Graph-based analysis and visualization of experimental results with ONDEX

Motivation: Assembling the relevant information needed to interpret the output from high-throughput, genome scale, experiments such as gene expression microarrays is challenging. Analysis reveals genes that show statistically significant changes in expression levels, but more information is needed to determine their biological relevance. The challenge is to bring these genes together with biological information distributed across hundreds of databases or buried in the scientific literature (millions of articles). Software tools are needed to automate this task which at present is labor-intensive and requires considerable informatics and biological expertise. Results: This article describes ONDEX and how it can be applied to the task of interpreting gene expression results. ONDEX is a database system that combines the features of semantic database integration and text mining with methods for graph-based analysis. An overview of the ONDEX system is presented, concentrating on recently developed features for graph-based analysis and visualization. A case study is used to show how ONDEX can help to identify causal relationships between stress response genes and metabolic pathways from gene expression data. ONDEX also discovered functional annotations for most of the genes that emerged as significant in the microarray experiment, but were previously of unknown function

Neurological Disorders and Publication Abstracts Follow Elements of Social Network Patterns when Indexed Using Ontology Tree-Based Key Term Search

Disorders of the Central Nervous System (CNS) are worldwide causes of morbidity and mortality. In order to further investigate the nature of the CNS research, we generate from an initial reference a controlled vocabulary of CNS disorder-related terms and ontological tree structure for this vocabulary, and then apply the vocabulary in an analysis of the past ten years of abstracts (N = 10,488) from a major neuroscience journal. Using literal search methodology with our terminology tree, we find over 5,200 relationships between abstracts and clinical diagnostic topics. After generating a network graph of these document-topic relationships, we find that this network graph contains characteristics of document-author and other human social networks, including evidence of scale-free and power law-like node distributions. However, we also found qualitative evidence for Z-normal-type (albeit logarithmically skewed) distributions within disorder popularity. Lastly, we discuss potential consumer-centered as well as clinic-centered uses for our ontology and search methodology

Extraction of biological interaction networks from scientific literature

Skusa A, Ruegg A, Koehler J. Extraction of biological interaction networks from scientific literature. BRIEFINGS IN BIOINFORMATICS. 2005;6(3):263-276.Biology can be regarded as a science of networks: interactions between various biological entities (eg genes, proteins, metabolites) on different levels (eg gene regulation, cell signalling) can be represented as graphs and, thus, analysis of such networks might shed new light on the function of biological systems. Such biological networks can be obtained from different sources. The extraction of networks from text is an important technique that requires the integration of several different computational disciplines. This paper summarises the most important steps in network extraction and reviews common approaches and solutions for the extraction of biological networks from scientific literature

Linking experimental results, biological networks and sequence analysis methods using Ontologies and Generalised Data Structures

The structure of a closely integrated data warehouse is described that is designed to link different types and varying numbers of biological networks, sequence analysis methods and experimental results such as those coming from microarrays. The data schema is inspired by a combination of graph based methods and generalised data structures and makes use of ontologies and meta-data. The core idea is to consider and store biological networks as graphs, and to use generalised data structures (GDS) for the storage of further relevant information. This is possible because many biological networks can be stored as graphs: protein interactions, signal transduction networks, metabolic pathways, gene regulatory networks etc. Nodes in biological graphs represent entities such as promoters, proteins, genes and transcripts whereas the edges of such graphs specify how the nodes are related. The semantics of the nodes and edges are defined using ontologies of node and relation types. Besides generic attributes that most biological entities possess (name, attribute description), further information is stored using generalised data structures. By directly linking to underlying sequences (exons, introns, promoters, amino acid sequences) in a systematic way, close interoperability to sequence analysis methods can be achieved. This approach allows us to store, query and update a wide variety of biological information in a way that is semantically compact without requiring changes at the database schema level when new kinds of biological information is added. We describe how this datawarehouse is being implemented by extending the text-mining framework ONDEX to link, support and complement different bioinformatics applications and research activities such as microarray analysis, sequence analysis and modelling/simulation of biological systems. The system is developed under the GPL license and can be downloaded from http://sourceforge.net/projects/ondex/&nbsp

Representation of Genotype and Phenotype in a Coherent Framework Based on Extended L-Systems

A formal language approach for the specification of ALife models is presented. "Relational Growth Grammars" incorporate rulebased, procedural and object-oriented concepts. By enabling parametric Lindenmayer systems to rewrite multiscaled graphs, it becomes possible to represent genes, plant organs and populations as well as developmental aspects of these entities in a common formal framework. Genetic operators (mutation, crossing-over, selection) take the form of simple graph rewrite rules. This is illustrated using Richard Dawkins' "biomorphs", whereas other applications are briefly sketched. The formalism is implemented as part of an interactive software platform