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|Title: ||The de novo Prediction of Functionally Significant Sequence Motifs in Arabidopsis thaliana.|
|Authors: ||Austin, Ryan|
|Advisor: ||Provart, Nicholas|
|Department: ||Cell and Systems Biology|
transcription factor binding site
|Issue Date: ||18-Feb-2010|
|Abstract: ||This thesis performs de novo predictions for functionally significant sequence motifs in the Arabidopsis genome under two separate contexts. Each study applies the use of genomic positional information, statistical over-representation and several biologically contextual filters to maximize the visibility of biological signal in prediction results. Numerous literature supported motifs are prevalent in the results of both studies and a number of novel motif patterns possess a strong potential for in planta significance.
The first study examines the statistical over-representation of C-terminal tripeptides as a means for identifying eukaryotic conserved protein targetting signatures. Comparative genomics is applied to the analysis of tripeptide frequencies in the C-terminus of 7 eukaryotic proteomes. While biological signal is maximized through the filtering of both simple sequences and homologous sequences present across protein families.
The second study introduces a methodology for the effective prediction of transcription factor binding sites in Arabidopsis. A collection of motif prediction algorithms and a novel enumerative strategy are applied to the prediction of cis-acting regulatory elements within the promoters of genes found coexpressed within distinct tissues and under specific abiotic stress treatments. Overall, the analysis identifies 4 known motifs in expected contexts, 5 known motifs in novel contexts and 7 novel motifs with a high potential for biological function.|
|Appears in Collections:||Doctoral|
Department of Cell and Systems Biology - Doctoral theses
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