Frank Schacherer Homepage
home technology bibliography

Publications

Journal articles

AuthorTitleYearJournalVolume,Pages
Thomas Heinemeyer and Xin Chen and Holger Karas and Alexander E. Kel and Olga V. Kel and Ines Liebich and Thorsten Meinhardt and Ingmar Reuter and Frank Schacherer and Edgar Wingender Expanding the TRANSFAC database towards an expert system of regulatory molecular mechanisms 1999 Nucleic Acids Res. 27(1), 318-322
TRANSFAC is a database on transcription factors, their genomic binding sites and DNA-binding profiles. In addition to being updated and extended by new features, it has been complemented now by a series of additional database modules. Among them, modules which provide data about signal transduction pathways (TRANSPATH) or about cell types/organs/developmental stages (CYTOMER) are available as well as an updated version of the previously described COMPEL database. The databases are available on the WWW at http://transfac.gbf.de/
Edgar Wingender and Xin Chen and Reinhard Hehl and Holger Karas and Ines Liebich and Volker Matys and Thorsten Meinhardt and Manuela Prüss and Ingmar Reuter and Frank Schacherer TRANSFAC: an integrated system for gene expression regulation 2000 Nucleic Acids Res. 28(1), 316-319
TRANSFAC is a database on transcription factors, their genomic binding sites and DNA-binding profiles (http://transfac.gbf.de/TRANSFAC/). Its content has been enhanced, in particular by information about training sequences used for the construction of nucleotide matrices as well as by data on plant sites and factors. Moreover, TRANSFAC has been extended by two new modules: PathoDB provides data on pathologically relevant mutations in regulatory regions and transcription factor genes, whereas S/MARt DB compiles features of scaffold/matrix attached regions (S/MARs) and the proteins binding to them. Additionally, the databases TRANSPATH, about signal transduction, and CYTOMER, about organs and cell types, have been extended and are increasingly integrated with the TRANSFAC data sources.
Frank Schacherer and Claudia Choi and Ulrike Götze and Mathias Krull and Susanne Pistor and Edgar Wingender The TRANSPATH signal transduction database: a knowledge base on signal transduction networks 2001 Bioinformatics 17(11), 1053-1057
TRANSPATH is an information system on gene-regulatory pathways, and an extension module to the TRANSFAC database system (Wingender et al., Nucleic Acids Res., 28, 316-319, 2000). It focuses on pathways involved in the regulation of transcription factors in different species, mainly human, mouse and rat. Elements of the relevant signal transduction pathways like complexes, signaling molecules, and their states are stored together with information about their interaction in an object-oriented database. The database interface provides clickable maps and automatically generated pathway cascades as additional ways to explore the data. All information is validated with references to the original publications. Also, references to other databases are provided (TRANSFAC, SWISS-PROT, EMBL, PubMed and others). AVAILABILITY: The database is available over (http://transpath.gbf.de) for interactive perusal. As an exchange format for the data, eXtensible Markup Language (XML) flatfiles and a Document Type Definition (DTD) are provided.
Edgar Wingender and Xin Chen and Ellen Fricke and Robert Geffers and Reinhard Hehl and Ines Liebich and Mathias Krull and Volker Matys and Holger Michael and Richard Ohnhäuser and Manuela Prüss and Frank Schacherer and Susanne Thiele and Sandra Urbach The TRANSFAC system on gene expression regulation 2001 Nucleic Acids Res. 29(1), 281-283
The TRANSFAC database on transcription factors and their DNA-bindingsites and profiles (http://www.gene-regulation.de/) has been quantitatively extended and supplemented by a number of modules. These modules give information about pathologically relevant mutations in regulatory regions and transcription factor genes (PathoDB), scaffold/matrix attached regions (S/MARt DB), signal transduction (TRANSPATH) and gene expression sources (CYTOMER). Altogether, these distinct database modules constitute the TRANSFAC system. They are accompanied by a number of program routines for identifying potential transcription factor binding sites or for localizing individual components in the regulatory network of a cell.
F. Schacherer and A. Bosio Gene expression profiling for diagnostics 2003 Laborwelt  (1/2003)32-34
Edgar Wingender and Jennifer Hogan and Frank Schacherer and Anatolij P Potapov and Olga Kel-Margoulis Integrating pathway data for systems pathology 2007 In Silico Biology 7(2 Suppl), 17-25
The HumanPSD database on the complete proteomes of human, mouse and rat has been integrated with the databases TRANSFAC on gene regulation and TRANSPATH on signal transduction to provide a comprehensive systems biological platform for these organisms. As a next step, integration with PathoDB and PathoSign on pathologically relevant mutations is planned together with an extension beyond the limits of the individual cell, towards intercellular networks, by integrating the database EndoNet on hormonal networks as well. The overall aim is to come up with a platform that is suitable to provide knowledge for systems pathology, i. e. a system-wide modeling of pathological states and their development.
Holger Michael and Jennifer Hogan and Alexander Kel and Olga Kel-Margoulis and Frank Schacherer and Nico Voss and Edgar Wingender Building a knowledge base for systems pathology 2008 Brief Bioinform 9(6), 518-31
Translating the exponentially growing amount of omics data into knowledge usable for a personalized medicine approach poses a formidable challenge. In this article-taking diabetes as a use case-we present strategies for developing data repositories into computer-accessible knowledge sources that can be used for a systemic view on the molecular causes of diseases, thus laying the foundation for systems pathology.
Emek Demir et. al. The BioPAX community standard for pathway data sharing 2010 Nature Biotechnology 28(9), 935-42
Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.

