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.

MOTIVATION: Single nucleotide polymorphism (SNP) genotyping arrays remain an attractive platform for assaying copy number variants (CNVs) in large population-wide cohorts. However, current tools for calling CNVs are still prone to extensive false positive calls when applied to biobank scale arrays. Moreover, there is a lack of methods exploiting cohorts with trios available (e.g. nuclear family) to assist in quality control and downstream analyses following the calling. RESULTS: We developed SeeCiTe (Seeing CNVs in Trios), a novel CNV-quality control tool that postprocesses output from current CNV-calling tools exploiting child-parent trio data to classify calls in quality categories and provide a set of visualizations for each putative CNV call in the offspring. We apply it to the Norwegian Mother, Father and Child Cohort Study (MoBa) and show that SeeCiTe improves the specificity and sensitivity compared to the common empiric filtering strategies. To our knowledge, it is the first tool that utilizes probe-level CNV data in trios (and singletons) to systematically highlight potential artifacts and visualize signal intensities in a streamlined fashion suitable for biobank scale studies. AVAILABILITY AND IMPLEMENTATION: The software is implemented in R with the source code freely available at https://github.com/aksenia/SeeCiTe. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.