Reactome pathway analysis to enrich biological discovery in proteomics data sets

The pharmaceutical industry has begun to leverage a range of new technologies (proteomics, pharmacogenomics, metabolomics and molecular toxicology [e.g., toxicogenomics]) and analysis tools that are becoming increasingly integrated in the area of drug discovery and development. The approach of analyzing the vast amount of toxicogenomics data generated using molecular pathway and networks analysis tools in combination with analysis of reference data will be the main focus of this review. We will demonstrate how this combined approach can increase the understanding of the molecular mechanisms that lead to chemical-induced toxicity and application of this knowledge to compound risk assessment. We will provide an example of the insights achieved through a molecular toxicology analysis based on the well-known hepatotoxicant lipopolysaccharide to illustrate the utility of these new tools in the analysis of complex data sets, both in vivo and in vitro. The ultimate objective is a better lead selection process that improves the chances for success across the different stages of drug discovery and development.

As the US Army transitions to a regionally aligned architecture, analysts are seeking new and innovative geospatial applications to inform the operations planning process for unfamiliar terrain. In this paper, we outline our systems approach used to develop a suite of geospatial analysis tools used to plan military operations in the urban environment as part of a larger research effort to develop tactical geospatial tools for the US Army (Situational Awareness Geospatially Enabled (SAGE)). This paper describes the iterative stakeholder analysis used to accurately identify operational needs, requirements, capability gaps and associated importance, and the comprehensive methodology used to generate the suite of tools. This research was unique in that we simultaneously developed a recommended suite of tools in addition to a formalized geospatial tool development methodology for the clients' later use. This paper describes the development and application of this methodology which categorically decomposed military operations, aligned stakeholder requirements with existing capabilities, and architected new geospatial capabilities consistent with the existing geospatial program architecture. Key to the developed methodology is that it produced geospatial tools which capitalized upon previously developed technologies, maintained the program's operational theme, and logically developed capabilities which directly satisfied requirements.

BackgroundThe primary goal of pathway analysis using transcriptome data is to find significantly perturbed pathways. However, pathway analysis is not always successful in identifying pathways that are truly relevant to the context under study. A major reason for this difficulty is that a single gene is involved in multiple pathways. In the KEGG pathway database, there are 146 genes, each of which is involved in more than 20 pathways. Thus activation of even a single gene will result in activation of many pathways. This complex relationship often makes the pathway analysis very difficult. While we need much more powerful pathway analysis methods, a readily available alternative way is to incorporate the literature information.ResultsIn this study, we propose a novel approach for prioritizing pathways by combining results from both pathway analysis tools and literature information. The basic idea is as follows. Whenever there are enough articles that provide evidence on which pathways are relevant to the context, we can be assured that the pathways are indeed related to the context, which is termed as relevance in this paper. However, if there are few or no articles reported, then we should rely on the results from the pathway analysis tools, which is termed as significance in this paper. We realized this concept as an algorithm by introducing Context Score and Impact Score and then combining the two into a single score. Our method ranked truly relevant pathways significantly higher than existing pathway analysis tools in experiments with two data sets.ConclusionsOur novel framework was implemented as ContextTRAP by utilizing two existing tools, TRAP and BEST. ContextTRAP will be a useful tool for the pathway based analysis of gene expression data since the user can specify the context of the biological experiment in a set of keywords. The web version of ContextTRAP is available at http://biohealth.snu.ac.kr/software/contextTRAP.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1335-8) contains supplementary material, which is available to authorized users.