PhD Thesis

AuthorTitleYearSchool
Frank Schacherer An object-oriented database for the compilation of signal transduction pathways 2001 Technical University of Braunschweig
Transpath is an information system on signal-transduction networks. It focuses on pathways involved in the regulation of transcription factors. Molecules and reactions are stored in an object-oriented database, together with information about their location, quality, family relationships and signaling motifs. Also stored are links to other databases and references to the original literature. Transpath differentiates between the states of a signal molecule and can describe the reaction mechanisms. It used to be available over the web through a Servlet-based interface. Nowadays it is distributed by Biobase Gmbh (www.biobase-international.com). Pathway query mechanisms and several kinds of display are provided for the database in addition to text-based queries and information on single entries.

Conference presentations, posters and demos

AuthorTitleYearConferencePages
Frank Schacherer and Edgar Wingender The Transpath Signal Transduction Database 1999 Conference of the Signal Transduiction Society poster
Frank Schacherer and Edgar Wingender The Transpath Signal Transduction Database 1999 German Conference on Bioinformatics poster
Frank Schacherer and Edgar Wingender The TRANSPATH Signal Transduction Database: A knowledge base on signal transduction networks 2000 Intelligent Systems in Molecular Biology poster
The huge and ever more rapidly growing amount of signal transduction data demands for a database that stores and organizes this knowledge, providing simple and fast access to the information. The complexity created by the cross-talk between pathways makes it virtually impossible to infer by hand all the consequences that follow after one modifies one part of the network. To this end, computer-aided simulation will have to be used. It can only be successful on the basis of a comprehensive and detailed dataset. TRANSPATH is an information system on gene-regulatory pathways, and an extension module to the TRANSFAC database [Wingender et al. 2000]. It focuses on pathways involved in the regulation of transcription factors in different species, mainly human, mouse and rat. Elements of the relevant signal transduction pathways are stored together with information about their interaction and references in an object-oriented database. All information is validated with references to the original publications. Also, references to other databases are provided (TRANSFAC, Swissprot, EMBL, PubMed and others). The database is available over WWW (http://transpath.gbf.de). There are also clickable graphic maps of selected pathways. This site also provides an interface to the CSNDB database on signal transduction [Takai-Igarashi and Kaminuma, 1998]. Transpath provides a knowledge base which goes beyond the approach of traditional gene or sequence databases by focusing on the interactions between the stored data items. By building up the signaling network from single interactions instead of using predefined pathways, it becomes possible to explore the pathways in an unbiased way.
Frank Schacherer and Claudia Choi and Ulrike Götze and Mathias Krull and Edgar Wingender The TRANSPATH Signal Transduction Database: A knowledge base on signal transduction networks 2000 The Second International Conference on Bioinformatics of Genome Regulation and Structure (BGRS'2000) poster
Anatolij Potapov and Klaus Seidl and Maik Christensen and Volker Drewes and Frank Schacherer and Edgar Wingender The integrated TRANSFAC system as a basis for modeling of gene regulation mechanisms: protein target finding 2001 Bioinformatics 2001 34
Frank Schacherer A review of biological network visualisation 2001 Pacific Symposium on Bioinformatics (PSB01) presentation
Frank Schacherer and Hartmut Scheel and Kay Hofmann Searching for biologically meaningful groupings in microarray clustering data 2002 European Conference on Computational Biology 2002 poster
A usual first step in the interpretation of large amount of microarray data is the clustering of the genes by their expression properties. Several unsupervised clustering methods are commonly in use, including K-means clustering, hierarchical clustering, self-organizing maps and others. Of particular importance are hierarchical clustering schemes, which arrange the data in a tree-like structure, where genes with similar expression patterns occupy neighboring 'leaves' of the tree. The major advantage of this method is that it allows an analysis of the data at different levels of 'granularity': it is possible to look at various 'family sizes' defined by various degrees of expression similarity. The algorithms used for hierarchical clustering are largely the same as used for distance-based phylogenetic reconstruction from sequence data, but restricted to those methods that are fast enough to deal with large