Software - Forschungsgruppe Bioinformatics/Research Group Computational Biology

BiLayout is a Java plugin for Cytoscape, a software platform for the analysis and visualization of molecular interaction networks. This plugin computes a bipartite network layout for 2 user-selected groups of nodes.
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BiQ Analyzer (http://biq-analyzer.bioinf .mpi-inf.mpg.de/) is a software tool for easy visualization and quality control of DNA methylation data. With more than a thousand downloads so far, BiQ Analyzer has become a standard tool for processing DNA methylation data from bisulfite sequencing. BiQ Analyzer has been selected by ABI to be part of the Applied Biosystems Software CommunityProgram.
Reference: Bock C, et al. (2005) BiQ Analyzer: visualization and quality control for DNA methylation data from bisulfite sequencing. Bioinformatics 21(21):4067-4068.
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Dive (http://dive.mpi-inf.mpg.de/) enables biologists to perform large-scale (epi-)genomic data analysis. It provides an easy interface, with commands and data provided by DeepBlue.

DomainGraph is the successor tool of DomainNetworkBuilder and works as Java plugin for Cytoscape, a free open-source software platform for visualization and analysis of biomolecular networks. This plugin decomposes protein networks into domain-domain interactions and generates a new network of interacting domains. It also allows the integration of exon expression data measured using Affymetrix GeneChip microarrays, which supports the analysis of alternative splicing events and the characterization of their effects on protein and domain interaction networks.
Reference: Emig, D., Salomonis, N., Baumbach, J., Lengauer, T., Conklin, BR., Albrecht, M. (2010) AltAnalyze and DomainGraph: analyzing and visualizing exon expression data. Nucleic Acids Res. (Abstract)
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DomainNetworkBuilder works as Java plugin for Cytoscape, a free open-source software platform for visualization and analysis of biomolecular networks. This plugin decomposes protein networks into domain-domain interactions and generates a new network of interacting domains.
Reference: Albrecht, M.; Huthmacher, C.; Tosatto, S. C.; Lengauer, T., Decomposing protein networks into domain-domain interactions. Bioinformatics 2005, 21 Suppl 2, ii220-ii221.
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The EpiExplorer integrates multiple epigenetic and genetic annotations and
makes them explorable via an interactive interface.
Reference: Halachev, K., et al., EpiExplorer: live exploration and global analysis of large epigenomic datasets. Genome Biol, 2012. 13(10): p. R96. 

EpiGRAPH (http://epigraph.mpi-inf.mpg.de/) enables biologists to analyze genome and epigenome datasets with powerful statistical and machine learning methods. In a typical workflow, the user uploads a set of genomic regions of interest (e.g. experimentally mapped enhancers, hotspots of epigenetic regulation or sites exhibiting disease-specific alterations), and EpiGRAPH searches a large database of (epi-) genomic attributes for significant overlap and correlation with the regions in the input dataset. Furthermore, EpiGRAPH can predict the status of genomic regions that were not included in the input dataset.
Reference: Bock C, Halachev K, Büch J, & Lengauer T (2009) EpiGRAPH: User-friendly software for statistical analysis and prediction of (epi-) genomic data. Genome Biol 10(2):R14
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The EuResist project (IST-2004-027173) aims at developing an integrated European system for computer-based clinical management of antiretroviral drug resistance. The resulting EuResist prediction engine is a data-driven system, which predicts the response to an antiretroviral combination drug therapy for HIV infected patients. The system comprises three prediction engines that are located at different sites: Italy (informa S.r.l.), Israel (IBM Israel), and Germany (Max Planck Institute for Informatics). The three services are connected via a SOAP client-server acrchitecture.
Reference: Rosen-Zvi, M.; Altmann, A.; Prosperi, M.; Aharoni, E.; Neuvirth, H.; Sonnerborg, A.; Schülter, E.; Struck, D.; Peres, Y.; Incardona, F.; Kaiser, R.; Zazzi, M.; Lengauer, T., Selecting anti-HIV therapies based on a variety of genomic and clinical factors. Bioinformatics 2008, 24 (13), i399-406.
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Geno2pheno is a server that is freely accessible and that supports patient-specific analysis of viral resistance to drugs and ranking of combination drug therapies with respect to their effectiveness. The pathogens covered by the server are HIV, HBV and HCV. A dozen analyses are offered. All analyses are based on the viral genotype. For some analyses additional information on clinical parameters of the patient and/or on patient history can be added. Predictions are based partly on rule sets hand-crafted by medical experts, and partly on models trained on resistance data with statistical learning methods.
Reference: Lengauer, T. and T. Sing, Bioinformatics-assisted anti-HIV therapy. Nat Rev Microbiol, 2006. 4(10): p. 790-7.
Lengauer, T., et al., Bioinformatics prediction of HIV coreceptor usage. Nat Biotechnol, 2007. 25(12): p. 1407-10. 

