Showing papers by "William L. Crosby published in 2014"

Genome-wide single nucleotide polymorphism and Insertion-Deletion discovery through next-generation sequencing of reduced representation libraries in common bean

Abstract: Single nucleotide polymorphisms (SNPs) and insertions-deletions (InDels) are valuable molecular markers for genomics and genetics studies and molecular breeding. The advent of next-generation sequencing techniques has enabled researchers to approach high-throughput and cost-effective SNP and InDel discovery on a genomic scale. In this report, 36 common bean genotypes grown in Canada were used to construct reduced representation libraries for next-generation sequencing. Using 76 million sequence reads generated by the Illumina HiSeq 2000 Sequencing System, we identified a total of 43,698 putative SNPs and 1,267 putative InDels. Of the SNPs, 43,504 were bi-allelic and 194 were tri-allelic, and the InDels comprised 574 insertions and 693 deletions. The putative bi-allelic SNPs were distributed across all 11 chromosomes with the highest number of SNPs observed in chromosome 2 (4,788), and the lowest in chromosome 10 (2,941). With the aid of the recent release of the first chromosome-scale version of Phaseolus vulgaris, 24,907 bi-allelic SNPs, 79 tri-allelic SNPs, 315 insertions, and 377 deletions were located in 8,758, 77, 273, and 364 genes, respectively. Among these 24,907 bi-allelic SNPs, 7,168 nonsynonymous bi-allelic SNPs were identified within 36 common bean genotypes that were located in 4,303 genes. A total of 113 putative SNPs were randomly chosen for validation using high-resolution melt analysis. Of the 113 candidate SNPs, 105 (92.9 %) contained the predicted SNPs.

Computational analysis of the stability of SCF ligases employing domain information

Abstract: Because of the unequivocally fundamental role of SCF ubiquitin ligase in many biological functions within a living cell including regulating DNA repair, cell cycle progression, and inflammation, we have analyzed the role of domain interactions in determining particular types of protein-protein interactions (PPIs) that are known or predicted to occur involving subunit components of the SCF-ligase complex. We focus on the prediction and analysis of obligate and non-obligate SCF-ligase complexes by using sequence domains from the Pfam database. After extracting different types of feature vectors, the prediction is performed via a support vector machine (SVM). The numerical results demonstrate that most of the interactions of SCF-ligase complexes are mediated by at least one domain. Moreover, domain-domain interactions dominate in obligate complexes whereas non-obligate complexes exhibit more domain-peptide chain interactions. Also, the computational results show that the best prediction accuracy of 80.46% is achieved using the combination of feature vectors of domain-domain type, domain-peptide chain type and no-domain interactions.