MOTIVATION: Biological network analysis for high-throughput biomedical data interpretation relies heavily on topological characteristics. Networks are commonly composed of nodes representing genes or proteins that are connected by edges when interacting. In this study, we use the rich information available in the Reactome pathway database to build biological networks accounting for small molecules and proteoforms modeled using protein isoforms and post-translational modifications to study the topological changes induced by this refinement of the network representation. RESULTS: We find that improving the interactome modeling increases the number of nodes and interactions, but that isoform and post-translational modification annotation is still limited compared to what can be expected biologically. We also note that small molecule information can distort the topology of the network due to the high connectedness of these molecules, which does not necessarily represent the reality of biology. However, by restricting the connections of small molecules to the context of biochemical reactions, we find that these improve the overall connectedness of the network and reduce the prevalence of isolated components and nodes. Overall, changing the representation of the network alters the prevalence of articulation points and bridges globally but also within and across pathways. Hence, some molecules can gain or lose in biological importance depending on the level of detail of the representation of the biological system, which might in turn impact network-based studies of diseases or druggability. AVAILABILITY AND IMPLEMENTATION: Networks are constructed based on data publicly available in the Reactome Pathway knowledgebase: reactome.org.

Abstract The mapping of amino acid sequences is an essential task in bioinformatics. Notably, the mapping of peptide sequences on a proteome is required for the post-processing of proteomics results. However, this step can quickly become a bottleneck when working with extensive numbers of peptides or large protein sequence databases. Here, we present PeptideMapper, a novel amino acid sequence mapper for both peptide sequences and de novo sequencing identification results. By taking advantage of the latest advances in pattern matching, PeptideMapper achieves unprecedented performance (i.e. up to 1000× faster than state-of-the-art software) in terms of memory footprint and execution speed, with regards to both the indexing and the querying of protein sequence databases. Availability and Implementation PeptideMapper is implemented in the open source Java CompOmics framework under the permissive Apache 2.0 license https://github.com/compomics/compomics-utilities. Supplementary information Supplementary data are available at Bioinformatics online.

MOTIVATION: BioContainers (biocontainers.pro) is an open-source and community-driven framework which provides platform independent executable environments for bioinformatics software. BioContainers allows labs of all sizes to easily install bioinformatics software, maintain multiple versions of the same software and combine tools into powerful analysis pipelines. BioContainers is based on popular open-source projects Docker and rkt frameworks, that allow software to be installed and executed under an isolated and controlled environment. Also, it provides infrastructure and basic guidelines to create, manage and distribute bioinformatics containers with a special focus on omics technologies. These containers can be integrated into more comprehensive bioinformatics pipelines and different architectures (local desktop, cloud environments or HPC clusters). AVAILABILITY AND IMPLEMENTATION: The software is freely available at github.com/BioContainers/. CONTACT: [email protected].

BACKGROUND: The growing interest in the field of proteomics has increased the demand for software tools and applications that process and analyze the resulting data. And even though the purpose of these tools can vary significantly, they usually share a basic set of features, including the handling of protein and peptide sequences, the visualization of (and interaction with) spectra and chromatograms, and the parsing of results from various proteomics search engines. Developers typically spend considerable time and effort implementing these support structures, which detracts from working on the novel aspects of their tool. RESULTS: In order to simplify the development of proteomics tools, we have implemented an open-source support library for computational proteomics, called compomics-utilities. The library contains a broad set of features required for reading, parsing, and analyzing proteomics data. compomics-utilities is already used by a long list of existing software, ensuring library stability and continued support and development. CONCLUSIONS: As a user-friendly, well-documented and open-source library, compomics-utilities greatly simplifies the implementation of the basic features needed in most proteomics tools. Implemented in 100% Java, compomics-utilities is fully portable across platforms and architectures. Our library thus allows the developers to focus on the novel aspects of their tools, rather than on the basic functions, which can contribute substantially to faster development, and better tools for proteomics.

BACKGROUND: With the growing amount of biomedical data available in public databases it has become increasingly important to annotate data in a consistent way in order to allow easy access to this rich source of information. Annotating the data using controlled vocabulary terms and ontologies makes it much easier to compare and analyze data from different sources. However, finding the correct controlled vocabulary terms can sometimes be a difficult task for the end user annotating these data. RESULTS: In order to facilitate the location of the correct term in the correct controlled vocabulary or ontology, the Ontology Lookup Service was created. However, using the Ontology Lookup Service as a web service is not always feasible, especially for researchers without bioinformatics support. We have therefore created a Java front end to the Ontology Lookup Service, called the OLS Dialog, which can be plugged into any application requiring the annotation of data using controlled vocabulary terms, making it possible to find and use controlled vocabulary terms without requiring any additional knowledge about web services or ontology formats. CONCLUSIONS: As a user-friendly open source front end to the Ontology Lookup Service, the OLS Dialog makes it straightforward to include controlled vocabulary support in third-party tools, which ultimately makes the data even more valuable to the biomedical community.

BACKGROUND: Proteomics is the study of the proteome, and is critical to the understanding of cellular processes. Two central and related tasks of proteomics are protein identification and protein characterization. Many small laboratories are interested in the characterization of a small number of proteins, e.g., how posttranslational modifications change under different conditions. RESULTS: We have developed a software tool called MassSorter for administrating and analyzing data from peptide mass fingerprinting experiments on proteins with known amino acid sequences. It is meant for small scale mass spectrometry laboratories that are interested in posttranslational modifications of known proteins. Several experiments can be compared simultaneously, and the matched and unmatched peak values are clearly indicated. The hits can be sorted according to m/z values (default) or according to the sequence of the protein. Filters defined by the user can mark autolytic protease peaks and other contaminating peaks (keratins, proteins co-migrating with the protein of interest, etc.). Unmatched peaks can be further analyzed for unexpected modifications by searches against a local version of the UniMod database. They can also be analyzed for unexpected cleavages, a highly useful feature for proteins that undergo maturation by proteolytic cleavage, creating new N- or C-terminals. Additional tools exist for visualization of the results, like sequence coverage, accuracy plots, different types of statistics, 3D models, etc. The program and a tutorial are freely available for academic users at http://www.bioinfo.no/software/massSorter. CONCLUSION: MassSorter has a number of useful features that can promote the analysis and administration of MS-data.