Strengths and weaknesses of EST-based prediction of tissue-specific alternative splicing

BACKGROUND: Alternative splicing contributes significantly to the complexity of the human transcriptome and proteome. Computational prediction of alternative splice isoforms are usually based on EST sequences that also allow to approximate the expression pattern of the related transcripts. However, the limited number of tissues represented in the EST data as well as the different cDNA construction protocols may influence the predictive capacity of ESTs to unravel tissue-specifically expressed transcripts. METHODS: We predict tissue and tumor specific splice isoforms based on the genomic mapping (SpliceNest) of the EST consensus sequences and library annotation provided in the GeneNest database. We further ascertain the potentially rare tissue specific transcripts as the ones represented only by ESTs derived from normalized libraries. A subset of the predicted tissue and tumor specific isoforms are then validated via RT-PCR experiments over a spectrum of 40 tissue types. RESULTS: Our strategy revealed 427 genes with at least one tissue specific transcript as well as 1120 genes showing tumor specific isoforms. While our experimental evaluation of computationally predicted tissue-specific isoforms revealed a high success rate in confirming the expression of these isoforms in the respective tissue, the strategy frequently failed to detect the expected restricted expression pattern. The analysis of putative lowly expressed transcripts using normalized cDNA libraries suggests that our ability to detect tissue-specific isoforms strongly depends on the expression level of the respective transcript as well as on the sensitivity of the experimental methods. Especially splice isoforms predicted to be disease-specific tend to represent transcripts that are expressed in a set of healthy tissues rather than novel isoforms. CONCLUSIONS: We propose to combine the computational prediction of alternative splice isoforms with experimental validation for efficient delineation of an accurate set of tissue-specific transcripts

Texaco and its consultants

An issue relevant to scientific integrity has arisen in connection with a court case in the Amazon, wherein the Amazonian people are seeking redress for environmental damage and deleterious health effects related to the operations of Texaco in the Amazon region of Ecuador.ments are now required\u2014putting the onus where it belongs: on those who are responsible for the potential health impacts. In fact, environmental health impact assessments are increasingly addressing not only direct (toxicologic), but also indirect impacts of development projects

A Statistical Thermodynamic Approach for Predicting the Sequence-Dependent Nucleosome Positioning Along Genomes

Nucleosomes are the fundamental repeating unit of chromatin and constitute the structural building blocks of the eukaryotic genome. The distribution of nucleosomes along the genome is a significant aspect of chromatin structure and influences gene regulation through modulation of DNA accessibility. For this reason, an increasing interest is arising in models capable of predicting the nucleosome positioning along genomes. Toward this goal, we propose a theoretical model for predicting nucleosome thermodynamic stability in terms of DNA sequence. The model, based on a statistical mechanical approach allows the calculation of the canonical ensemble free energy involved in nucleosome formation. The theoretical free energies were evaluated for about one hundred nucleosome DNA tracts and successfully compared with those obtained in different laboratories with nucleosome competitive reconstitution (correlation coefficient equal to 0.92). We extended these results to the nucleosome positioning along genomes. To test our model, the theoretical nucleosome distribution was compared with that of yeast genome experimentally determined. The results are comparable with those obtained by different authors adopting models based on identifying some recurrent sequence features obtained from the statistical analysis of a very large pool of nucleosomal DNA sequences provided by the positioning maps of genomes