Thesis (Ph.D.)--Washington State University. / Includes bibliographical references.

DNA sequence analysis involves precise discrimination of two of the sequence's most important components: exons and introns. Exons encode the proteins that are responsible for almost all the functions in a living organism. Introns interrupt the sequence coding for a protein and must be removed from primary RNA transcripts before translation to protein can occur. A pattern recognition technique called Finite Induction (FI) is utilized to study the language of exons and introns. FI is especially suited for analyzing and classifying large amounts of data representing sequences of interest. It requires no biological information and employs no statistical functions. Finite Induction is applied to the exon and intron components of DNA by building a collection of rules based upon what it finds in the sequences it examines. It then attempts to match the known rule patterns with new rules formed as a result of analyzing a new sequence. A high number of matches predict a probable close relationship between the two sequences; a low number of matches signifies a large amount of difference between the two. This research demonstrates FI to be a viable tool for measurement when known patterns are available for the formation of rule sets.

Thesis (M. Phil.)--University of Hong Kong, 2010. / Includes bibliographical references (leaves 120-131). Also available in print.

No description available.

The development, implementation, and performance evaluation of new techniques for the location of hot spots in proteins and exons in DNA using digital filters are presented. The application of bandpass notch (BPN) digital filters for locating hot spots in proteins is first investigated. A technique is proposed for designing the appropriate BPN filter for a specific protein sequence in which the area under the amplitude response is minimized to achieve maximum selectivity for a chosen stability margin. The minimization is performed using the golden-section search. A tuning technique is also proposed for improving the accuracy of the BPN filter. The tuning is carried out using a least-squares polynomial model. Several example protein sequences are used to illustrate these techniques. BPN filters are then employed for locating exons in DNA. An additional step of lowpass filtering is introduced in order to detect the strength of the bandpass filtered signal as a function of nucleotide location. For the character-to-numerical mapping, the application of the electron-ion interaction potentials (EIIPs) of the nucleotides as well as their binary sequences is investigated. The performance of the techniques is then evaluated using metrics such as sensitivity, specificity, accuracy, precision, and computational efficiency. These metrics are used in conjunction with the so-called receiver operating characteristic (ROC) technique to establish a reliable framework for the comparisons. For exon location, a technique based on the short-time discrete Fourier transform (STDFT) reported in the literature is also included in the comparison. The effect of using different window functions on the prediction accuracy of the technique is explored. Using a set of examples, it is shown that BPN filters predict short exons with better accuracy than the STDFT. The test dataset comprised 66 protein sequences and 160 DNA sequences obtained from the protein data bank and the HMR195 database, respectively. Results show that among the techniques considered, BPN filters perform best for the location of both protein hot spots and DNA exons in terms of accuracy and computational efficiency. User-friendly MATLAB implementations of the techniques incorporating graphical interfaces are also described. Optimized numerical mapping schemes are proposed for exon location using both EIIP as well as binary sequences. Characteristic numerical values are obtained for the four nucleotides using a training procedure in which the prediction accuracy is maximized using a quasi-Newton algorithm based on the Broyden-Fletcher-Goldfarb-Shanno updating formula. A training set of 80 DNA sequences is chosen from the HMR195 database and the objective function is formulated using the ROC technique. The procedure is initialized using EIIP values. Unbiased testing of the optimized values is carried out using a test set that has no overlap with the training set. Simulation results show that the optimized values yield more accurate exon locations than those obtained using the actual EIIP values. In addition, they perform significantly better than a set of existing optimized complex values. By employing a similar strategy to optimize the weights of the binary sequences, it is shown that, in practice, only three out of four binary sequences are necessary to obtain accurate estimates of exon locations. Consequently, a computational saving of 25% can be achieved, which is substantial considering that DNA sequences encountered in practice are very long in nature. / Graduate