An interactive mutation database for human coagulation factor IX provides novel insights into the phenotypes and genetics of hemophilia B

DEFINING TEXT MINING Text mining can be broadly defined as a knowledge-intensive process in which a user interacts with a document collection over time by using a suite of analysis tools. In a manner analogous to data mining, text mining seeks to extract useful information from data sources through the identification and exploration of interesting patterns. In the case of text mining, however, the data sources are document collections, and interesting patterns are found not among formalized database records but in the unstructured textual data in the documents in these collections. Certainly, text mining derives much of its inspiration and direction from seminal research on data mining. Therefore, it is not surprising to find that text mining and data mining systems evince many high-level architectural similarities. For instance, both types of systems rely on preprocessing routines, pattern-discovery algorithms, and presentation-layer elements such as visualization tools to enhance the browsing of answer sets. Further, text mining adopts many of the specific types of patterns in its core knowledge discovery operations that were first introduced and vetted in data mining research. Because data mining assumes that data have already been stored in a structured format, much of its preprocessing focus falls on two critical tasks: Scrubbing and normalizing data and creating extensive numbers of table joins. In contrast, for text mining systems, preprocessing operations center on the identification and extraction of representative features for natural language documents.

Despite the widespread use of immune repertoire profiling, there is currently no standardized format for output files from VDJ analysis. Researchers utilize software such as IgBlast and IMGT/High V-Quest to perform VDJ analysis and infer germline rearrangements. Each of these software tools produces results in a different file format, and can identify the same result using different labels. These differences make it challenging for users to perform additional analysis using the output file from one software to the next. We have addressed this problem by developing a standardized file format for representing results. The purpose of VDJML is to provide a common standardized format for different VDJ analysis applications and to facilitate downstream processing of the results in an application-agnostic manner. The VDJML file format is accompanied by a suite of analysis tools, which are accessible via command line and written in C++ and python. The VDJML suite will allow users to streamline their VDJ analysis and facilitate the sharing of scientific knowledge within the community. The VDJML suite and documentation are available from https://www.vdjserver.org/software. We welcome participation from others in developing the file format standard, as well as code contributions.

Foodborne pathogen surveillance in the United States is transitioning from strain identification using restriction digest technology (pulsed-field gel electrophoresis [PFGE]) to shotgun sequencing of the entire genome (whole-genome sequencing [WGS]). WGS requires a new suite of analysis tools, some of which have long histories in academia but are new to the field of public health and regulatory decision making. Although the general workflow is fairly standard for collecting and analyzing WGS data for disease surveillance, there are a number of differences in how the data are collected and analyzed across public health agencies, both nationally and internationally. This impedes collaborative public health efforts, so national and international efforts are underway to enable direct comparison of these different analysis methods. Ultimately, the harmonization efforts will allow the (mutually trusted and understood) production and analysis of WGS data by labs and agencies worldwide, thus improving outbreak response capabilities globally. This review provides a historical perspective on the use of WGS for pathogen tracking and summarizes the efforts underway to ensure the major steps in phylogenomic pipelines used for pathogen disease surveillance can be readily validated. The tools for doing this will ensure that the results produced are sound, reproducible, and comparable across different analytic approaches.

In an effort to provide a common platform for utilizing and developing data analysis and assessment applications in heterogeneous high performance computing (HPC) environments, the Classified Data Analysis and Assessment Center (CDAAC) has developed the computational science environment (CSE). CSE provides a stable suite of data analysis and assessment tools, applications, and libraries, which are common across most HPC environments and standard 64-bit Linux workstations. CSE also provides an experimental, cutting-edge suite of developer tools that can enhance the data analysis and assessment process. Most importantly, CSE provides a common platform that enables the development of full-featured portable HPC applications. This paper provides an overview of how CSE works and highlights some of the benefits of CSE.

