BioPAX-Parser: parsing and enrichment analysis of BioPAX pathways

Abstract Summary Biological pathways are fundamental for learning about healthy and disease states. Many existing formats support automatic software analysis of biological pathways, e.g. BioPAX (Biological Pathway Exchange). Although some algorithms are available as web application or stand-alone tools, no general graphical application for the parsing of BioPAX pathway data exists. Also, very few tools can perform pathway enrichment analysis (PEA) using pathway encoded in the BioPAX format. To fill this gap, we introduce BiP (BioPAX-Parser), an automatic and graphical software tool aimed at performing the parsing and accessing of BioPAX pathway data, along with PEA by using information coming from pathways encoded in BioPAX. Availability and implementation BiP is freely available for academic and non-profit organizations at https://gitlab.com/giuseppeagapito/bip under the LGPL 2.1, the GNU Lesser General Public License. Supplementary information Supplementary data are available at Bioinformatics online

A Path to Graduation: An Evaluation of Degree Planning Resources at Western Oregon University

Evidence suggests that additional registration tools may improve graduation and retention rates in higher education institutions like Western Oregon University (WOU). The tools that WOU currently employs to support students and advisors could be enhanced to benefit students, advisors, departments, and consequently, the university as a whole. With the guidance of the Student Success and Advising Office and the Office of the Registrar, I conducted research in order to better understand the degree pathway planning needs of students and advisors. Using formal software implementation techniques to elicit software requirements, I evaluated the potential benefits and challenges of available degree pathway planning tools to students, advisors, and administrators. I hope that information generated from this project may be of use to the Office of the Registrar in the process of implementing new systems throughout the continuous improvement of the registration process at WOU. Furthermore, I hope that the recommendations from this research will be a valuable contribution to the university’s mission and its strategic planning initiative, Forward Together. I hope that this project will inspire and inform a successful software implementation and be an asset to the university in its plan to promote student success

SubpathwayMiner: a software package for flexible identification of pathways

With the development of high-throughput experimental techniques such as microarray, mass spectrometry and large-scale mutagenesis, there is an increasing need to automatically annotate gene sets and identify the involved pathways. Although many pathway analysis tools are developed, new tools are still needed to meet the requirements for flexible or advanced analysis purpose. Here, we developed an R-based software package (SubpathwayMiner) for flexible pathway identification. SubpathwayMiner facilitates subpathway identification of metabolic pathways by using pathway structure information. Additionally, SubpathwayMiner also provides more flexibility in annotating gene sets and identifying the involved pathways (entire pathways and sub-pathways): (i) SubpathwayMiner is able to provide the most up to- date pathway analysis results for users; (ii) SubpathwayMiner supports multiple species (~100 eukaryotes, 714 bacteria and 52 Archaea) and different gene identifiers (Entrez Gene IDs, NCBI-gi IDs, UniProt IDs, PDB IDs, etc.) in the KEGG GENE database; (iii) the system is quite efficient in cooperating with other R-based tools in biology. SubpathwayMiner is freely available at http://cran.r-project.org/web/packages/SubpathwayMiner/

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

The MetaCyc database (http://metacyc.org/) provides a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains more than 1800 pathways derived from more than 30 000 publications, and is the largest curated collection of metabolic pathways currently available. Most reactions in MetaCyc pathways are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes and literature citations. BioCyc (http://biocyc.org/) is a collection of more than 1700 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference database, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs contain additional features, including predicted operons, transport systems and pathway-hole fillers. The BioCyc website and Pathway Tools software offer many tools for querying and analysis of PGDBs, including Omics Viewers and comparative analysis. New developments include a zoomable web interface for diagrams; flux-balance analysis model generation from PGDBs; web services; and a new tool called Web Groups

The Importance of Modularity in Bioinformatics Tools

In the last decade the amount of Bioinformatics tools has increased enormously. There are tools to store, analyse, visualize, edit or generate biological data and there are still more in development. Still, the demand for increased functionality in a single piece of software must be balanced by the need for modularity to keep the software maintainable. In complex systems, the conflicting demands of features and maintainability are often solved by plug-in systems.

For example Cytoscape, an open source platform for Complex-Network Analysis and Visualization, is using a plug-in system to allow the extension of the application without changing the core. This not only allows the integration of new functionality without a new release but offers the possibility for other developers to contribute plug-ins which are needed in their research.

