Biomedical subjects

Moshe Havilio

Publications and source records attributed to Moshe Havilio.

3 recordsLinked to original sources

PubMed2005

Evidence for abundant transcription of non-coding regions in the Saccharomyces cerevisiae genome.

BACKGROUND: Recent studies in a growing number of organisms have yielded accumulating evidence that a significant portion of the non-coding region in the genome is transcribed. We address this issue in the yeast Saccharomyces cerevisiae. RESULTS: Taking into account the absence of a significantly large yeast EST database, we use microarray expression data collected for genomic regions erroneously believed to be coding to study the expression pattern of non-coding regions in the Saccharomyces cerevisiae genome. We find that at least 164 out of 589 (28%) such regions are expressed under specific biological conditions. In particular, looking at the probes that are located opposing other known genes at the same genomic locus, we find that 88 out of 341 (26%) of these genes support antisense transcription. The expression patterns of these antisense genes are positively correlated. We validate these results using RT-PCR on a sample of 6 non-coding transcripts. CONCLUSION: 1. The yeast genome is transcribed on a scale larger than previously assumed. 2. Correlated transcription of antisense genes is abundant in the yeast genome. 3. Antisense genes in yeast are non-coding.

Computational Biology↗

PubMed2003

Intensity-based statistical scorer for tandem mass spectrometry.

We describe a new statistical scorer for tandem mass spectrometry. The scorer is based on the probability that fragments with given chemical properties create measured intensity levels in the experimental spectrum. The scorer's parameters are computed using a fully automated procedure. Benchmarking the new scorer on a large set of experimental spectra, we show that it performs significantly better than the widely used cross-correlation scoring algorithm of Eng et al. (Eng, J. K; McKormack, A. L.; Yates, J. R. J. Am. Soc. Mass Spectrom. 1994, 5, 976-989.).

Mass Spectrometry↗

PubMedDate not supplied

Signal deconvolution based expression-detection and background adjustment for microarray data.

Background adjustment is an essential stage in analyzing DNA microarrays. Discriminating expressed genes from unexpressed ones (expression detection), and estimating the expression levels of weakly expressed genes, critically depend on accurate treatment of the background intensity. Current methods for background adjustment either do not deal with nonspecific hybridization or strongly depend on the reliability of control probes. Existing model-based methods have limited accuracy. A new platform-independent background adjustment algorithm is presented. The algorithm relies on the deconvoluted experimental signal distribution for evaluating the expression probability and adjusting the background of each probe. Considering expression detection, it is shown, for two-channels cDNA arrays and for the Affymetrix GeneChip platform, that the algorithm performs at least as good or better than control-probes-based algorithms. For the Affymetrix GeneChip arrays, it is further shown that the algorithm outperforms the robust multiarray (RMA) expression measure in estimating genomewide expression levels.

Algorithms↗