Using the Semantic Web for Rapid Integration of WikiPathways with Other Biological Online Data Resources

The diversity of online resources storing biological data in different formats provides a challenge for bioinformaticians to integrate and analyse their biological data. The semantic web provides a standard to facilitate knowledge integration using statements built as triples describing a relation between two objects. WikiPathways, an online collaborative pathway resource, is now available in the semantic web through a SPARQL endpoint at http://sparql.wikipathways.org. Having biological pathways in the semantic web allows rapid integration with data from other resources that contain information about elements present in pathways using SPARQL queries. In order to convert WikiPathways content into meaningful triples we developed two new vocabularies that capture the graphical representation and the pathway logic, respectively. Each gene, protein, and metabolite in a given pathway is defined with a standard set of identifiers to support linking to several other biological resources in the semantic web. WikiPathways triples were loaded into the Open PHACTS discovery platform and are available through its Web API (https://dev.openphacts.org/docs) to be used in various tools for drug development. We combined various semantic web resources with the newly converted WikiPathways content using a variety of SPARQL query types and third-party resources, such as the Open PHACTS API. The ability to use pathway information to form new links across diverse biological data highlights the utility of integrating WikiPathways in the semantic web.     

The Genome Sequence DataBase: towards an integrated functional genomics resource.

During 1998 the primary focus of the Genome Sequence DataBase (GSDB; http://www.ncgr.org/gsdb ) located at the National Center for Genome Resources (NCGR) has been to improve data quality, improve data collections, and provide new methods and tools to access and analyze data. Data quality has been improved by extensive curation of certain data fields necessary for maintaining data collections and for using certain tools. Data quality has also been increased by improvements to the suite of programs that import data from the International Nucleotide Sequence Database Collaboration (IC). The Sequence Tag Alignment and Consensus Knowledgebase (STACK), a database of human expressed gene sequences developed by the South African National Bioinformatics Institute (SANBI), became available within the last year, allowing public access to this valuable resource of expressed sequences. Data access was improved by the addition of the Sequence Viewer, a platform-independent graphical viewer for GSDB sequence data. This tool has also been integrated with other searching and data retrieval tools. A BLAST homology search service was also made available, allowing researchers to search all of the data, including the unique data, that are available from GSDB. These improvements are designed to make GSDB more accessible to users, extend the rich searching capability already present in GSDB, and to facilitate the transition to an integrated system containing many different types of biological data.     

Mining relational paths in integrated biomedical data

Much life science and biology research requires an understanding of complex relationships between biological entities (genes, compounds, pathways, diseases, and so on). There is a wealth of data on such relationships in publicly available datasets and publications, but these sources are overlapped and distributed so that finding pertinent relational data is increasingly difficult. Whilst most public datasets have associated tools for searching, there is a lack of searching methods that can cross data sources and that in particular search not only based on the biological entities themselves but also on the relationships between them. In this paper, we demonstrate how graph-theoretic algorithms for mining relational paths can be used together with a previous integrative data resource we developed called Chem2Bio2RDF to extract new biological insights about the relationships between such entities. In particular, we use these methods to investigate the genetic basis of side-effects of thiazolinedione drugs, and in particular make a hypothesis for the recently discovered cardiac side-effects of Rosiglitazone (Avandia) and a prediction for Pioglitazone which is backed up by recent clinical studies.     

An application of fuzzy logic to build ecological sympatry networks

In recent years sympatry networks have been proposed as a mean to perform biogeographic analysis, but their computation posed practical difficulties that limited their use. We propose a novel approach, bringing closer the application of well-established network analysis tools to the study of sympatry patterns using both geographic and environmental data associated with the occurrence of species. Our proposed algorithm, SGraFuLo, combines the use of fuzzy logic and numerical methods to directly compute the network of interest from point locality records, without the need of specialized tools, such as geographic information systems, thereby simplifying the process for end users. By posing the problem in matrix terms, SGraFuLo is able to achieve remarkable efficiency even for large datasets, taking advantage of well established scientific computing algorithms. We present sympatry networks constructed using real-world data collected in Mexico and Central America and highlight the potential of our approach in the analysis of overlapping niches of species that could have important applications even in evolutionary studies. We also present details on the design and implementation of the algorithm, as well as experiments that show its efficiency. The source code is freely released and datasets are also available to support the reproducibility of our results.     