published_or_final_version / Biological Sciences / Master / Master of Philosophy

<p>Alternative RNA splicing is one of the leading mechanisms contributing towards transcript and protein diversity. Several alternative splicing surveys have confirmed the frequent occurrence of exon skipping in human genes. However, the occurrence of exon skipping in mouse genes has not yet been extensively examined. Recent improvements in mouse genome sequencing have permitted the current study to explore the occurrence of exon skipping in mouse genes orthologous to human genes on chromosome 22. A low number (5/72 multi-exon genes) of mouse exon-skipped genes were captured through alignments of mouse ESTs to mouse genomic contigs. Exon-skipping events in two mouse exon-skipped genes (GNB1L, SMARCB1) appear to affect biological processes such as electron and protein transport. All mouse, skipped exons were observed to have ubiquitous tissue expression. Comparison of our mouse exon-skipping events to previously detected human exon-skipping events on chromosome 22 by Hide et al.2001, has revealed that mouse and human exon-skipping events were never observed together within an orthologous gene-pair. Although the transcript identity between mouse and human orthologous transcripts were high (greater than 80% sequence identity), the exon order in these gene-pairs may be different between mouse and human orthologous genes.<br /> <br /> Main factors contributing towards the low detection of mouse exon-skipping events include the lack of mouse transcripts matching to mouse genomic sequences and the under-prediction of mouse exons. These factors resulted in a large number (112/269) of mouse transcripts lacking matches to mouse genomic contigs and nearly half (12/25) of the mouse multi-exon genes, which have matching Ensembl transcript identifiers, have under-predicted exons. The low frequency of mouse exon skipping on chromosome 22 cannot be extrapolated to represent a genome-wide estimate due to the small number of observed mouse exon-skipping events. However, when compared to a higher estimate (52/347) of exon skipping in human genes for chromosome 22 produced under similar conditions by Hide et al.2001, it is possible that our mouse exon-skipping frequency may be lower than the human frequency. Our hypothesis contradicts with a previous study by Brett et al.2002, in which the authors claim that mouse and human alternative splicing is comparable. Our conclusion that the mouse exon-skipping frequency may be lower than the human estimate remains to be tested with a larger mouse multi-exon gene set. However, the mouse exon-skipping frequency may represent the highest estimate that can be obtained given that the current number (87) of mouse genes orthologous to chromosome 22 in Ensembl (v1 30th Jan. 2002) does not deviate significantly from our total number (72) of mouse multi-exon genes. The quality of the current mouse genomic data is higher than the one utilized in this study. The capture of mouse exon-skipping events may increase as the quality and quantity of mouse genomic and transcript sequences improves.</p>

Thèse de doctorat : Biologie cellulaire et moléculaire : Paris 12 : 2005. / Version électronique uniquement consultable au sein de l'Université Paris 12 (Intranet). Titre provenant de l'écran-titre. Bibliogr. : 150 réf.

<p>Alternative RNA splicing is one of the leading mechanisms contributing towards transcript and protein diversity. Several alternative splicing surveys have confirmed the frequent occurrence of exon skipping in human genes. However, the occurrence of exon skipping in mouse genes has not yet been extensively examined. Recent improvements in mouse genome sequencing have permitted the current study to explore the occurrence of exon skipping in mouse genes orthologous to human genes on chromosome 22. A low number (5/72 multi-exon genes) of mouse exon-skipped genes were captured through alignments of mouse ESTs to mouse genomic contigs. Exon-skipping events in two mouse exon-skipped genes (GNB1L, SMARCB1) appear to affect biological processes such as electron and protein transport. All mouse, skipped exons were observed to have ubiquitous tissue expression. Comparison of our mouse exon-skipping events to previously detected human exon-skipping events on chromosome 22 by Hide et al.2001, has revealed that mouse and human exon-skipping events were never observed together within an orthologous gene-pair. Although the transcript identity between mouse and human orthologous transcripts were high (greater than 80% sequence identity), the exon order in these gene-pairs may be different between mouse and human orthologous genes.<br /> <br /> Main factors contributing towards the low detection of mouse exon-skipping events include the lack of mouse transcripts matching to mouse genomic sequences and the under-prediction of mouse exons. These factors resulted in a large number (112/269) of mouse transcripts lacking matches to mouse genomic contigs and nearly half (12/25) of the mouse multi-exon genes, which have matching Ensembl transcript identifiers, have under-predicted exons. The low frequency of mouse exon skipping on chromosome 22 cannot be extrapolated to represent a genome-wide estimate due to the small number of observed mouse exon-skipping events. However, when compared to a higher estimate (52/347) of exon skipping in human genes for chromosome 22 produced under similar conditions by Hide et al.2001, it is possible that our mouse exon-skipping frequency may be lower than the human frequency. Our hypothesis contradicts with a previous study by Brett et al.2002, in which the authors claim that mouse and human alternative splicing is comparable. Our conclusion that the mouse exon-skipping frequency may be lower than the human estimate remains to be tested with a larger mouse multi-exon gene set. However, the mouse exon-skipping frequency may represent the highest estimate that can be obtained given that the current number (87) of mouse genes orthologous to chromosome 22 in Ensembl (v1 30th Jan. 2002) does not deviate significantly from our total number (72) of mouse multi-exon genes. The quality of the current mouse genomic data is higher than the one utilized in this study. The capture of mouse exon-skipping events may increase as the quality and quantity of mouse genomic and transcript sequences improves.</p>

Title from PDF of title page (University of Missouri--Columbia, viewed on Feb 25, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dissertation advisor: David J. Pintel. Vita. Includes bibliographical references.