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Full-Text Articles in Genomics
A Comparison Of The Caulobacter Na1000 And K31 Genomes Reveals Extensive Genome Rearrangements And Differences In Metabolic Potential, Craig Stephens, Kurt Ash, Theta Brown, Tynetta Watford, Latia E. Scott, Bert Ely
A Comparison Of The Caulobacter Na1000 And K31 Genomes Reveals Extensive Genome Rearrangements And Differences In Metabolic Potential, Craig Stephens, Kurt Ash, Theta Brown, Tynetta Watford, Latia E. Scott, Bert Ely
Biology
The genus Caulobacter is found in a variety of habitats and is known for its abilityto thrive in low-nutrient conditions. K31 is a novel Caulobacter isolate that has the ability to tolerate copper and chlorophenols, and can grow at 48Cwith a doubling time of 40 h. K31 contains a 5.5 Mb chromosome that codes for more than 5500 proteins and two large plasmids (234 and 178 kb) thatcode for 438 additional proteins. A comparison of the K31 and the Caulobactercrescentus NA1000 genomes revealed extensive rearrangements of gene order,suggesting that the genomes had been randomly scrambled. However, a careful analysis revealed …
Methylation Of Leukocyte Dna And Ovarian Cancer: Relationships With Disease Status And Outcome, Brooke L. Fridley, Sebastian M. Armasu, Mine S. Cicek, Melissa C. Larson, Chen Wang, Stacey J. Winham, Kimberly R. Kalli, Devin C. Koestler
Methylation Of Leukocyte Dna And Ovarian Cancer: Relationships With Disease Status And Outcome, Brooke L. Fridley, Sebastian M. Armasu, Mine S. Cicek, Melissa C. Larson, Chen Wang, Stacey J. Winham, Kimberly R. Kalli, Devin C. Koestler
Dartmouth Scholarship
Genome-wide interrogation of DNA methylation (DNAm) in blood-derived leukocytes has become feasible with the advent of CpG genotyping arrays. In epithelial ovarian cancer (EOC), one report found substantial DNAm differences between cases and controls; however, many of these disease-associated CpGs were attributed to differences in white blood cell type distributions. We examined blood-based DNAm in 336 EOC cases and 398 controls; we included only high-quality CpG loci that did not show evidence of association with white blood cell type distributions to evaluate association with case status and overall survival.
How To Get The Most From Microarray Data: Advice From Reverse Genomics, Ivan P. Gorlov, Ji-Yeon Yang, Jinyoung Byun, Christopher Logothetis, Olga Y. Gorlova, Kim-Anh Do, Christopher Amos
How To Get The Most From Microarray Data: Advice From Reverse Genomics, Ivan P. Gorlov, Ji-Yeon Yang, Jinyoung Byun, Christopher Logothetis, Olga Y. Gorlova, Kim-Anh Do, Christopher Amos
Dartmouth Scholarship
Whole-genome profiling of gene expression is a powerful tool for identifying cancer-associated genes. Genes differentially expressed between normal and tumorous tissues are usually considered to be cancer associated. We recently demonstrated that the analysis of interindividual variation in gene expression can be useful for identifying cancer associated genes. The goal of this study was to identify the best microarray data–derived predictor of known cancer associated genes. We found that the traditional approach of identifying cancer genes—identifying differentially expressed genes—is not very efficient. The analysis of interindividual variation of gene expression in tumor samples identifies cancer-associated genes more effectively. The results …