Geno2Pheno[hev] is a web service for genotyping and resistance testing of HEV nucleotide sequences.
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Geno2Pheno[hev] is a web service for genotyping and resistance testing of HEV nucleotide sequences.
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IRECS can predict multiple conformations for all side chains of a target protein. Side-chain conformations are selected according to the flexibility of the respective side chain. IRECS is also able to mutate single side chains or read in alignments to create a homology model of the target protein on a template backbone.
Reference: Hartmann, C.; Antes, I.; Lengauer, T., Docking and scoring with alternative side-chain conformations. Proteins 2009, 74 (3), 712-26
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MethMarker (http://methmarker.mpi-inf.mpg.de/) facilitates the design of DNA methylation assays for COBRA, bisulfite SNuPE, bisulfite pyrosequencing, MethyLight and MSP. It also implements a systematic workflow for design, optimization and (computational) validation of DNA methylation biomarkers. This workflow starts from a preselected differentially methylated region (DMR) and results in an optimized DNA methylation assay that is ready to be tested in a large-scale clinical trial.
Reference: Schüffler, P., et al., MethMarker: User-friendly design and optimization of gene-specific DNA methylation assays. Genome Biol, 2009. 10(10): p. R105.
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NetworkAnalyzer works as Java plugin for Cytoscape, a free open-source software platform for visualization and analysis of biomolecular networks. This plugin computes parameters describing the network topology and displays their distributions in diagrams.
Reference: Assenov Y, Ramirez F, Schelhorn SE, Lengauer T, & Albrecht M: Computing topological parameters of biological networks. Bioinformatics 2008 24(2):282-284.
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openPrimeR is an R package providing methods for designing, evaluating, and comparing primer sets for multiplex polymerase chain reaction (PCR). The package provides a primer design function that generates novel primer setes by solving a set cover problem such that the number of covered template sequences is maximized with the smallest possible set of primers. Moreover, existing primer sets can be evaluated according to their coverage and their fulfillment of constraints on the PCR-relevant physicochemical properties. For PCR tasks for which multiple possible primer sets exist, openPrimeR can facilitate the selection of the most suitable set by performing comparative analyses. The R package includes a Shiny application that provides a comprehensive and intuitive user interface for the core functionalites of the package.
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Recco analyzes alignments of sequences that evolved subject to recombination and mutation. The analysis provides evidence as to whether a dataset contains recombination, which sequence is a recombinant and where the recombination breakpoints are. The analysis is based on explaining one sequence with all other sequences in the alignment using mutation and recombination. A parametric analysis of the parameter alpha, which weights recombination cost against mutation cost, yields additional information as to which sequence might be recombinant.
Reference: Maydt, J.; Lengauer, T., Recco: recombination analysis using cost optimization. Bioinformatics 2006, 22 (9), 1064-71.
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Recco analyzes alignments of sequences that evolved subject to recombination and mutation. The analysis provides evidence as to whether a dataset contains recombination, which sequence is a recombinant and where the recombination breakpoints are. The analysis is based on explaining one sequence with all other sequences in the alignment using mutation and recombination. A parametric analysis of the parameter alpha, which weights recombination cost against mutation cost, yields additional information as to which sequence might be recombinant.
Reference: Maydt, J.; Lengauer, T., Recco: recombination analysis using cost optimization. Bioinformatics 2006, 22 (9), 1064-71.
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RINalyzer is a Java plugin for Cytoscape, a free open-source software platform for visualization and analysis of biomolecular networks. This plugin allows the simultaneous visualization and interactive analysis of residue interaction networks (RINs) together with the corresponding 3D protein structures displayed in UCSF Chimera. It also provides a comprehensive set of topological centrality measures to gain additional insights into the structural and functional role of interacting residues.
Reference: Doncheva, N.T., Klein, K., Domingues, F.S., Albrecht, M. (2011): Analyzing and visualizing residue networks of protein structures. Trends in Biochemical Sciences, 36(4): 179-182.
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RnBeads is an R package for comprehensive analysis of DNA methylation data obtained with any experimental protocol that provides single-CpG resolution, including Infinium 450K microarray and bisulfite sequencing protocols, but also MeDIP-seq and MBD-seq once the data have been preprocessed with DNA methylation inference software. RnBeads implements an analysis workflow that is significantly more comprehensive than those of other existing tools. It documents its results in a highly annotated and readable hypertext report, and it scales to the large sample sizes that are becoming the norm for DNA methylation analysis in human cohorts.

ROC graphs, sensitivity/specificity curves, lift charts, and precision/recall plots are popular examples of trade-off visualizations for specific pairs of performance measures. ROCR is a flexible tool for creating cutoff-parametrized 2D performance curves by freely combining two from over 25 performance measures (new performance measures can be added using a standard interface). Curves from different cross-validation or bootstrapping runs can be averaged by different methods, and standard deviations, standard errors or box plots can be used to visualize the variability across the runs. The parametrization can be visualized by printing cutoff values at the corresponding curve positions, or by coloring the curve according to cutoff. All components of a performance plot can be quickly adjusted using a flexible parameter dispatching mechanism. Despite its flexibility, ROCR is easy to use, with only three commands and reasonable default values for all optional parameters.
Reference: Sing, T., et al., ROCR: visualizing classifier performance in R. Bioinformatics, 2005. 21(20): p. 3940-1.
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topGO (topology-based Gene Ontology scoring) is a software package for calculating the significance of biological terms from gene expression data. It implements various standard and advanced new algorithms for determining the relevance of Gene Ontology groups from microarrays. A specific feature of the advanced algorithms is the exploitation of the hierarchical graph structure of the GO annotation for coping with the large number of GO groups. Often, related biological terms are scored with a similar statistical significance. Dependencies between GO terms can be de-correlated by accounting for the neighborhood of a GO node when calculating its significance. The new algorithms better detect significant GO terms from gene expression data.
Reference: Alexa, A.; Rahnenführer, J.; Lengauer, T., Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics 2006, 22 (13), 1600-7.
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