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Wyeth W Wasserman

Publications and source records attributed to Wyeth W Wasserman.

24 records · Page 2Linked to original sources
PubMed2003

Integrated analysis of yeast regulatory sequences for biologically linked clusters of genes.

Dramatic progress in deciphering the regulatory controls in Saccharomyces cerevisiae has been enabled by the fusion of high-throughput genomics technologies with advanced sequence analysis algorithms. Sets of genes likely to function together and with similar expression profiles have been identified in diverse studies. By fusing an advanced pattern recognition algorithm for identification of transcription factor binding sites with a new method for the quantitative comparison of binding properties of transcription factors, we provide an integrated means to move from expression data to biological insights. The Yeast Regulatory Sequence Analysis system, YRSA, combines standard functions with a novel pattern characterization procedure in an intuitive interface designed for use by a broad range of scientists. The features of the system include automated retrieval of user-defined promoter sequences, binding site discovery by pattern recognition, graphical displays of the observed pattern and positions of similar sequences in the specified genes, and comparison of the new pattern against a collection of binding patterns for characterized transcription factors. The comprehensive YRSA system was used to study the regulatory mechanisms of yeast regulons. Analysis of the regulatory controls of a battery of genes induced by DNA damaging agents supports a putative mediating role for the cell-cycle checkpoint regulatory element MCB. YRSA is available at http://yrsa.cgb.ki.se. [YRSA: ancient Scandinavian name meaning old she-bear (Latin Ursus arctos = brown bear/grizzly).]

Algorithms↗
PubMed2003

Identification of conserved regulatory elements by comparative genome analysis.

BACKGROUND: For genes that have been successfully delineated within the human genome sequence, most regulatory sequences remain to be elucidated. The annotation and interpretation process requires additional data resources and significant improvements in computational methods for the detection of regulatory regions. One approach of growing popularity is based on the preferential conservation of functional sequences over the course of evolution by selective pressure, termed 'phylogenetic footprinting'. Mutations are more likely to be disruptive if they appear in functional sites, resulting in a measurable difference in evolution rates between functional and non-functional genomic segments. RESULTS: We have devised a flexible suite of methods for the identification and visualization of conserved transcription-factor-binding sites. The system reports those putative transcription-factor-binding sites that are both situated in conserved regions and located as pairs of sites in equivalent positions in alignments between two orthologous sequences. An underlying collection of metazoan transcription-factor-binding profiles was assembled to facilitate the study. This approach results in a significant improvement in the detection of transcription-factor-binding sites because of an increased signal-to-noise ratio, as demonstrated with two sets of promoter sequences. The method is implemented as a graphical web application, ConSite, which is at the disposal of the scientific community at http://www.phylofoot.org/. CONCLUSIONS: Phylogenetic footprinting dramatically improves the predictive selectivity of bioinformatic approaches to the analysis of promoter sequences. ConSite delivers unparalleled performance using a novel database of high-quality binding models for metazoan transcription factors. With a dynamic interface, this bioinformatics tool provides broad access to promoter analysis with phylogenetic footprinting.

Algorithms↗
PubMed2003

In silico identification of metazoan transcriptional regulatory regions.

Transcriptional regulation remains one of the most intriguing and challenging subjects in biomedical research. The catalysis of transcription is a clear example of multiple proteins interacting to orchestrate a biological process, offering a starting point for the study of biological systems. Transcriptional regulation is viewed as one of the principal mechanisms governing the spatial and temporal distribution of gene expression, thus the field of transcriptional regulation provides a natural stage for quantitative studies of multiple gene systems. Building on the body of focused experimental studies and new genomics-driven data, computational biologists are making significant strides in accelerating our understanding of the transcriptional regulatory process in metazoan cells. Recent advances in the computational analysis of the interplay between factors have been fueled by well-defined computational methods for the modeling of the binding of individual transcription factors. We present here an overview of advances in the analysis of regulatory systems and the fundamental methods that underlie the recent developments.

Animals↗
PubMed2003

GeneLynx mouse: integrated portal to the mouse genome.

GeneLynx Mouse is a meta-database providing an extensive collection of hyperlinks to mouse gene-specific information in diverse databases available via the Internet. The GeneLynx project is based on the simple notion that given any gene-specific identifier (e.g., accession number, gene name, text, or sequence), scientists should be able to access a single location that provides a set of links to all the publicly available information pertinent to the specified gene. The recent climax in the mouse genome and RIKEN cDNA sequencing projects provided the data necessary for the development of a gene-centric mouse information portal based on the GeneLynx ideals. Clusters of RIKEN cDNA sequences were used to define the initial set of mouse genes. Like its human counterpart, GeneLynx Mouse is designed as an extensible relational database with an intuitive and user-friendly Web interface. Data is automatically extracted from diverse resources, using appropriate approaches to maximize the coverage. To promote cross-database interoperability, an indexing utility is provided to facilitate the establishment of hyperlinks in external databases. As a result of the integration of the human and mouse systems, GeneLynx now serves as a powerful comparative genomics data mining resource. GeneLynx Mouse can be freely accessed at http://mouse.genelynx.org.

Animals↗
PubMed2002

TFBS: Computational framework for transcription factor binding site analysis.

MOTIVATION: TFBS is a set of integrated, object-oriented Perl modules for transcription factor binding site detection and analysis. It implements objects representing specificity profile matrices, binding sites and sets thereof, pattern generators, and pattern database interfaces. The modules are interoperable with the BioPerl open source system. AVAILABILITY AND SUPPLEMENTARY INFORMATION: The module package with documentation and example scripts are available at http://forkhead.cgb.ki.se/TFBS/

Binding Sites↗