Clustering-based approaches to SAGE data mining

Serial analysis of gene expression (SAGE) is one of the most powerful tools for global gene expression profiling. It has led to several biological discoveries and biomedical applications, such as the prediction of new gene functions and the identification of biomarkers in human cancer research. Clustering techniques have become fundamental approaches in these applications. This paper reviews relevant clustering techniques specifically designed for this type of data. It places an emphasis on current limitations and opportunities in this area for supporting biologically-meaningful data mining and visualisation

Modeling SAGE tag formation and its effects on data interpretation within a Bayesian framework

<p>Abstract</p> <p>Background</p> <p>Serial Analysis of Gene Expression (SAGE) is a high-throughput method for inferring mRNA expression levels from the experimentally generated sequence based tags. Standard analyses of SAGE data, however, ignore the fact that the probability of generating an observable tag varies across genes and between experiments. As a consequence, these analyses result in biased estimators and posterior probability intervals for gene expression levels in the transcriptome.</p> <p>Results</p> <p>Using the yeast <it>Saccharomyces cerevisiae </it>as an example, we introduce a new Bayesian method of data analysis which is based on a model of SAGE tag formation. Our approach incorporates the variation in the probability of tag formation into the interpretation of SAGE data and allows us to derive exact joint and approximate marginal posterior distributions for the mRNA frequency of genes detectable using SAGE. Our analysis of these distributions indicates that the frequency of a gene in the tag pool is influenced by its mRNA frequency, the cleavage efficiency of the anchoring enzyme (AE), and the number of informative and uninformative AE cleavage sites within its mRNA.</p> <p>Conclusion</p> <p>With a mechanistic, model based approach for SAGE data analysis, we find that inter-genic variation in SAGE tag formation is large. However, this variation can be estimated and, importantly, accounted for using the methods we develop here. As a result, SAGE based estimates of mRNA frequencies can be adjusted to remove the bias introduced by the SAGE tag formation process.</p

A human glomerular SAGE transcriptome database

Background: To facilitate in the identification of gene products important in regulating renal glomerular structure and function, we have produced an annotated transcriptome database for normal human glomeruli using the SAGE approach. Description: The database contains 22,907 unique SAGE tag sequences, with a total tag count of 48,905. For each SAGE tag, the ratio of its frequency in glomeruli relative to that in 115 non-glomerular tissues or cells, a measure of transcript enrichment in glomeruli, was calculated. A total of 133 SAGE tags representing well-characterized transcripts were enriched 10-fold or more in glomeruli compared to other tissues. Comparison of data from this study with a previous human glomerular Sau3A-anchored SAGE library reveals that 47 of the highly enriched transcripts are common to both libraries. Among these are the SAGE tags representing many podocyte-predominant transcripts like WT-1, podocin and synaptopodin. Enrichment of podocyte transcript tags SAGE library indicates that other SAGE tags observed at much higher frequencies in this glomerular compared to non-glomerular SAGE libraries are likely to be glomerulus-predominant. A higher level of mRNA expression for 19 transcripts represented by glomerulus-enriched SAGE tags was verified by RT-PCR comparing glomeruli to lung, liver and spleen. Conclusions: The database can be retrieved from, or interrogated online at http://cgap.nci.nih.gov/SAGE. The annotated database is also provided as an additional file with gene identification for 9,022, and matches to the human genome or transcript homologs in other species for 1,433 tags. It should be a useful tool for in silico mining of glomerular gene expression

Efficient pairwise RNA structure prediction and alignment using sequence alignment constraints

BACKGROUND: We are interested in the problem of predicting secondary structure for small sets of homologous RNAs, by incorporating limited comparative sequence information into an RNA folding model. The Sankoff algorithm for simultaneous RNA folding and alignment is a basis for approaches to this problem. There are two open problems in applying a Sankoff algorithm: development of a good unified scoring system for alignment and folding and development of practical heuristics for dealing with the computational complexity of the algorithm. RESULTS: We use probabilistic models (pair stochastic context-free grammars, pairSCFGs) as a unifying framework for scoring pairwise alignment and folding. A constrained version of the pairSCFG structural alignment algorithm was developed which assumes knowledge of a few confidently aligned positions (pins). These pins are selected based on the posterior probabilities of a probabilistic pairwise sequence alignment. CONCLUSION: Pairwise RNA structural alignment improves on structure prediction accuracy relative to single sequence folding. Constraining on alignment is a straightforward method of reducing the runtime and memory requirements of the algorithm. Five practical implementations of the pairwise Sankoff algorithm – this work (Consan), David Mathews' Dynalign, Ian Holmes' Stemloc, Ivo Hofacker's PMcomp, and Jan Gorodkin's FOLDALIGN – have comparable overall performance with different strengths and weaknesses