IndexToolkit: an open source toolbox to index protein databases for high-throughput proteomics

A software package, IndexToolkit, aimed at overcoming the disadvantage of FASTA-format databases for frequent searching, is developed to utilize an indexing strategy to substantially accelerate sequence queries. IndexToolkit includes user-friendly tools and an Application Programming Interface (API) to facilitate indexing, storage and retrieval of protein sequence databases. As open source, it provides a sequence-retrieval developing framework, which is easily extensible for high-speed-request proteomic applications, such as database searching or modification discovering. We applied IndexToolkit to database searching engine pFind to demonstrate its effect. Experimental studies show that IndexToolkit is able to support significantly faster searches of protein database. AVAILABILITY: The IndexToolkit is free to use under the open source GNU GPL license. The source code and the compiled binary can be freely accessed through the website http://pfind.jdl.ac.cn/IndexToolkit. In this website, the more detailed information including screenshots and documentations for users and developers is also available.     

Data standards for flow cytometry

Flow cytometry (FCM) is an analytical tool widely used for cancer and HIV/AIDS research, and treatment, stem cell manipulation and detecting microorganisms in environmental samples. Current data standards do not capture the full scope of FCM experiments and there is a demand for software tools that can assist in the exploration and analysis of large FCM datasets. We are implementing a standardized approach to capturing, analyzing, and disseminating FCM data that will facilitate both more complex analyses and analysis of datasets that could not previously be efficiently studied. Initial work has focused on developing a community-based guideline for recording and reporting the details of FCM experiments. Open source software tools that implement this standard are being created, with an emphasis on facilitating reproducible and extensible data analyses. As well, tools for electronic collaboration will assist the integrated access and comprehension of experiments to empower users to collaborate on FCM analyses. This coordinated, joint development of bioinformatics standards and software tools for FCM data analysis has the potential to greatly facilitate both basic and clinical research--impacting a notably diverse range of medical and environmental research areas.     

Advancing analytical algorithms and pipelines for billions of microbial sequences

The vast number of microbial sequences resulting from sequencing efforts using new technologies require us to re-assess currently available analysis methodologies and tools. Here we describe trends in the development and distribution of software for analyzing microbial sequence data. We then focus on one widely used set of methods, dimensionality reduction techniques, which allow users to summarize and compare these vast datasets. We conclude by emphasizing the utility of formal software engineering methods for the development of computational biology tools, and the need for new algorithms for comparing microbial communities. Such large-scale comparisons will allow us to fulfill the dream of rapid integration and comparison of microbial sequence data sets, in a replicable analytical environment, in order to describe the microbial world we inhabit.     

Dynamic spectrum quality assessment and iterative computational analysis of shotgun proteomic data: toward more efficient identification of post-translational modifications, sequence polymorphisms, and novel peptides

In mass spectrometry-based proteomics, frequently hundreds of thousands of MS/MS spectra are collected in a single experiment. Of these, a relatively small fraction is confidently assigned to peptide sequences, whereas the majority of the spectra are not further analyzed. Spectra are not assigned to peptides for diverse reasons. These include deficiencies of the scoring schemes implemented in the database search tools, sequence variations (e.g. single nucleotide polymorphisms) or omissions in the database searched, post-translational or chemical modifications of the peptide analyzed, or the observation of sequences that are not anticipated from the genomic sequence (e.g. splice forms, somatic rearrangement, and processed proteins). To increase the amount of information that can be extracted from proteomic MS/MS datasets we developed a robust method that detects high quality spectra within the fraction of spectra unassigned by conventional sequence database searching and computes a quality score for each spectrum. We also demonstrate that iterative search strategies applied to such detected unassigned high quality spectra significantly increase the number of spectra that can be assigned from datasets and that biologically interesting new insights can be gained from existing data.     

