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JZ Song KM Duan T Ware M Surette 《EURASIP Journal on Bioinformatics and Systems Biology》2007,2007(1):39382
A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29] 相似文献
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Novel Regulatory Factors Interacting with the Promoter of the Gene Encoding the mRNA Cap Binding Protein (eIF4E) and Their Function in Growth Regulation 总被引:1,自引:0,他引:1
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Kelly A. Johnston Michael Polymenis Shanping Wang John Branda Emmett V. Schmidt 《Molecular and cellular biology》1998,18(10):5621-5633
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Francesco Fabris Andrea Sgarro Alessandro Tossi 《EURASIP Journal on Bioinformatics and Systems Biology》2007,2007(1):31450
Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29] 相似文献
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Thomas Farkas Yulia A. Kutskova Vincenzo Zimarino 《Molecular and cellular biology》1998,18(2):906-918