Incompatible data storage formats have hindered the sharing and analyses of digital representations of biological pathways. BioPAX is a standardized language supported by >40 databases and software tools for exchanging pathway data. Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.

A rapidly growing corpus of formal, computable pathway information can be used to answer important biological questions including finding non-trivial connections between cellular processes, identifying significantly altered portions of the cellular network in a disease state and building predictive models that can be used for precision medicine. Due to its complexity and fragmented nature, however, working with pathway data is still difficult. We present Paxtools, a Java library that contains algorithms, software components and converters for biological pathways represented in the standard BioPAX language. Paxtools allows scientists to focus on their scientific problem by removing technical barriers to access and analyse pathway information. Paxtools can run on any platform that has a Java Runtime Environment and was tested on most modern operating systems. Paxtools is open source and is available under the Lesser GNU public license (LGPL), which allows users to freely use the code in their software systems with a requirement for attribution. Source code for the current release (4.2.0) can be found in Software S1. A detailed manual for obtaining and using Paxtools can be found in Protocol S1. The latest sources and release bundles can be obtained from biopax.org/paxtools.

The Integrating Network Objects with Hierarchies (INOH) database is a highly structured, manually curated database of signal transduction pathways including Mammalia, Xenopus laevis, Drosophila melanogaster, Caenorhabditis elegans and canonical. Since most pathway knowledge resides in scientific articles, the database focuses on curating and encoding textual knowledge into a machine-processable form. We use a hierarchical pathway representation model with a compound graph, and every pathway component in the INOH database is annotated by a set of uniquely developed ontologies. Finally, we developed the Similarity Search using the combination of a compound graph and hierarchical ontologies. The INOH database is to be a good resource for many users who want to analyze a large protein network. INOH ontologies and 73 signal transduction and 29 metabolic pathway diagrams (including over 6155 interactions and 3395 protein entities) are freely available in INOH XML and BioPAX formats. Database URL: http://www.inoh.org/

Motivation: Since their initial development, integration and construction of databases for molecular-level data have progressed. Though biological molecules are related to each other and form a complex system, the information is stored in the vast archives of the literature or in diverse databases. There is no unified naming convention for biological object, and biological terms may be ambiguous or polysemic. This makes the integration and interaction of databases difficult. In order to eliminate these problems, machine-readable natural language resources appear to be quite promising. We have developed a workbench for protein name abbreviation dictionary (PNAD) building. Results: We have developed PNAD Construction Support System (PNAD-CSS), which offers various convenient facilities to decrease the construction costs of a protein name abbreviation dictionary of which entries are collected from abstracts in biomedical papers. The system allows the users to concentrate on higher level interpretation by removing some troublesome tasks, e.g. management of abstracts, extracting protein names and their abbreviations, and so on. To extract a pair of protein names and abbreviations, we have developed a hybrid system composed of the PROPER System and the PNAD System. The PNAD System can extract the pairs from parenthetical-paraphrases involved in protein names, the PROPER System identified these pairs, with 98.95% precision, 95.56% recall and 97.58% complete precision. Availability: PROPER System is freely available from http://www.hgc.ims.u-tokyo.ac.jp/service/tooldoc/KeX/intro.html. The other software are also available on request. Contact the authors. Contact: mikio@ims.u-tokyo.ac.jp

Motivations: Signal transduction is the common term used to define a diverse topic that encompasses a large body of knowledge about the biochemical mechanisms. Since most of the knowledge of signal transduction resides in scientific articles and is represented by texts in natural language or by diagrams, there is the need of a knowledge representation model for signal transduction pathways that can be as readily processed by a computer as it is easily understood by humans. Results: A signal transduction pathway representation model is presented. It is based on a compound graph structure and is designed to handle the diversity and hierarchical structure of pathways. A prototype knowledge base was implemented on a deductive database and a number of biological queries are demonstrated on it. Contact: fukuda-cbrc@aist.go.jp; ichiro@ims.u-tokyo.ac.jp 2 Present address: Computational Biology Research Center (CBRC), NationalInstitute of Advanced Industrial Science and Technology (AIST) 2-41-6 Aomi, Koutou-ku, Tokyo 135-0064, Japan.