The ImageJ ecosystem: Open‐source software for image visualization,processing, and analysis

For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open‐source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative user and developer communities. The close collaboration between programmers and users has resulted in adaptations to accommodate new challenges in image analysis that address the needs of ImageJ's diverse user base. ImageJ consists of many components, some relevant primarily for developers and a vast collection of user‐centric plugins. It is available in many forms, including the widely used Fiji distribution. We refer to this entire ImageJ codebase and community as the ImageJ ecosystem. Here we review the core features of this ecosystem and highlight how ImageJ has responded to imaging technology advancements with new plugins and tools in recent years. These plugins and tools have been developed to address user needs in several areas such as visualization, segmentation, and tracking of biological entities in large, complex datasets. Moreover, new capabilities for deep learning are being added to ImageJ, reflecting a shift in the bioimage analysis community towards exploiting artificial intelligence. These new tools have been facilitated by profound architectural changes to the ImageJ core brought about by the ImageJ2 project. Therefore, we also discuss the contributions of ImageJ2 to enhancing multidimensional image processing and interoperability in the ImageJ ecosystem.     

Categorizer: a tool to categorize genes into user-defined biological groups based on semantic similarity

Background

Communalities between large sets of genes obtained from high-throughput experiments are often identified by searching for enrichments of genes with the same Gene Ontology (GO) annotations. The GO analysis tools used for these enrichment analyses assume that GO terms are independent and the semantic distances between all parent–child terms are identical, which is not true in a biological sense. In addition these tools output lists of often redundant or too specific GO terms, which are difficult to interpret in the context of the biological question investigated by the user. Therefore, there is a demand for a robust and reliable method for gene categorization and enrichment analysis.

Results

We have developed Categorizer, a tool that classifies genes into user-defined groups (categories) and calculates p-values for the enrichment of the categories. Categorizer identifies the biologically best-fit category for each gene by taking advantage of a specialized semantic similarity measure for GO terms. We demonstrate that Categorizer provides improved categorization and enrichment results of genetic modifiers of Huntington’s disease compared to a classical GO Slim-based approach or categorizations using other semantic similarity measures.

Conclusion

Categorizer enables more accurate categorizations of genes than currently available methods. This new tool will help experimental and computational biologists analyzing genomic and proteomic data according to their specific needs in a more reliable manner.     

Database design for ecologists: Composing core entities with observations

The ecoinformatics community recognizes that ecological synthesis across studies, space, and time will require new informatics tools and infrastructure. Recent advances have been encouraging, but many problems still face ecologists who manage their own datasets, prepare data for archiving, and search data stores for synthetic research. In this paper, we describe how work by the Canopy Database Project (CDP) might enable use of database technology by field ecologists: increasing the quality of database design, improving data validation, and providing structural and semantic metadata — all of which might improve the quality of data archives and thereby help drive ecological synthesis.The CDP has experimented with conceptual components for database design, templates, to address information technology issues facing ecologists. Templates represent forest structures and observational measurements on these structures. Using our software, researchers select templates to represent their study’s data and can generate normalized relational databases. Information hidden in those databases is used by ancillary tools, including data intake forms and simple data validation, data visualization, and metadata export. The primary question we address in this paper is, which templates are the right templates.We argue for defining simple templates (with relatively few attributes) that describe the domain's major entities, and for coupling those with focused and flexible observation templates. We present a conceptual model for the observation data type, and show how we have implemented the model as an observation entity in the DataBank database designer and generator. We show how our visualization tool CanopyView exploits metadata made explicit by DataBank to help scientists with analysis and synthesis. We conclude by presenting future plans for tools to conduct statistical calculations common to forest ecology and to enhance data mining with DataBank databases.DataBank could be extended to another domain by replacing our forest–ecology-specific templates with those for the new domain. This work extends the basic computer science idea of abstract data types and user-defined types to ecology-specific database design tools for individual users, and applies to ecoinformatics the software engineering innovations of domain-specific languages, software patterns, components, refactoring, and end-user programming.     

From genomics to metagenomics

Next-generation sequencing has changed metagenomics. However, sequencing DNA is no longer the bottleneck, rather, the bottleneck is computational analysis and also interpretation. Computational cost is the obvious issue, as is tool limitations, considering most of the tools we routinely use have been built for clonal genomics or are being adapted to microbial communities. The current trend in metagenomics analysis is toward reducing computational costs through improved algorithms and through analysis strategies. Data sharing and interoperability between tools are critical, since computation for metagenomic datasets is very high.