Recognizing sequences of sequences.
Ontology highlight
ABSTRACT:
SUBMITTER: Kiebel SJ
PROVIDER: S-EPMC2714976 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Expression of the multidrug efflux pump ttgDEF and ttgGHI operons is modulated in vivo mainly by the TtgV repressor. TtgV is a multidrug recognition repressor that exhibits a DNA binding domain with a long interaction helix comprising residues 47 to 64. The pattern of expression of the two pumps is different in Pseudomonas putida: in the absence of effectors, the promoter for the ttgD gene is silent, whereas the ttgG gene is expressed at a high basal level. This correlates with the fact that TtgV exhibits a higher affinity for the ttgD operator (K(D)=10+/-1 nM) than for the ttgG (K(D)=19+/-1 nM) operator. Sequence analysis revealed that both operators are 40% identical, and mutational analysis of the ttgD and ttgG operators combined with electrophoretic mobility shift assays and in vivo expression analysis suggests that TtgV recognizes an inverted repeat with a high degree of palindromicity around the central axis. We generated a collection of alanine substitution mutants with substitutions between residues 47 and 64 of TtgV. The results of extensive combinations of promoter variants with these TtgV alanine substitution mutants revealed that TtgV modulates expression from ttgD and ttgG promoters through the recognition of both common and different sequences in the two promoters. In this regard, we found that TtgV mutants at residues 48, 50, 53, 54, 60, and 61 failed to bind ttgG but recognized the ttgD operator. TtgV residues R47, R52, L57, and T49 are critical for binding to both operators. Based on three-dimensional models, we propose that these residues contact nucleotides within the major groove of DNA.
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Project description:With the ever-increasing pace of genome sequencing, there is a great need for fast and accurate computational tools to automatically identify genes in these genomes. Although great progress has been made in the development of gene-finding algorithms during the past decades, there is still room for further improvement. In particular, the issue of recognizing short exons in eukaryotes is still not solved satisfactorily. This article is devoted to assessing various linear and kernel-based classification algorithms and selecting the best combination of Z-curve features for further improvement of the issue. Eight state-of-the-art linear and kernel-based supervised pattern recognition techniques were used to identify the short (21-192?bp) coding sequences of human genes. By measuring the prediction accuracy, the tradeoff between sensitivity and specificity and the time consumption, partial least squares (PLS) and kernel partial least squares (KPLS) algorithms were verified to be the most optimal linear and kernel-based classifiers, respectively. A surprising result was that, by making good use of the interpretability of the PLS and the Z-curve methods, 93 Z-curve features were proved to be the best selective combination. Using them, the average recognition accuracy was improved as high as 7.7% by means of KPLS when compared with what was obtained by the Fisher discriminant analysis using 189 Z-curve variables (Gao and Zhang, 2004 ). The used codes are freely available from the following approaches (implemented in MATLAB and supported on Linux and MS Windows): (1) SVM: http://www.support-vector-machines.org/SVM_soft.html. (2) GP: http://www.gaussianprocess.org. (3) KPLS and KFDA: Taylor, J.S., and Cristianini, N. 2004. Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge, UK. (4) PLS: Wise, B.M., and Gallagher, N.B. 2011. PLS-Toolbox for use with MATLAB: ver 1.5.2. Eigenvector Technologies, Manson, WA. Supplementary Material for this article is available at www.liebertonline.com/cmb.
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Project description:BACKGROUND: Significant efforts have been made to address the problem of identifying short genes in prokaryotic genomes. However, most known methods are not effective in detecting short genes. Because of the limited information contained in short DNA sequences, it is very difficult to accurately distinguish between protein coding and non-coding sequences in prokaryotic genomes. We have developed a new Iteratively Adaptive Sparse Partial Least Squares (IASPLS) algorithm as the classifier to improve the accuracy of the identification process. RESULTS: For testing, we chose the short coding and non-coding sequences from seven prokaryotic organisms. We used seven feature sets (including GC content, Z-curve, etc.) of short genes.In comparison with GeneMarkS, Metagene, Orphelia, and Heuristic Approachs methods, our model achieved the best prediction performance in identification of short prokaryotic genes. Even when we focused on the very short length group ([60-100 nt)), our model provided sensitivity as high as 83.44% and specificity as high as 92.8%. These values are two or three times higher than three of the other methods while Metagene fails to recognize genes in this length range.The experiments also proved that the IASPLS can improve the identification accuracy in comparison with other widely used classifiers, i.e. Logistic, Random Forest (RF) and K nearest neighbors (KNN). The accuracy in using IASPLS was improved 5.90% or more in comparison with the other methods. In addition to the improvements in accuracy, IASPLS required ten times less computer time than using KNN or RF. CONCLUSIONS: It is conclusive that our method is preferable for application as an automated method of short gene classification. Its linearity and easily optimized parameters make it practicable for predicting short genes of newly-sequenced or under-studied species.
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