Computational Methods for Identifying and Characterizing the Human Gene Regulatory Regions and Cis-elements

Phylogenetic footprinting;Mixed Markov models;Alignment algorithm;Motif identification;Transcription regulation;CNS;Circular Markov chain;Gene regulatory networks

Huang, Weichun ; Leping Li, Committee Member,Bruce S. Weir, Committee Chair,William R. Atchley, Committee Member,Jeffrey L. Thorne, Committee Member,Russell D. Wolfinger, Committee Member,Huang, Weichun ; Leping Li ; Committee Member ; Bruce S. Weir ; Committee Chair ; William R. Atchley ; Committee Member ; Jeffrey L. Thorne ; Committee Member ; Russell D. Wolfinger ; Committee Member

University:North Carolina State University

关键词: Phylogenetic footprinting;    Mixed Markov models;    Alignment algorithm;    Motif identification;    Transcription regulation;    CNS;    Circular Markov chain;    Gene regulatory networks;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/5393/etd.pdf?sequence=1&isAllowed=y

美国|英语

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【 摘 要 】

The identification of functional regulatory regions and cis-elements is a preliminary step toward the reconstruction of gene regulatory networks. Comparative genomics has been demonstrated to be a powerful approach for motif discovery. However, the accurate alignment of complex genomic sequences, especially those of mammalians, remains a computational challenge. In chapter 2, we propose a novel pairwise alignment system, ACANA, to improve the alignment quality of genomic sequences. Compared with top competing alignment tools, ACANA achieves better alignment quality in aligning divergent sequences for both local and global alignments. When applied to the upstream sequences of human-mouse orthologs, ACANA is able to reliably detect the conserved functional regions containing most cis-elements.Statistical motif modeling is another fundamental computational approach for motif prediction in large genome sequence. In chapter 3, we introduce the mixture of optimized Markov models to reduce false motif discovery rate in large genomic sequences. Our model is not only able to incorporate most dependency information within a motif by optimizing the arrangement of motif positions, but also flexible for adjusting model complexity limited by the size of training data. We implement the mixture model in our OMiMa system. Using OMiMa, we demonstrate that our model can improve motif prediction accuracy.Although the reconstruction of complete human gene regulatory networks, at present, remains a distant hope, it is still possible to infer some distinct features of the networks from the available data. In chapter 4, we present an example of inferring major evolutionary features of human gene regulatory networks by combining information from both gene sequence data and functional annotations. We systematically analyze the association between gene function and upstream region conservation for human-rodent orthologs. Our study shows that upstream regulatory regions of developmental transcription regulators, such as Hox genes, are extremely conserved while those of catalytic enzymes are significantly less conserved.We suggest that developmental and other important regulators constitute the central hub of human gene regulatory networks.