Though chemical exposures are known to potentially have negative impacts on health, including contributing to chronic diseases such as cancer, the quantitative contribution of risk is not fully understood for every chemical. A commonly used approach to quantify levels of risk is to measure the proportion of organisms (such as a total number of zebrafish on a plate or mice in a cage) with abnormal behavioral responses or morphology at increasing concentrations of chemical exposure. A particular challenge with processing the proportional data from these assays is the appropriate estimation of chemical concentration levels that result in malformations or acute toxicity, as these values typically vary between experimental measurements. The recommended approach by the Environmental Protection Agency (EPA) is to fit benchmark dose curves with specific filters and model fitting steps, which are crucial to properly processing the proportional data. Several tools exist for the fitting of benchmark dose response curves, but none are standalone Python libraries built to process both morphological and behavioral data as proportions with all the EPA recommended filters, filter parameters, models, and model parameters. Thus, here we present the benchmark dose response curve (bmdrc) Python library, which was built to closely follow these EPA guidelines with helpful visualizations of filters and fitted model curves, and reports for reproducibility purposes. bmdrc is open-source and has demonstrated utility as a support package to an existing web portal for information on chemicals (https://srp.pnnl.gov). Our package will support any toxicology analysis where the response is a proportional value at increasing levels of a concentration of a chemical or chemical mixture.

BACKGROUND: Representing biological networks as graphs is a powerful approach to reveal underlying patterns, signatures, and critical components from high-throughput biomolecular data. However, graphs do not natively capture the multi-way relationships present among genes and proteins in biological systems. Hypergraphs are generalizations of graphs that naturally model multi-way relationships and have shown promise in modeling systems such as protein complexes and metabolic reactions. In this paper we seek to understand how hypergraphs can more faithfully identify, and potentially predict, important genes based on complex relationships inferred from genomic expression data sets. RESULTS: We compiled a novel data set of transcriptional host response to pathogenic viral infections and formulated relationships between genes as a hypergraph where hyperedges represent significantly perturbed genes, and vertices represent individual biological samples with specific experimental conditions. We find that hypergraph betweenness centrality is a superior method for identification of genes important to viral response when compared with graph centrality. CONCLUSIONS: Our results demonstrate the utility of using hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of highly pathogenic viruses.

BACKGROUND: The Bioinformatics Resource Manager (BRM) is a web-based tool developed to facilitate identifier conversion and data integration for Homo sapiens (human), Mus musculus (mouse), Rattus norvegicus (rat), Danio rerio (zebrafish), and Macaca mulatta (macaque), as well as perform orthologous conversions among the supported species. In addition to providing a robust means of identifier conversion, BRM also incorporates a suite of microRNA (miRNA)-target databases upon which to query target genes or to perform reverse target lookups using gene identifiers. RESULTS: BRM has the capability to perform cross-species identifier lookups across common identifier types, directly integrate datasets across platform or species by performing identifier retrievals in the background, and retrieve miRNA targets from multiple databases simultaneously and integrate the resulting gene targets with experimental mRNA data. Here we use workflows provided in BRM to integrate RNA sequencing data across species to identify common biomarkers of exposure after treatment of human lung cells and zebrafish to benzo[a]pyrene (BAP). We further use the miRNA Target workflow to experimentally determine the role of miRNAs as regulators of BAP toxicity and identify the predicted functional consequences of miRNA-target regulation in our system. The output from BRM can easily and directly be uploaded to freely available visualization tools for further analysis. From these examples, we were able to identify an important role for several miRNAs as potential regulators of BAP toxicity in human lung cells associated with cell migration, cell communication, cell junction assembly and regulation of cell death. CONCLUSIONS: Overall, BRM provides bioinformatics tools to assist biologists having minimal programming skills with analysis and integration of high-content omics' data from various transcriptomic and proteomic platforms. BRM workflows were developed in Java and other open-source technologies and are served publicly using Apache Tomcat at https://cbb.pnnl.gov/brm/ .

High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different-omic types (transcripts, proteins, lipids etc). We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm mechanisms of disease.

