these artifacts from sequencing data sets. Though only seen in a low percentage of reads in affected samples, such artifacts could have profoundly deleterious effects on the ability to confidently call rare mutations, and eliminating other possible sources of artifacts should become a priority for the research community.

Gel-free multiplexed reduced representation bisulfite sequencing for large-scale DNA methylation profiling.

Sequencing-based approaches have led to new insights about DNA methylation. While many different techniques for genome-scale mapping of DNA methylation have been employed, throughput has been a key limitation for most. To further facilitate the mapping of DNA methylation, we describe a protocol for gel-free multiplexed reduced representation bisulfite sequencing (mRRBS) that reduces the workload dramatically and enables processing of 96 or more samples per week. mRRBS achieves similar CpG coverage to the original RRBS protocol, while the higher throughput and lower cost make it better suited for large-scale DNA methylation mapping studies, including cohorts of cancer samples.

Targeted deletion of nrf2 impairs lung development and oxidant injury in neonatal mice.

AIMS: Nrf2 is an essential transcription factor for protection against oxidant disorders. However, its role in organ development and neonatal disease has received little attention. Therapeutically administered oxygen has been considered to contribute to bronchopulmonary dysplasia (BPD) in prematurity. The current study was performed to determine Nrf2-mediated molecular events during saccular-to-alveolar lung maturation, and the role of Nrf2 in the pathogenesis of hyperoxic lung injury using newborn Nrf2-deficient (Nrf2(-/-)) and wild-type (Nrf2(+/+)) mice. RESULTS: Pulmonary basal expression of cell cycle, redox balance, and lipid/carbohydrate metabolism genes was lower while lymphocyte immunity genes were more highly expressed in Nrf2(-/-) neonates than in Nrf2(+/+) neonates. Hyperoxia-induced phenotypes, including mortality, arrest of saccular-to-alveolar transition, and lung edema, and inflammation accompanying DNA damage and tissue oxidation were significantly more severe in Nrf2(-/-) neonates than in Nrf2(+/+) neonates. During lung injury pathogenesis, Nrf2 orchestrated expression of lung genes involved in organ injury and morphology, cellular growth/proliferation, vasculature development, immune response, and cell-cell interaction. Bioinformatic identification of Nrf2 binding motifs and augmented hyperoxia-induced inflammation in genetically deficient neonates supported Gpx2 and Marco as Nrf2 effectors. INNOVATION: This investigation used lung transcriptomics and gene targeted mice to identify novel molecular events during saccular-to-alveolar stage transition and to elucidate Nrf2 downstream mechanisms in protection from hyperoxia-induced injury in neonate mouse lungs. CONCLUSION: Nrf2 deficiency augmented lung injury and arrest of alveolarization caused by hyperoxia during the newborn period. Results suggest a therapeutic potential of specific Nrf2 activators for oxidative stress-associated neonatal disorders including BPD.

Sources of variance in baseline gene expression in the rodent liver.

The use of gene expression profiling in both clinical and laboratory settings would be enhanced by better characterization of variation due to individual, environmental, and technical factors. Analysis of microarray data from untreated or vehicle-treated animals within the control arm of toxicogenomics studies has yielded useful information on baseline fluctuations in liver gene expression in the rodent. Here, studies which highlight contributions of different factors to gene expression variability in the rodent liver are discussed including a large meta-analysis of rat liver, which identified genes that vary in control animals in the absence of chemical treatment. Genes and their pathways that are the most and least variable were identified in a number of these studies. Life stage, fasting, sex, diet, circadian rhythm and liver lobe source can profoundly influence gene expression in the liver. Recognition of biological and technical factors that contribute to variability of background gene expression can help the investigator in the design of an experiment that maximizes sensitivity and reduces the influence of confounders that may lead to misinterpretation of genomic changes. The factors that contribute to variability in liver gene expression in rodents are likely analogous to those contributing to human interindividual variability in drug response and chemical toxicity. Identification of batteries of genes that are altered in a variety of background conditions could be used to predict responses to drugs and chemicals in appropriate models of the human liver.

Targeted deletion of Nrf2 reduces urethane-induced lung tumor development in mice.

