The Significant Subgraph Miner (SSM) is an algorithm to find subgraphs in a single graph that are significantly associated with a set of vertices. It can handle a large variety of graph dataset types and vertex selections. Further it efficiently progresses through the search space so that most problems should be computable. The accompanying implementation is provided for free for research purposes. Some bugs may be present within the software and no guarantees are given! We would appreciate any comments, bug descriptions, suggestions or succes stories regarding the tool. This implementation should work on most regular size biological networks with any number of selected vertices and several node labels as demonstrated in the accompanying publication. The only requirement to run this version is a standard Perl 5+ installation and it has been extensively tested on Linux and Mac OSX systems. The example dataset is included in the zip file for illustrative and testing purposes.
- Significant Subgraph Miner v1.1 + Example dataset
- Significant Subgraph Miner v1.0 (BioKDD) + Example dataset
- Discovery of Significantly Enriched Subgraphs Associated with Selected Vertices in a Single Graph, BIOKDD’15 August 10, 2015 (link)
speaq stands for "spectral alignment and quantitation" and constitutes a novel suite of informatics tools for the quantitative analysis of NMR metabolomic profile data. The core of the processing cascade is a novel peak alignment algorithm, called hierarchical Cluster-based Peak Alignment (CluPA). The algorithm aligns a target spectrum to the reference spectrum in a top-down fashion by building a hierarchical cluster tree from peak lists of reference and target spectra and then dividing the spectra into smaller segments based on the most distant clusters of the tree. To reduce the computational time to estimate the spectral misalignment, the method makes use of Fast Fourier Transformation (FFT) cross-correlation. Since the method returns a high-quality alignment, we can propose a simple methodology to study the variability of the NMR spectra.
- Vu TN, Valkenborg D, Smets K, Verwaest KA, Dommisse R, Lemière F, Verschoren A, Goethals B, Laukens K. (2011) An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data. BMC Bioinformatics. 2011 Oct 20;12:405. (link)
CRPhos was originally developed as a tool to predict kinase-specific phosphorylation sites from a protein sequence. To this end, it employs a powerful algorithm based on a Conditional Random Fields model, which offers several advantages over alternative models. Due to its generic nature, the method was later extended to other post-translational modifications, such as SUMOylation, which it can predict with a significantly improved performance. Development of CRPhos is still ongoing.
- Dang, T.H., Van Leemput, K., Verschoren, A. & Laukens, K. Prediction of kinase-specific phosphorylation sites using conditional random fields. Bioinformatics 24, 2857-2864 (2008). (link)
VariantDB is a versatile annotation and filtering database with a web-based frontend, which automatically annotates variants with allele frequencies, functional impact, pathogenicity predictions and pathway information. VariantDB allows filtering under different inheritance models, including dominant, recessive or de novo inheritance. Furthermore, all available annotations can be set as additional filtering criteria. VariantDB is therefore a user-friendly and powerful tool to help in the interpretation of NGS data.
- Vandeweyer G., Van Laer L., Loeys B., Van den Bulcke T., Kooy R.F. (2014) VariantDB: a flexible annotation and filtering portal for next generation sequencing data. Genome Medicine Oct 2;6(10):74. (pubmed)
pBRIT is a gene prioritization tool based on Bayesian Ridge Regression and Information-Theoretic model.