BACKGROUND: MicroRNAs (miRNAs) are noncoding RNAs that direct post-transcriptional regulation of protein coding genes. Recent studies have shown miRNAs are important for controlling many biological processes, including nervous system development, and are highly conserved across species. Given their importance, computational tools are necessary for analysis, interpretation and integration of high-throughput (HTP) miRNA data in an increasing number of model species. The Bioinformatics Resource Manager (BRM) v2.3 is a software environment for data management, mining, integration and functional annotation of HTP biological data. In this study, we report recent updates to BRM for miRNA data analysis and cross-species comparisons across datasets. RESULTS: BRM v2.3 has the capability to query predicted miRNA targets from multiple databases, retrieve potential regulatory miRNAs for known genes, integrate experimentally derived miRNA and mRNA datasets, perform ortholog mapping across species, and retrieve annotation and cross-reference identifiers for an expanded number of species. Here we use BRM to show that developmental exposure of zebrafish to 30 uM nicotine from 6-48 hours post fertilization (hpf) results in behavioral hyperactivity in larval zebrafish and alteration of putative miRNA gene targets in whole embryos at developmental stages that encompass early neurogenesis. We show typical workflows for using BRM to integrate experimental zebrafish miRNA and mRNA microarray datasets with example retrievals for zebrafish, including pathway annotation and mapping to human ortholog. Functional analysis of differentially regulated (p<0.05) gene targets in BRM indicates that nicotine exposure disrupts genes involved in neurogenesis, possibly through misregulation of nicotine-sensitive miRNAs. CONCLUSIONS: BRM provides the ability to mine complex data for identification of candidate miRNAs or pathways that drive phenotypic outcome and, therefore, is a useful hypothesis generation tool for systems biology. The miRNA workflow in BRM allows for efficient processing of multiple miRNA and mRNA datasets in a single software environment with the added capability to interact with public data sources and visual analytic tools for HTP data analysis at a systems level. BRM is developed using Java™ and other open-source technologies for free distribution (http://www.sysbio.org/dataresources/brm.stm).

MOTIVATION: In the analysis of differential peptide peak intensities (i.e. abundance measures), LC-MS analyses with poor quality peptide abundance data can bias downstream statistical analyses and hence the biological interpretation for an otherwise high-quality dataset. Although considerable effort has been placed on assuring the quality of the peptide identification with respect to spectral processing, to date quality assessment of the subsequent peptide abundance data matrix has been limited to a subjective visual inspection of run-by-run correlation or individual peptide components. Identifying statistical outliers is a critical step in the processing of proteomics data as many of the downstream statistical analyses [e.g. analysis of variance (ANOVA)] rely upon accurate estimates of sample variance, and their results are influenced by extreme values. RESULTS: We describe a novel multivariate statistical strategy for the identification of LC-MS runs with extreme peptide abundance distributions. Comparison with current method (run-by-run correlation) demonstrates a significantly better rate of identification of outlier runs by the multivariate strategy. Simulation studies also suggest that this strategy significantly outperforms correlation alone in the identification of statistically extreme liquid chromatography-mass spectrometry (LC-MS) runs. AVAILABILITY: https://www.biopilot.org/docs/Software/RMD.php CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

Abstract Motivation: The standard approach to identifying peptides based on accurate mass and elution time (AMT) compares profiles obtained from a high resolution mass spectrometer to a database of peptides previously identified from tandem mass spectrometry (MS/MS) studies. It would be advantageous, with respect to both accuracy and cost, to only search for those peptides that are detectable by MS (proteotypic). Results: We present a support vector machine (SVM) model that uses a simple descriptor space based on 35 properties of amino acid content, charge, hydrophilicity and polarity for the quantitative prediction of proteotypic peptides. Using three independently derived AMT databases (Shewanella oneidensis, Salmonella typhimurium, Yersinia pestis) for training and validation within and across species, the SVM resulted in an average accuracy measure of ∼0.83 with an SD of <0.038. Furthermore, we demonstrate that these results are achievable with a small set of 13 variables and can achieve high proteome coverage. Availability: http://omics.pnl.gov/software/STEPP.php Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

