Content-based microarray search using differential expression profiles

<p>Abstract</p> <p>Background</p> <p>With the expansion of public repositories such as the Gene Expression Omnibus (GEO), we are rapidly cataloging cellular transcriptional responses to diverse experimental conditions. Methods that query these repositories based on gene expression content, rather than textual annotations, may enable more effective experiment retrieval as well as the discovery of novel associations between drugs, diseases, and other perturbations.</p> <p>Results</p> <p>We develop methods to retrieve gene expression experiments that differentially express the same transcriptional programs as a query experiment. Avoiding thresholds, we generate differential expression profiles that include a score for each gene measured in an experiment. We use existing and novel dimension reduction and correlation measures to rank relevant experiments in an entirely data-driven manner, allowing emergent features of the data to drive the results. A combination of matrix decomposition and <it>p</it>-weighted Pearson correlation proves the most suitable for comparing differential expression profiles. We apply this method to index all GEO DataSets, and demonstrate the utility of our approach by identifying pathways and conditions relevant to transcription factors Nanog and FoxO3.</p> <p>Conclusions</p> <p>Content-based gene expression search generates relevant hypotheses for biological inquiry. Experiments across platforms, tissue types, and protocols inform the analysis of new datasets.</p

TVSBS: A fast exact pattern matching algorithm for biological sequences

The post-genomic era is witnessing a remarkable increase in the number of nucleotide and amino acid sequences. The content of biological sequence databases almost doubles frequently. Pattern matching emerges as a powerful tool in locating nucleotide or amino acid sequence patterns in the biological sequence databases. Presently, several pattern-matching algorithms are available in the literature right from the basic Brute Force algorithm to the recent SSABS. The efficiency of the various algorithms depends on faster and exact identification of the pattern in the text. In this article, we propose an exact pattern-matching algorithm for biological sequences. The proposed algorithm, TVSBS, is a combination of Berry–Ravindran and SSABS algorithms. The performance of the new algorithm has been improved using the shift of Berry–Ravindran bad character table, which leads to lesser number of character comparisons. It works consistently well for both nucleotide and amino acid sequences. The proposed algorithm has been compared with the recent algorithm, SSABS. The results show the robustness of the proposed algorithm and thus it can be incorporated in any exact pattern-matching applications involving biological sequences. The best- and worst-case time complexities of the new algorithm are also outlined