Nrf2 is a key transcription factor that regulates cellular redox and defense responses. However, permanent Nrf2 activation in human lung carcinomas promotes pulmonary malignancy and chemoresistance. We tested the hypothesis that Nrf2 has cell survival properties and lack of Nrf2 suppresses chemically-induced pulmonary neoplasia by treating Nrf2(+/+) and Nrf2(-/-) mice with urethane. Airway inflammation and injury were assessed by bronchoalveolar lavage analyses and histopathology, and lung tumors were analyzed by gross and histologic analysis. We used transcriptomics to assess Nrf2-dependent changes in pulmonary gene transcripts at multiple stages of neoplasia. Lung hyperpermeability, cell death and apoptosis, and inflammatory cell infiltration were significantly higher in Nrf2(-/-) mice compared to Nrf2(+/+) mice 9 and 11 wk after urethane. Significantly fewer lung adenomas were found in Nrf2(-/-) mice than in Nrf2(+/+) mice at 12 and 22 wk. Nrf2 modulated expression of genes involved cell-cell signaling, glutathione metabolism and oxidative stress response, and immune responses during early stage neoplasia. In lung tumors, Nrf2-altered genes had roles in transcriptional regulation of cell cycle and proliferation, carcinogenesis, organismal injury and abnormalities, xenobiotic metabolism, and cell-cell signaling genes. Collectively, Nrf2 deficiency decreased susceptibility to urethane-induced lung tumorigenesis in mice. Cell survival properties of Nrf2 were supported, at least in part, by reduced early death of initiated cells and heightened advantage for tumor cell expansion in Nrf2(+/+) mice relative to Nrf2(-/-) mice. Our results were consistent with the concept that Nrf2 over-activation is an adaptive response of cancer conferring resistance to anti-cancer drugs and promoting malignancy.

Maximizing biomarker discovery by minimizing gene signatures.

BACKGROUND: The use of gene signatures can potentially be of considerable value in the field of clinical diagnosis. However, gene signatures defined with different methods can be quite various even when applied the same disease and the same endpoint. Previous studies have shown that the correct selection of subsets of genes from microarray data is key for the accurate classification of disease phenotypes, and a number of methods have been proposed for the purpose. However, these methods refine the subsets by only considering each single feature, and they do not confirm the association between the genes identified in each gene signature and the phenotype of the disease. We proposed an innovative new method termed Minimize Feature's Size (MFS) based on multiple level similarity analyses and association between the genes and disease for breast cancer endpoints by comparing classifier models generated from the second phase of MicroArray Quality Control (MAQC-II), trying to develop effective meta-analysis strategies to transform the MAQC-II signatures into a robust and reliable set of biomarker for clinical applications. RESULTS: We analyzed the similarity of the multiple gene signatures in an endpoint and between the two endpoints of breast cancer at probe and gene levels, the results indicate that disease-related genes can be preferably selected as the components of gene signature, and that the gene signatures for the two endpoints could be interchangeable. The minimized signatures were built at probe level by using MFS for each endpoint. By applying the approach, we generated a much smaller set of gene signature with the similar predictive power compared with those gene signatures from MAQC-II. CONCLUSIONS: Our results indicate that gene signatures of both large and small sizes could perform equally well in clinical applications. Besides, consistency and biological significances can be detected among different gene signatures, reflecting the studying endpoints. New classifiers built with MFS exhibit improved performance with both internal and external validation, suggesting that MFS method generally reduces redundancies for features within gene signatures and improves the performance of the model. Consequently, our strategy will be beneficial for the microarray-based clinical applications.

Prolonged drug selection of breast cancer cells and enrichment of cancer stem cell characteristics.

BACKGROUND: Cancer stem cells are presumed to have virtually unlimited proliferative and self-renewal abilities and to be highly resistant to chemotherapy, a feature that is associated with overexpression of ATP-binding cassette transporters. We investigated whether prolonged continuous selection of cells for drug resistance enriches cultures for cancer stem-like cells. METHODS: Cancer stem cells were defined as CD44+/CD24⁻ cells that could self-renew (ie, generate cells with the tumorigenic CD44+/CD24⁻ phenotype), differentiate, invade, and form tumors in vivo. We used doxorubicin-selected MCF-7/ADR cells, weakly tumorigenic parental MCF-7 cells, and MCF-7/MDR, an MCF-7 subline with forced expression of ABCB1 protein. Cells were examined for cell surface markers and side-population fractions by microarray and flow cytometry, with in vitro invasion assays, and for ability to form mammospheres. Xenograft tumors were generated in mice to examine tumorigenicity (n = 52). The mRNA expression of multidrug resistance genes was examined in putative cancer stem cells and pathway analysis of statistically significantly differentially expressed genes was performed. All statistical tests were two-sided. RESULTS: Pathway analysis showed that MCF-7/ADR cells express mRNAs from ABCB1 and other genes also found in breast cancer stem cells (eg, CD44, TGFB1, and SNAI1). MCF-7/ADR cells were highly invasive, formed mammospheres, and were tumorigenic in mice. In contrast to parental MCF-7 cells, more than 30% of MCF-7/ADR cells had a CD44+/CD24⁻ phenotype, could self-renew, and differentiate (ie, produce CD44+/CD24⁻ and CD44+/CD24+ cells) and overexpressed various multidrug resistance-linked genes (including ABCB1, CCNE1, and MMP9). MCF-7/ADR cells were statistically significantly more invasive in Matrigel than parental MCF-7 cells (MCF-7 cells = 0.82 cell per field and MCF-7/ADR = 7.51 cells per field, difference = 6.69 cells per field, 95% confidence interval = 4.82 to 8.55 cells per field, P < .001). No enrichment in the CD44+/CD24⁻ or CD133+ population was detected in MCF-7/MDR. CONCLUSION: The cell population with cancer stem cell characteristics increased after prolonged continuous selection for doxorubicin resistance.

