Publishing Interactive Articles: Integrating Journals And Biological Databases

In collaboration with the journal GENETICS, we've developed and launched a pipeline by which interactive full-text HTML/PDF journal articles are published with named entities linked to corresponding resource pages in "WormBase":http://www.wormbase.org/ (WB). Our interactive articles allow a reader to click on over ten different data type objects (gene, protein, transgene, etc.) and be directed to the relevant webpage. This seamless connection from the article to summaries of data types promotes a deeper level of understanding for the naïve reader, and incisive evaluation for the sophisticated reader. Further, this collaboration allows us to identify and collect information before the publication of the article. The pipeline uses automated recognition scripts to identify entities that already exist in the database and a self-reporting form we created at WB that is sent to the author by GENETICS for submitting entities that do not already exist in our database. We include a manual quality control step to make sure ambiguous links are corrected, and that all new entities have been reported and linked properly. The automated entity recognition scripts allows us to potentially link any object found in a database as well as to expand this pipeline to other databases. We have already adapted this pipeline for linking _Saccharomyces cerevisiae_ GENETICS articles to the "Saccharomyces Genome Database":http://www.yeastgenome.org/ (SGD) and are currently expanding this pipeline for linking genes in _Drosophila_ articles to "FlyBase":http://flybase.org/. By integrating journals and databases, we are integrating the major modes of communication in the biological sciences, which will undoubtedly increase the pace of discovery.
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Pay-as-you-go data integration for bio-informatics

Scientific research in bio-informatics is often data-driven and supported by numerous biological databases. A biological database contains factual information collected from scientific experiments and computational analyses about areas including genomics, proteomics, metabolomics, microarray gene expression and phylogenetics. Information contained in biological databases includes gene function, structure, localization (both cellular and chromosomal), clinical effects of mutations as well as similarities of biological sequences and structures. In a growing number of research projects, bio-informatics researchers like to ask combined ques- tions, i.e., questions that require the combination of information from more than one database. We have observed that most bio-informatics papers do not go into detail on the integration of different databases. It has been observed that roughly 30% of all tasks in bio-informatics workflows are data transformation tasks, a lot of time is used to integrate these databases (shown by [1]). As data sources are created and evolve, many design decisions made by their creators. Not all of these choices are documented. Some of such choices are made implicitly based on experience or preference of the creator. Other choices are mandated by the purpose of the data source, as well as inherent data quality issues such as imprecision in measurements, or ongoing scientific debates. Integrating multiple data sources can be difficult. We propose to approach the time-consuming problem of integrating multiple biological databases through the principles of ‘pay-as-you-go’ and ‘good-is-good-enough’. By assisting the user in defin- ing a knowledge base of data mapping rules, schema alignment, trust information and other evidence we allow the user to focus on the work, and put in as little effort as is necessary for the integration to serve the purposes of the user. By using user feedback on query results and trust assessments, the integration can be improved upon over time. The research will be guided by a set of use cases. As the research is in its early stages, we have determined three use cases: Homologues, the representation and integration of groupings. Homology is the relationship between two characteristics that have descended, usually with divergence, from a common ancestral characteristic. A characteristic can be any genic, structural or behavioural feature of an organism Metabolomics integration, with a focus on the TCA cycle. The TCA cycle (also known as the citric acid cycle, or Krebs cycle) is used by aerobic organism to generate energy from the oxidation of carbohydrates, fats and proteins. Bibliography integration and improvement, the correction and expansion of citation databases. [1] I. Wassink. Work flows in life science. PhD thesis, University of Twente, Enschede, January 2010

Toward an interactive article: integrating journals and biological databases.

BACKGROUND: Journal articles and databases are two major modes of communication in the biological sciences, and thus integrating these critical resources is of urgent importance to increase the pace of discovery. Projects focused on bridging the gap between journals and databases have been on the rise over the last five years and have resulted in the development of automated tools that can recognize entities within a document and link those entities to a relevant database. Unfortunately, automated tools cannot resolve ambiguities that arise from one term being used to signify entities that are quite distinct from one another. Instead, resolving these ambiguities requires some manual oversight. Finding the right balance between the speed and portability of automation and the accuracy and flexibility of manual effort is a crucial goal to making text markup a successful venture. RESULTS: We have established a journal article mark-up pipeline that links GENETICS journal articles and the model organism database (MOD) WormBase. This pipeline uses a lexicon built with entities from the database as a first step. The entity markup pipeline results in links from over nine classes of objects including genes, proteins, alleles, phenotypes and anatomical terms. New entities and ambiguities are discovered and resolved by a database curator through a manual quality control (QC) step, along with help from authors via a web form that is provided to them by the journal. New entities discovered through this pipeline are immediately sent to an appropriate curator at the database. Ambiguous entities that do not automatically resolve to one link are resolved by hand ensuring an accurate link. This pipeline has been extended to other databases, namely Saccharomyces Genome Database (SGD) and FlyBase, and has been implemented in marking up a paper with links to multiple databases. CONCLUSIONS: Our semi-automated pipeline hyperlinks articles published in GENETICS to model organism databases such as WormBase. Our pipeline results in interactive articles that are data rich with high accuracy. The use of a manual quality control step sets this pipeline apart from other hyperlinking tools and results in benefits to authors, journals, readers and databases.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Storing, linking, and mining microarray databases using SRS