Most tools have their own, individual plug-in system to meet the needs of the application. These are often very simple and easy to use. However, the increasing complexity of plug-ins demands more functionality of the plug-in system. We want to reuse components in different contexts, we want to have simple plug-in interfaces and we want to allow communication and dependencies between plug-ins. Many tools implemented in Java are facing these problems and there seems to be a common solution: the integration of an established modularity framework, like OSGi. To our knowledge, a number of developers of bioinformatics tools are already implementing, planning or thinking about the integration of OSGi into their applications, e.g. Cytoscape, Protege, PathVisio, ImageJ, Jalview or Chipster. The adoption of modularity frameworks in the development of bioinformatics applications is steadily increasing and should be considered in the design of new software.

By modularity in the traditional computer science sense, we mean the division of a software application into logical parts with separate concerns. To ease the development of software tools the application is separated into smaller logical parts, which are implemented individually. A set of modules can form a larger application but only if a proper glue is used, OSGi is an example of such a glue. OSGi allows to build an infrastructure into an application to add and use different modules. It provides mechanisms to allow the individual modules to rely on and interact with each other, opening the possibility to put together different modules to solve the problem at hand. Later, modules can be removed and new ones can be added to tackle another problem. As Katy Boerner in her article 'Plug-and-Play Macroscopes' writes, we should 'implement software frameworks that empower domain scientists to assemble their own continuously evolving macroscopes, adding and upgrading existing (and removing obsolete) plug-ins to arrive at a set that is truly relevant for their work'.

Some of these modules are going to be specific for one application but a lot of these modules can actually be reused by other tools. We are talking about general features like the import or export of different file formats, a layout algorithm that could be used by several visualization tools or the lookup in an external online database. Why should every tool implement its own parser or algorithm? Modularity can help to share functionality. There is no need to start from scratch and implement everything anew, thus developers can focus on new and important features.

Adding modularity, or better, a modularity framework to an existing software application is not a trivial task. The developers of Cytoscape are currently undertaking this challenge with the coming version 3. We are also working on the integration of OSGi into our pathway visualization tool PathVisio and we now want to share and compare our experiences, so others can benefit from our discoveries. This will not only help them in making a decision if OSGi is a suitable solution for them but also in the integration process itself

Reactome knowledgebase of human biological pathways and processes

Reactome (http://www.reactome.org) is an expert-authored, peer-reviewed knowledgebase of human reactions and pathways that functions as a data mining resource and electronic textbook. Its current release includes 2975 human proteins, 2907 reactions and 4455 literature citations. A new entity-level pathway viewer and improved search and data mining tools facilitate searching and visualizing pathway data and the analysis of user-supplied high-throughput data sets. Reactome has increased its utility to the model organism communities with improved orthology prediction methods allowing pathway inference for 22 species and through collaborations to create manually curated Reactome pathway datasets for species including Arabidopsis, Oryza sativa (rice), Drosophila and Gallus gallus (chicken). Reactome's data content and software can all be freely used and redistributed under open source terms

Pathway activity analysis of bulk and single-cell RNA-Seq data

Gene expression profiling can produce effective biomarkers that can provide additional information beyond other approaches for characterizing disease. While these approaches are typically performed on standard bulk RNA sequencing data, new methods for RNA sequencing of individual cells have allowed these approaches to be applied at the resolution of a single cell. As these methods enter the mainstream, there is an increased need for user-friendly software that allows researchers without experience in bioinformatics to apply these techniques. In this thesis, I have developed new, user-friendly data resources and software tools to allow researchers to use gene expression signatures in their own datasets. Specifically, I created the Single Cell Toolkit, a user-friendly and interactive toolkit for analyzing single-cell RNA sequencing data and used this toolkit to analyze the pathway activity levels in breast cancer cells before and after cancer therapy. Next, I created and validated a set of activated oncogenic growth factor receptor signatures in breast cancer, which revealed additional heterogeneity within public breast cancer cell line and patient sample RNA sequencing datasets. Finally, I created an R package for rapidly profiling TB samples using a set of 30 existing tuberculosis gene signatures. I applied this tool to look at pathway differences in a dataset of tuberculosis treatment failure samples. Taken together, the results of these studies serve as a set of user-friendly software tools and data sets that allow researchers to rapidly and consistently apply pathway activity methods across RNA sequencing samples