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Articles 1 - 4 of 4
Full-Text Articles in Medical Genetics
Systems Level Analysis Of Systemic Sclerosis Shows A Network Of Immune And Profibrotic Pathways Connected With Genetic Polymorphisms, J. Matthew Mahoney, Jaclyn Taroni, Viktor Martyanov, Tammara A. A. Wood, Casey S. Greene, Patricia A. Pioli, Monique E. Hinchcliff, Michael L. Whitfield
Systems Level Analysis Of Systemic Sclerosis Shows A Network Of Immune And Profibrotic Pathways Connected With Genetic Polymorphisms, J. Matthew Mahoney, Jaclyn Taroni, Viktor Martyanov, Tammara A. A. Wood, Casey S. Greene, Patricia A. Pioli, Monique E. Hinchcliff, Michael L. Whitfield
Dartmouth Scholarship
Systemic sclerosis (SSc) is a rare systemic autoimmune disease characterized by skin and organ fibrosis. The pathogenesis of SSc and its progression are poorly understood. The SSc intrinsic gene expression subsets (inflammatory, fibroproliferative, normal-like, and limited) are observed in multiple clinical cohorts of patients with SSc. Analysis of longitudinal skin biopsies suggests that a patient's subset assignment is stable over 6-12 months. Genetically, SSc is multi-factorial with many genetic risk loci for SSc generally and for specific clinical manifestations. Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes …
Transcription Factor Binding Profiles Reveal Cyclic Expression Of Human Protein-Coding Genes And Non-Coding Rnas, Chao Cheng, Matthew Ung, Gavin D. Grant, Michael L. Whitfield
Transcription Factor Binding Profiles Reveal Cyclic Expression Of Human Protein-Coding Genes And Non-Coding Rnas, Chao Cheng, Matthew Ung, Gavin D. Grant, Michael L. Whitfield
Dartmouth Scholarship
Cell cycle is a complex and highly supervised process that must proceed with regulatory precision to achieve successful cellular division. Despite the wide application, microarray time course experiments have several limitations in identifying cell cycle genes. We thus propose a computational model to predict human cell cycle genes based on transcription factor (TF) binding and regulatory motif information in their promoters. We utilize ENCODE ChIP-seq data and motif information as predictors to discriminate cell cycle against non-cell cycle genes. Our results show that both the trans- TF features and the cis- motif features are predictive of cell cycle genes, and …
Circadian Rhythmicity By Autocatalysis, Arun Mehra, Christian I. Hong, Mi Shi, Jennifer J. Loros, Jay C. Dunlap, Peter Ruoff
Circadian Rhythmicity By Autocatalysis, Arun Mehra, Christian I. Hong, Mi Shi, Jennifer J. Loros, Jay C. Dunlap, Peter Ruoff
Dartmouth Scholarship
The temperature compensated in vitro oscillation of cyanobacterial KaiC phosphorylation, the first example of a thermodynamically closed system showing circadian rhythmicity, only involves the three Kai proteins (KaiA, KaiB, and KaiC) and ATP. In this paper, we describe a model in which the KaiA- and KaiB-assisted autocatalytic phosphorylation and dephosphorylation of KaiC are the source for circadian rhythmicity. This model, based upon autocatalysis instead of transcription-translation negative feedback, shows temperature-compensated circadian limit-cycle oscillations with KaiC phosphorylation profiles and has period lengths and rate constant values that are consistent with experimental observations.
Interpreting Patterns Of Gene Expression With Self-Organizing Maps: Methods And Application To Hematopoietic Differentiation, Pablo Tamayo, Donna Slonim, Jill Mesirov, Qing Zhu, Sutisak Kitareewan, Ethan Dmitrovsky
Interpreting Patterns Of Gene Expression With Self-Organizing Maps: Methods And Application To Hematopoietic Differentiation, Pablo Tamayo, Donna Slonim, Jill Mesirov, Qing Zhu, Sutisak Kitareewan, Ethan Dmitrovsky
Dartmouth Scholarship
Array technologies have made it straightforward to monitor simultaneously the expression pattern of thousands of genes. The challenge now is to interpret such massive data sets. The first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of self-organizing maps, a type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features in complex, multidimensional data. The method has been implemented in a publicly available computer package, GENECLUSTER, that performs the analytical calculations and provides easy data visualization. To illustrate the value of such analysis, the …