numbers of nodes. Provisions for testing the statistical significance of individual 'clades', e.g. by bootstrapping, are commonplace in phylogenetic reconstruction but are not normally used in expression clustering. Thus, a microarray experiments with N genes results in N-1 clusters. It is obvious that not of all these clusters can be scrutinized manually for their biological relevance. We present a method for the automatic re-annotation of hierarchical clustering data by looking for statistically significant 'enrichment' of biological properties in co-expression clusters. The nature of the biological properties used for the re-annotation can be heterogeneous: all classification schemes of genes or gene products are possible. Interesting applications include data from biological pathways, multi-protein complexes, subcellular localization, or pre-established co-expression data coming from other experiments. The significance of the cluster-wide enrichment of the biological properties is assessed by methods of inferential statistics, e.g. by Fisher's exact test. We will demonstrate in a number of example applications how this approach can be used to gain biological insight into co-expression patterns.
Frank Schacherer and Nico Voss and Alexander Kel From interactions to pathways - getting the bigger picture 2005 7th BioPathways Meeting presentation
Pathways are built from single interactions between proteins, and group these in ways useful for analysis and presentation of biological networks. They can also be composed from subpathways, or chains of reactions. Pathways represent expert knowledge about the context in which interactions effect biological systems. We present several uses of pathway information: When laying out biological networks, they enable hierarchical decomposition of these networks and allow the user to navigate the network and understand its overall structure. They also can be employed to simplify the presentation of complex parts of the network by highlighting key players. When examining expression data, pathway knowledge can be seen as a functional classification for statistical identification of biological processes. Networks also enable us to search key regulators or targets for a group of coregulated or overexpressed genes. We show how pathways can be used to avoid false positive crosstalks in these network searches
Katya Mantrova and Sarah Ratsch and Robin Johnson and Fern Barkalow and Alison Gagnon and Amy Hodge and Kelley Lennon-Hopkins and Gloria Kelly and Frank Schacherer Integrated Databases, Proteome, TRANSFAC(R), and TRANSPATH(R), are the Comprehensive Resource for Drug Discovery 2005 Drug Discovery Today poster
Frank Schacherer Pathway Analysis with Curated Protein Databases 2005 Intelligent Systems for Molecular Biology demo
Frank Schacherer Promoter analysis in signaling pathways 2006 MidSouth Computational biology and BIOinformatics Society (MCBIOS) presentation
B. Braun and C. Elfe and R. Nambudiry and S. Thomas and G. Kumar and M. Krull and J. Hogan and F. Schacherer The BIOBASE Knowledge Library - A unified resource for biological interpretation and discovery 2006 Bio IT World Conference poster
The BIOBASE Knowledge Library delivers comprehensive functional information for whole genomes and proteomes, providing breadth in coverage. It extends this base by offering detailed modules on pathways, diseases, and gene regulation, providing depth where it matters. Data is manually assembled from scientific literature and verified by domain experts for all genes that have been experimentally studied, and supplemented by predicted function for those that have not. The user thus can easily get a functional characterization for any gene or protein, and access detailed regulatory knowledge where it is known. The BIOBASE Knowledge Library was created by integrating the resources of several formerly independent databases, among them the Proteome family of databases (HumanPSD, YPD, WormPD and others), the TRANSFAC(R) family of databases which specializes in transcription regulation, and the TRANSPATH(R) database on signal transduction pathways, complexes and phosphorylation events. Information in the database is collected using open standards like Gene Ontology with extensive cross-links to public resources to enhance interoperability. All data are available under a common, unified interface that allows convenient querying and browsing. It also contains analytical tools to leverage the various kinds of data, from microarray data interpretation to transcription factor binding site prediction and pathway analysis. The data are also available in relational format, to facilitate integration into in-house resources and support proprietary bioinformatics research
Frank Schacherer and Philip Stegmayer Analysis of HER2/neu as a Diagnostic/Biomarker Using ExPlain(TM) to Examine Signaling Pathways and Transcription Factors 2008 Molecular Medicine Tri Conference presentation