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Articles 1 - 2 of 2
Full-Text Articles in Genetics and Genomics
Recurrent Tissue-Specific Mtdna Mutations Are Common In Humans, David C. Samuels, Chun Li, Bingshan Li, Zhuo Song, Eric Torstenson, Hayley Boyd Clay, Antonis Rokas, Tricia A. Thornton-Wells, Jason H. Moore, Tia M. Hughes, Robert D. Hoffman, Jonathan L. Haines, Deborah G. Murdock, Douglas P. Mortlock, Scott M. Williams
Recurrent Tissue-Specific Mtdna Mutations Are Common In Humans, David C. Samuels, Chun Li, Bingshan Li, Zhuo Song, Eric Torstenson, Hayley Boyd Clay, Antonis Rokas, Tricia A. Thornton-Wells, Jason H. Moore, Tia M. Hughes, Robert D. Hoffman, Jonathan L. Haines, Deborah G. Murdock, Douglas P. Mortlock, Scott M. Williams
Dartmouth Scholarship
Mitochondrial DNA (mtDNA) variation can affect phenotypic variation; therefore, knowing its distribution within and among individuals is of importance to understanding many human diseases. Intra-individual mtDNA variation (heteroplasmy) has been generally assumed to be random. We used massively parallel sequencing to assess heteroplasmy across ten tissues and demonstrate that in unrelated individuals there are tissue-specific, recurrent mutations. Certain tissues, notably kidney, liver and skeletal muscle, displayed the identical recurrent mutations that were undetectable in other tissues in the same individuals. Using RFLP analyses we validated one of the tissue-specific mutations in the two sequenced individuals and replicated the patterns in …
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 …