Protective role of interleukin-10 in ozone-induced pulmonary inflammation.

BACKGROUND: The mechanisms underlying ozone (O3)-induced pulmonary inflammation remain unclear. Interleukin-10 (IL-10) is an anti-inflammatory cytokine that is known to inhibit inflammatory mediators. OBJECTIVES: We investigated the molecular mechanisms underlying interleuken-10 (IL-10)-mediated attenuation of O3-induced pulmonary inflammation in mice. METHODS: Il10-deficient (Il10(-/-)) and wild-type (Il10(+/+)) mice were exposed to 0.3 ppm O3 or filtered air for 24, 48, or 72 hr. Immediately after exposure, differential cell counts and total protein (a marker of lung permeability) were assessed from bronchoalveolar lavage fluid (BALF). mRNA and protein levels of cellular mediators were determined from lung homogenates. We also used global mRNA expression analyses of lung tissue with Ingenuity Pathway Analysis to identify patterns of gene expression through which IL-10 modifies O3-induced inflammation. RESULTS: Mean numbers of BALF polymorphonuclear leukocytes (PMNs) were significantly greater in Il10(-/-) mice than in Il10(+/+) mice after exposure to O3 at all time points tested. O3-enhanced nuclear NF-κB translocation was elevated in the lungs of Il10(-/-) compared with Il10(+/+) mice. Gene expression analyses revealed several IL-10-dependent and O3-dependent mediators, including macrophage inflammatory protein 2, cathepsin E, and serum amyloid A3. CONCLUSIONS: Results indicate that IL-10 protects against O3-induced pulmonary neutrophilic inflammation and cell proliferation. Moreover, gene expression analyses identified three response pathways and several genetic targets through which IL-10 may modulate the innate and adaptive immune response. These novel mechanisms of protection against the pathogenesis of O3-induced pulmonary inflammation may also provide potential therapeutic targets to protect susceptible individuals.

Modeling biomedical experimental processes with OBI.

BACKGROUND: Experimental descriptions are typically stored as free text without using standardized terminology, creating challenges in comparison, reproduction and analysis. These difficulties impose limitations on data exchange and information retrieval. RESULTS: The Ontology for Biomedical Investigations (OBI), developed as a global, cross-community effort, provides a resource that represents biomedical investigations in an explicit and integrative framework. Here we detail three real-world applications of OBI, provide detailed modeling information and explain how to use OBI. CONCLUSION: We demonstrate how OBI can be applied to different biomedical investigations to both facilitate interpretation of the experimental process and increase the computational processing and integration within the Semantic Web. The logical definitions of the entities involved allow computers to unambiguously understand and integrate different biological experimental processes and their relevant components. AVAILABILITY: OBI is available at http://purl.obolibrary.org/obo/obi/2009-11-02/obi.owl.

Current and future applications of toxicogenomics: Results summary of a survey from the HESI Genomics State of Science Subcommittee.

BACKGROUND: In spite of the application of toxicogenomic (TGx) data to the field of toxicology for the past 10 years, the broad implementation and full impact of TGx for chemical and drug evaluation to improve decision making within organizations and by policy makers has not been achieved. OBJECTIVES: The goal of the Health and Environmental Sciences Institute (HESI) Committee on the Application of Genomics to Mechanism-based Risk Assessment was to construct and summarize a multisector survey, addressing key issues and perspectives on the current and future practical uses and challenges of implementing TGx data to facilitate discussions for decision making within organizations and by policy makers. METHODS: An online survey to probe the current status and future challenges facing the field of TGx for drug and chemical evaluation in experimental and nonclinical models was taken by scientists and scientific decision/policy makers actively engaged in the field of TGx within industrial, academic, and regulatory sectors of the United States, Europe, and Japan. For this survey, TGx refers specifically to the analysis of gene expression responses to evaluate xenobiotic exposure in experimental and preclinical models. RESULTS: The survey results are summarized from questions covering broad areas including technology used, organizational capacity and resource allocation, experimental approaches, data storage and exchange, perceptions of benefits and hurdles, and future expectations. CONCLUSIONS: The survey findings provide valuable information on the current state of the science of TGx applications and identify key areas in which TGx will have an impact as well as the key hurdles in applying TGx data to address issues. The findings serve as a public resource to facilitate discussions on the focus of future TGx efforts to ensure that a maximal benefit can be obtained from toxicogenomic studies.