BACKGROUND: SRS (Sequence Retrieval System) has proven to be a valuable platform for storing, linking, and querying biological databases. Due to the availability of a broad range of different scientific databases in SRS, it has become a useful platform to incorporate and mine microarray data to facilitate the analyses of biological questions and non-hypothesis driven quests. Here we report various solutions and tools for integrating and mining annotated expression data in SRS. RESULTS: We devised an Auto-Upload Tool by which microarray data can be automatically imported into SRS. The dataset can be linked to other databases and user access can be set. The linkage comprehensiveness of microarray platforms to other platforms and biological databases was examined in a network of scientific databases. The stored microarray data can also be made accessible to external programs for further processing. For example, we built an interface to a program called Venn Mapper, which collects its microarray data from SRS, processes the data by creating Venn diagrams, and saves the data for interpretation. CONCLUSION: SRS is a useful database system to store, link and query various scientific datasets, including microarray data. The user-friendly Auto-Upload Tool makes SRS accessible to biologists for linking and mining user-owned databases

Exposing WikiPathways as Linked Open Data

Biology has become a data intensive science. Discovery of new biological facts increasingly relies on the ability to find and match appropriate biological data. For instance for functional annotation of genes of interest or for identification of pathways affected by over-expressed genes. Functional and pathway information about genes and proteins is typically distributed over a variety of databases and the literature.

Pathways are a convenient, easy to interpret way to describe known biological interactions. WikiPathways provides community curated pathways. WikiPathways users integrate their knowledge with facts from the literature and biological databases. The curated pathway is then reviewed and possibly corrected or enriched. Different tools (e.g. Pathvisio and Cytoscape) support the integration of WikiPathways-knowledge for additional tasks, such as the integration with personal data sets. 

Data from WikiPathways is increasingly also used for advanced analysis where it is integrated or compared with other data, Currently, integration with data from different biological sources is mostly done manually. This can be a very time consuming task because the curator often first needs to find the available resources, needs to learn about their specific content and qualities and often spends a lot of time to technically combine the two. 

Semantic web and Linked Data technologies eliminate the barriers between database silos by relying on a set of standards and best practices for representing and describing data. The architecture of the semantic web relies on the architecture of the web itself for integrating and mapping universal resource identifiers (URI), coupled with basic inference mechanisms to enable matching concepts and properties across data sources. Semantic Web and Linked Data technologies are increasingly being successfully applied as integration engines for linking biological elements. 

Exposing WikiPathways content as Linked Open Data to the Semantic Web, enables rapid, semi-automated integration with a the growing amount of biological resources available from the linked open data cloud, it also allows really fast queries of WikiPathways itself. 

We have harmonised WikiPathways content according to a selected set of vocabularies (Biopax, CHEMBL, etc), common to resources already available as Linked Open Data. 
WikiPathways content is now available as Linked Open Data for dynamic querying through a SPARQL endpoint: http://semantics.bigcat.unimaas.nl:8000/sparql

Uncertain groupings: probabilistic combination of grouping data

Probabilistic approaches for data integration have much potential. We view data integration as an iterative process where data understanding gradually increases as the data scientist continuously refines his view on how to deal with learned intricacies like data conflicts. This paper presents a probabilistic approach for integrating data on groupings. We focus on a bio-informatics use case concerning homology. A bio-informatician has a large number of homology data sources to choose from. To enable querying combined knowledge contained in these sources, they need to be integrated. We validate our approach by integrating three real-world biological databases on homology in three iterations

Biodiversity informatics: the challenge of linking data and the role of shared identifiers

A major challenge facing biodiversity informatics is integrating data stored in widely distributed databases. Initial efforts have relied on taxonomic names as the shared identifier linking records in different databases. However, taxonomic names have limitations as identifiers, being neither stable nor globally unique, and the pace of molecular taxonomic and phylogenetic research means that a lot of information in public sequence databases is not linked to formal taxonomic names. This review explores the use of other identifiers, such as specimen codes and GenBank accession numbers, to link otherwise disconnected facts in different databases. The structure of these links can also be exploited using the PageRank algorithm to rank the results of searches on biodiversity databases. The key to rich integration is a commitment to deploy and reuse globally unique, shared identifiers (such as DOIs and LSIDs), and the implementation of services that link those identifiers

Argudas: arguing with gene expression information

In situ hybridization gene expression information helps biologists identify where a gene is expressed. However, the databases that republish the experimental information are often both incomplete and inconsistent. This presentation examines a system, Argudas, designed to help tackle these issues. Argudas is an evolution of an existing system, and so that system is reviewed as a means of both explaining and justifying the behavior of Argudas. Throughout the discussion of Argudas a number of issues will be raised including the appropriateness of argumentation in biology and the challenges faced when integrating apparently similar online biological databases