Systems biology research demands the availability of tools and technologies that span a comprehensive range of computational capabilities, including data management, transfer, processing, integration, and interpretation. To address these needs, we have created the bioinformatics resource manager (BRM), a scalable, flexible, and easy to use tool for biologists to undertake complex analyses. This paper describes the underlying software architecture of the BRM that integrates multiple commodity platforms to provide a highly extensible and scalable software infrastructure for bioinformatics. The architecture integrates a J2EE 3-tier application with an archival experimental data management system, the GAGGLE framework for desktop tool integration, and the MeDICi integration framework for high-throughput data analysis workflows. This architecture facilitates a systems biology software solution that enables the entire spectrum of scientific activities, from experimental data access to high throughput processing and analysis of data for biologists and experimental scientists.

MOTIVATION: Modern transcriptomics and proteomics enable us to survey the expression of RNAs and proteins at large scales. While these data are usually generated and analyzed separately, there is an increasing interest in comparing and co-analyzing transcriptome and proteome expression data. A major open question is whether transcriptome and proteome expression is linked and how it is coordinated. RESULTS: Here we have developed a probabilistic clustering model that permits analysis of the links between transcriptomic and proteomic profiles in a sensible and flexible manner. Our coupled mixture model defines a prior probability distribution over the component to which a protein profile should be assigned conditioned on which component the associated mRNA profile belongs to. We apply this approach to a large dataset of quantitative transcriptomic and proteomic expression data obtained from a human breast epithelial cell line (HMEC). The results reveal a complex relationship between transcriptome and proteome with most mRNA clusters linked to at least two protein clusters, and vice versa. A more detailed analysis incorporating information on gene function from the Gene Ontology database shows that a high correlation of mRNA and protein expression is limited to the components of some molecular machines, such as the ribosome, cell adhesion complexes and the TCP-1 chaperonin involved in protein folding. AVAILABILITY: Matlab code is available from the authors on request.

MOTIVATION: The standard approach to identifying peptides based on accurate mass and elution time (AMT) compares profiles obtained from a high resolution mass spectrometer to a database of peptides previously identified from tandem mass spectrometry (MS/MS) studies. It would be advantageous, with respect to both accuracy and cost, to only search for those peptides that are detectable by MS (proteotypic). RESULTS: We present a support vector machine (SVM) model that uses a simple descriptor space based on 35 properties of amino acid content, charge, hydrophilicity and polarity for the quantitative prediction of proteotypic peptides. Using three independently derived AMT databases (Shewanella oneidensis, Salmonella typhimurium, Yersinia pestis) for training and validation within and across species, the SVM resulted in an average accuracy measure of 0.8 with a SD of <0.025. Furthermore, we demonstrate that these results are achievable with a small set of 12 variables and can achieve high proteome coverage. AVAILABILITY: http://omics.pnl.gov/software/STEPP.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: The Bioinformatics Resource Manager (BRM) is a software environment that provides the user with data management, retrieval and integration capabilities. Designed in collaboration with biologists, BRM simplifies mundane analysis tasks of merging microarray and proteomic data across platforms, facilitates integration of users' data with functional annotation and interaction data from public sources and provides connectivity to visual analytic tools through reformatting of the data for easy import or dynamic launching capability. BRM is developed using Java and other open-source technologies for free distribution. AVAILABILITY: BRM, sample data sets and a user manual can be downloaded from http://www.sysbio.org/dataresources/brm.stm.

For the SC|06 analytics challenge, we demonstrate an end-to-end solution for processing data produced by high-throughput mass spectrometry (MS)-based proteomics so biological hypotheses can be explored. This approach is based on a tool called the Bioinformatics Resource Manager (BRM) which will interact with high-performance architecture and experimental data sources to provide high-throughput analytics to a specific experimental dataset. Peptide identification was achieved by a high-performance code, Polygraph, which has been shown to scale well beyond 1000 processors. Visual analytics applications such as PQuad, Cytoscape, or others may be used to visualize protein identities in the context of pathways using data from public repositories such as Kyoto Encyclopedia of Genes and Genomes (KEGG). The end result was that a user can go from experimental spectra to pathway data in a single workflow reducing time-to-solution for analyzing biological data from weeks to minutes.