Biomedical subjects

Jeremy Glasner

Publications and source records attributed to Jeremy Glasner.

3 recordsLinked to original sources

PubMed2006

Whole-genome plasticity among Mycobacterium avium subspecies: insights from comparative genomic hybridizations.

Infection with Mycobacterium avium subsp. paratuberculosis causes Johne's disease in cattle and is also implicated in cases of Crohn's disease in humans. Another closely related strain, M. avium subsp. avium, is a health problem for immunocompromised patients. To understand the molecular pathogenesis of M. avium subspecies, we analyzed the genome contents of isolates collected from humans and domesticated or wildlife animals. Comparative genomic hybridizations indicated distinct lineages for each subspecies where the closest genomic relatedness existed between M. avium subsp. paratuberculosis isolates collected from human and clinical cow samples. Genomic islands (n = 24) comprising 846 kb were present in the reference M. avium subsp. avium strain but absent from 95% of M. avium subsp. paratuberculosis isolates. Additional analysis identified a group of 18 M. avium subsp. paratuberculosis-associated islands comprising 240 kb that were absent from most of the M. avium subsp. avium isolates. Sequence analysis of DNA regions flanking the genomic islands identified three large inversions in addition to several small inversions that could play a role in regulation of gene expression. Analysis of genes encoded in the genomic islands reveals factors that are probably important for various mechanisms of virulence. Overall, M. avium subsp. avium isolates displayed a higher level of genomic diversity than M. avium subsp. paratuberculosis isolates. Among M. avium subsp. paratuberculosis isolates, those from wildlife animals displayed the highest level of genomic rearrangements that were not observed in other isolates. The presented findings will affect the future design of diagnostics and vaccines for Johne's and Crohn's diseases and provide a model for genomic analysis of closely related bacteria.

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PubMed2003

A Bayesian network approach to operon prediction.

MOTIVATION: In order to understand transcription regulation in a given prokaryotic genome, it is critical to identify operons, the fundamental units of transcription, in such species. While there are a growing number of organisms whose sequence and gene coordinates are known, by and large their operons are not known. RESULTS: We present a probabilistic approach to predicting operons using Bayesian networks. Our approach exploits diverse evidence sources such as sequence and expression data. We evaluate our approach on the Escherichia coli K-12 genome where our results indicate we are able to identify over 78% of its operons at a 10% false positive rate. Also, empirical evaluation using a reduced set of data sources suggests that our approach may have significant value for organisms that do not have as rich of evidence sources as E.coli. AVAILABILITY: Our E.coli K-12 operon predictions are available at http://www.biostat.wisc.edu/gene-regulation.

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PubMed2003

Predicting bacterial transcription units using sequence and expression data.

MOTIVATION: A key aspect of elucidating gene regulation in bacterial genomes is identifying the basic units of transcription. We present a method, based on probabilistic language models, that we apply to predict operons, promoters and terminators in the genome of Escherichia coli K-12. Our approach has two key properties: (i) it provides a coherent set of predictions for related regulatory elements of various types and (ii) it takes advantage of both DNA sequence and gene expression data, including expression measurements from inter-genic probes. RESULTS: Our experimental results show that we are able to predict operons and localize promoters and terminators with high accuracy. Moreover, our models that use both sequence and expression data are more accurate than those that use only one of these two data sources.

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