Advancement of next generation sequencing and high-throughput technologies has resulted in generation of multi-level of ‘OMICS’ data for many organisms. However, these data are often individually scattered across different repositories based on data type, making it difficult to integrate them. We have addressed this issue through our in-house developed Soybean Knowledge Base [1,2] (SoyKB) framework, a comprehensive web-based resource. It acts as a centralized repository for soybean multi-omics data, and is equipped with an array of bioinformatics analytical and graphical visualization tools. It is available at http://soykb.org and has proven to be a great success with more than 500 registered users. Users working on other biological organisms including plants, animals and biomedical diseases have similar needs and the developed framework can be expanded to make the visualization and analysis tools function for other organisms, without having to reinvent the wheel. To achieve this we have developed KBCommons, a platform that automates the process of establishing the database and making the tools for other organisms available via a dedicated web resource. It provides information for six entities including genes/proteins, microRNAs/sRNAs, metabolites, SNP, traits as well as plant introduction or strains/populations. It also incorporates several multi-omics datasets including transcriptomics, proteomics, metabolomics, epigenomics, molecular breeding and other types. We have currently expanded KBCommons framework and tools to Zea mays, Arabidopsis, Mus musculus and Homo sapiens. We have integrated various genomics dataset for maize including RNAseq B73 mutants and Tassel meristem from our collaborators. It provides a suite of tools such as the gene/metabolite pathway viewer, Protein Bio-Viewer, heatmaps, scatter plots and hierarchical clustering. It also provides access to PGen, Pegasus analytics workflows developed for genomics variations analysis. It also has suite of tools for differential expression analysis of transcriptomics and other multi-omics datasets including venn diagrams, volcano plots, function enrichment and gene modules.

The continuously evolving field of proteomics produces increasing amounts of data while improving the quality of protein identifications. Albeit quantitative measurements are becoming more popular, many proteomic studies are still based on non-quantitative methods for protein identification. These studies result in potentially large sets of identified proteins, where the biological interpretation of proteins can be challenging. Systems biology develops innovative network-based methods, which allow an integrated analysis of these data. Here we present a novel approach, which combines prior knowledge of protein-protein interactions (PPI) with proteomics data using functional similarity measurements of interacting proteins. This integrated network analysis exactly identifies network modules with a maximal consistent functional similarity reflecting biological processes of the investigated cells. We validated our approach on small (H9N2 virus-infected gastric cells) and large (blood constituents) proteomic data sets. Using this novel algorithm, we identified characteristic functional modules in virus-infected cells, comprising key signaling proteins (e.g. the stress-related kinase RAF1) and demonstrate that this method allows a module-based functional characterization of cell types. Analysis of a large proteome data set of blood constituents resulted in clear separation of blood cells according to their developmental origin. A detailed investigation of the T-cell proteome further illustrates how the algorithm partitions large networks into functional subnetworks each representing specific cellular functions. These results demonstrate that the integrated network approach not only allows a detailed analysis of proteome networks but also yields a functional decomposition of complex proteomic data sets and thereby provides deeper insights into the underlying cellular processes of the investigated system.

BackgroundUnderstanding the information-processing capabilities of signal transduction networks, how those networks are disrupted in disease, and rationally designing therapies to manipulate diseased states require systematic and accurate reconstruction of network topology. Data on networks central to human physiology, such as the inflammatory signalling networks analyzed here, are found in a multiplicity of on-line resources of pathway and interactome databases (Cancer CellMap, GeneGo, KEGG, NCI-Pathway Interactome Database (NCI-PID), PANTHER, Reactome, I2D, and STRING). We sought to determine whether these databases contain overlapping information and whether they can be used to construct high reliability prior knowledge networks for subsequent modeling of experimental data.ResultsWe have assembled an ensemble network from multiple on-line sources representing a significant portion of all machine-readable and reconcilable human knowledge on proteins and protein interactions involved in inflammation. This ensemble network has many features expected of complex signalling networks assembled from high-throughput data: a power law distribution of both node degree and edge annotations, and topological features of a “bow tie” architecture in which diverse pathways converge on a highly conserved set of enzymatic cascades focused around PI3K/AKT, MAPK/ERK, JAK/STAT, NFκB, and apoptotic signaling. Individual pathways exhibit “fuzzy” modularity that is statistically significant but still involving a majority of “cross-talk” interactions. However, we find that the most widely used pathway databases are highly inconsistent with respect to the actual constituents and interactions in this network. Using a set of growth factor signalling networks as examples (epidermal growth factor, transforming growth factor-beta, tumor necrosis factor, and wingless), we find a multiplicity of network topologies in which receptors couple to downstream components through myriad alternate paths. Many of these paths are inconsistent with well-established mechanistic features of signalling networks, such as a requirement for a transmembrane receptor in sensing extracellular ligands.ConclusionsWide inconsistencies among interaction databases, pathway annotations, and the numbers and identities of nodes associated with a given pathway pose a major challenge for deriving causal and mechanistic insight from network graphs. We speculate that these inconsistencies are at least partially attributable to cell, and context-specificity of cellular signal transduction, which is largely unaccounted for in available databases, but the absence of standardized vocabularies is an additional confounding factor. As a result of discrepant annotations, it is very difficult to identify biologically meaningful pathways from interactome networks a priori. However, by incorporating prior knowledge, it is possible to successively build out network complexity with high confidence from a simple linear signal transduction scaffold. Such reduced complexity networks appear suitable for use in mechanistic models while being richer and better justified than the simple linear pathways usually depicted in diagrams of signal transduction.

