Biomedical subjects

Rose Hoberman

Publications and source records attributed to Rose Hoberman.

3 recordsLinked to original sources

PubMed2006

Diagnosing duplications--can it be done?

New genes arise through duplication and modification of DNA sequences on a range of scales: single gene duplication, duplication of large chromosomal fragments and whole-genome duplication. Each duplication mechanism has specific characteristics that influence the fate of the resulting duplicates, such as the size of the duplicated fragment, the potential for dosage imbalance, the preservation or disruption of regulatory control and genomic context. The ability to diagnose or identify the mechanism that produced a pair of paralogs has the potential to increase our ability to reconstruct evolutionary history, to understand the processes that govern genome evolution and to make functional predictions based on paralogy. The recent availability of large amounts of whole-genome sequence, often from several closely related species, has stimulated a wealth of new computational methods to diagnose gene duplications.

Animals↗

PubMed2005

The statistical analysis of spatially clustered genes under the maximum gap criterion.

Statistical validation of gene clusters is imperative for many important applications in comparative genomics which depend on the identification of genomic regions that are historically and/or functionally related. We develop the first rigorous statistical treatment of max-gap clusters, a cluster definition frequently used in empirical studies. We present exact expressions for the probability of observing an individual cluster of a set of marked genes in one genome, as well as upper and lower bounds on the probability of observing a cluster of h homologs in a pairwise whole-genome comparison. We demonstrate the utility of our approach by applying it to a whole-genome comparison of E. coli and B. subtilis. Code for statistical tests is available at.

Bacillus subtilis↗

PubMed2004

Inferring property selection pressure from positional residue conservation.

In this study, we attempt to understand and explain positional selection pressure in terms of underlying physical and chemical properties. We propose a set of constraining assumptions about how these pressures behave, then describe a procedure for analysing and explaining the distribution of residues at a particular position in a multiple sequence alignment. In contrast to previous approaches, our model takes into account both amino acid frequencies and a large number of physical-chemical properties. By analysing each property separately, it is possible to identify positions where distinct conservation patterns are present. In addition, the model can easily incorporate sequence weights that adjust for bias in the sample sequences. Finally, a test of statistical significance is provided for our conservation measure. The applicability of this method is demonstrated on two HIV-1 proteins: Nef and Env. The tools, data and results presented in this article are available at http://flan.blm.cs.cmu.edu.

Algorithms↗