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Biomedical subjects

Michael F Ochs

Publications and source records attributed to Michael F Ochs.

6 recordsLinked to original sources
PubMed2005

Bayesian decomposition analysis of bacterial phylogenomic profiles.

BACKGROUND: The past two decades have seen the appearance of new infectious diseases and the reemergence of old diseases previously thought to be under control. At the same time, the effectiveness of the existing antibacterials is rapidly decreasing due to the spread of multidrug-resistant pathogens. AIM: The aim of this study was to the identify candidate molecular targets (e.g. enzymes) within essential metabolic pathways specific to a significant subset of bacterial pathogens as the first step in the rational design of new antibacterial drugs. METHODS: We constructed a dataset of phylogenomic profiles (vectors that encode the similarity, measured by BLAST scores, of a gene across many species) for a series of 31 pathogenic bacteria of interest with 1073 genes taken from the reference organisms Escherichia coli and Mycobacterium tuberculosis. We applied Bayesian Decomposition, a matrix decomposition algorithm, to identify functional metabolic units comprising overlapping sets of genes in this dataset. RESULTS: Although no information on phylogeny was provided to the system, Bayesian Decomposition retrieved the known bacteria phylogenic relationships on the basis of the proteins necessary for survival. In addition, a set of genes required by all bacteria was identified, as well as components and enzymes specific to subsets of bacteria. CONCLUSION: The use of phylogenomic profiles and Bayesian Decomposition provide important insights for the design of new antibacterial therapeutics.

Algorithms↗
PubMed2004

ClutrFree: cluster tree visualization and interpretation.

UNLABELLED: ClutrFree facilitates the visualization and interpretation of clusters or patterns computed from microarray data through a graphical user interface that displays patterns, membership information of the genes and annotation statistics simultaneously. ClutrFree creates a tree linking the patterns based on similarity, permitting the navigation among patterns identified by different algorithms or by the same algorithm with different parameters, and aids the inferring of conclusions from a microarray experiment. AVAILABILITY: The ClutrFree Java source code and compiled bytecode are available as a package under the GNU General Public License at http://bioinformatics.fccc.edu

Algorithms↗
PubMed2004

FGDP: functional genomics data pipeline for automated, multiple microarray data analyses.

UNLABELLED: Gene expression microarrays and oligonucleotide GeneChips have provided biologists with a means of measuring, in a single experiment, the expression levels of entire genomes under a variety of conditions. As with any nascent field, there is no single accepted method for analyzing the new data types, with new methods appearing monthly. Investigators using the new technology must constantly seek access to the latest tools and explore their data in multiple ways. The functional genomics data pipeline provides an integrated, extendable analysis environment permitting multiple, simultaneous analyses to be automatically performed and provides a web server and interface for presenting results. AVAILABILITY: Source code and executables are available under the GNU public license at http://bioinformatics.fccc.edu/

Computing Methodologies↗
PubMed2004

Bayesian decomposition: analyzing microarray data within a biological context.

The detection and correct identification of cancer, especially at an early stage, are vitally important for patient survival and quality of life. Since signaling pathways play critical roles in cancer development and metastasis, methods that reliably assess the activity of these pathways are critical to understand cancer and the response to therapy. Bayesian Decomposition (BD) identifies signatures of expression that can be linked directly to signaling pathway activity, allowing the changes in mRNA levels to be used as downstream indicators of pathway activity. Here, we demonstrate this ability by identifying the downstream expression signal associated with the mating response in Saccharomyces cerevisiae and showing that this signal disappears in deletion mutants of genes critical to the MAPK signaling cascade used to trigger the response. We also show the use of BD in the context of supervised learning, by analyzing the Mus musculus tissue-specific data set provided by Project Normal. The algorithm correctly removes routine metabolic processes, allowing tissue-specific signatures of expression to be identified. Gene ontology is used to interpret these signatures. Since a number of modern therapeutics specifically target signaling proteins, it is important to be able to identify changes in signaling pathways in order to use microarray data to interpret cancer response. By removing routine metabolic signatures and linking specific signatures to signaling pathway activity, BD makes it possible to link changes in microarray results to signaling pathways.

Algorithms↗
PubMed2003

ASAP: automated sequence annotation pipeline for web-based updating of sequence information with a local dynamic database.

The automated sequence annotation pipeline (ASAP) is designed to ease routine investigation of new functional annotations on unknown sequences, such as expressed sequence tags (ESTs), through querying of web-accessible resources and maintenance of a local database. The system allows easy use of the output from one search as the input for a new search, as well as the filtering of results. The database is used to store formats and parameters and information for parsing data from web sites. The database permits easy updating of format information should a site modify the format of a query or of a returned web page.

Database Management Systems↗