With the imminent emergence of low-cost tracking solutions, everyday VR users will soon experience the enhanced immersion of natural walking. Even with consumer-grade room-scale tracking, exploring large virtual environments can be made possible using a software solution known as redirected walking. Wide adoption of this technique has been hindered by the complexity and subtleties involved in successfully deploying redirection. To address this matter, we introduce the Redirected Walking Toolkit, to serve as a unified platform for developing, benchmarking, and deploying redirected walking algorithms. Our design enables seamless integration with standard virtual reality configurations, requiring minimal setup effort for content developers. The toolkit's flexible architecture offers an interface that is not only easy to extend, but also complimented with a suite of simulation tools for testing and analysis. We envision the Redirected Walking Toolkit to be a common testbed for VR researchers as well as a publicly-available tool for large virtual exploration in virtual reality applications.

We present a new version of Babelomics, a complete suite of web tools for functional analysis of genome-scale experiments, with new and improved tools. New functionally relevant terms have been included such as CisRed motifs or bioentities obtained by text-mining procedures. An improved indexing has considerably speeded up several of the modules. An improved version of the FatiScan method for studying the coordinate behaviour of groups of functionally related genes is presented, along with a similar tool, the Gene Set Enrichment Analysis. Babelomics is now more oriented to test systems biology inspired hypotheses. Babelomics can be found at .

This paper presents LinguaKit, a multilingual suite of tools for analysis, extraction, annotation and linguistic correction, as well as its integration into a Big Data infrastructure. LinguaKit allows the user to perform different tasks such as PoS-tagging, syntactic parsing, coreference resolution (among others), including applications for relation extraction, sentiment analysis, summarization, extraction of multiword expressions, or entity linking to DBpedia. Most modules work in four languages: Portuguese, Spanish, English, and Galician. The system is programmed in Perl and is freely available under a GPLv3 license.

BackgroundOnce a list of differentially expressed genes has been identified from a microarray experiment, a subsequent post-analysis task is required in order to find the main biological processes associated to the experimental system. This paper describes two pathways analysis tools, ArrayUnlock and Ingenuity Pathways Analysis (IPA) to deal with the post-analyses of microarray data, in the context of the EADGENE and SABRE post-analysis workshop. Dataset employed in this study proceeded from an experimental chicken infection performed to study the host reactions after a homologous or heterologous secondary challenge with two species of Eimeria.ResultsAnalysis of the same microarray data source employing both commercial pathway analysis tools in parallel let to identify several biological and/or molecular functions altered in the chicken Eimeria maxima infection model, including several immune system related pathways. Biological functions differentially altered in the homologous and heterologous second infection were identified. Similarly, the effect of the timing in a homologous second infection was characterized by several biological functions.ConclusionFunctional analysis with ArrayUnlock and IPA provided information related to functional differences with the three comparisons of the chicken infection leading to similar conclusions. ArrayUnlock let an improvement of the annotations of the chicken genome adding InterPro annotations to the data set file. IPA provides two powerful tools to understand the pathway analysis results: the networks and canonical pathways that showed several pathways related to an adaptative immune response.