Biomedical subjects

Roni Rosenfeld

Publications and source records attributed to Roni Rosenfeld.

3 recordsLinked to original sources

PubMed2004

Deconvolving cell cycle expression data with complementary information.

MOTIVATION: In the study of many systems, cells are first synchronized so that a large population of cells exhibit similar behavior. While synchronization can usually be achieved for a short duration, after a while cells begin to lose their synchronization. Synchronization loss is a continuous process and so the observed value in a population of cells for a gene at time t is actually a convolution of its values in an interval around t. Deconvolving the observed values from a mixed population will allow us to obtain better models for these systems and to accurately detect the genes that participate in these systems. RESULTS: We present an algorithm which combines budding index and gene expression data to deconvolve expression profiles. Using the budding index data we first fit a synchronization loss model for the cell cycle system. Our deconvolution algorithm uses this loss model and can also use information from co-expressed genes, making it more robust against noise and missing values. Using expression and budding data for yeast we show that our algorithm is able to reconstruct a more accurate representation when compared with the observed values. In addition, using the deconvolved profiles we are able to correctly identify 15% more cycling genes when compared to a set identified using the observed values. AVAILABILITY: Matlab implementation can be downloaded from the supporting website http://www.cs.cmu.edu/~zivbj/decon/decon.html

Biological Clocks↗

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↗

PubMedDate not supplied

Finding motifs with insufficient number of strong binding sites.

A molecule called transcription factor usually binds to a set of promoter sequences of coexpressed genes. As a result, these promoter sequences contain some short substrings, or binding sites, with similar patterns. The motif discovering problem is to find these similar patterns and motifs in a set of sequences. Most existing algorithms find the motifs based on strong-signal sequences only (i.e., those containing binding sites very similar to the motif). In this paper, we use a probability matrix to represent a motif to calculate the minimum total number of binding sites required to be in the input dataset in order to confirm that the discovered motifs are not artifacts. Next, we introduce a more general and realistic energy-based model, which considers all sequences with varying degrees of binding strength to the transcription factors (as measured experimentally). By treating sequences with varying degrees of binding strength, we develop a heuristic algorithm called EBMF (Energy-Based Motif Finding Algorithm) to find the motif, which can handle sequences ranging from those that contain more than one binding site to those that contain none. EBMF can find motifs for datasets that do not even have the required minimum number of binding sites as previously derived. EBMF compares favorably with common motif-finding programs AlignACE and MEME. In particular, for some simulated and real datasets, EBMF finds the motif when both AlignACE and MEME fail to do so.

Algorithms↗