Graduate Research Assistantship in Biomedical Imaging Informatics (available Aug. 2019):
July 25, 2019
Many heart patients have not benefitted from MR scans due to technical limitations. A Ph.D. student is sought to further develop the new TRENDimaging software package to analyze complex and irregular heart motions. This will support wider application of ultrafast cardiac MR scans. The position is supported by a new grant from the American Heart […]
Dr. Eileen Avery takes the helm as the new Executive Director of the University of Missouri Research Data Center (MU RDC) and Population, Education, and Health Center (PEHC).
July 10, 2019
Dr. J. Chris Pires led a multi-institutional team to study vegetable family tree for better food and published in Nature Communications
Dr. Chris Pires led a multi-institutional team to study vegetable family tree for better food and published in Nature Communications. https://www.sciencedaily.com/releases/2019/07/190708154106.htm
Leadership Auditorium, 2501 Student Center
Hierarchical agglomerative clustering of eusocial bee proteins
The assembly and annotation of the European Honey bee (Apis mellifera) genome has predicted more than 15,000 protein-coding genes and became the foundation for studies of nature and evolution of eusociality. Since then, other eusocial bee genomes have been sequenced, providing an excellent opportunity to seek additional insight into unique traits of eusocial bees at the genomic and proteomic level. We wish to build a non-redundant organization of protein sequences by applying an unsupervised hierarchical protein-clustering method to the protein sequences of 4 advanced eusocial honey bees and 2 primitively eusocial bumblebees. The clustering method will group proteins into clusters according to their sequence similarity, generating homogeneous protein domain organization within clusters. The goal is to generate a final organization that consists of protein clusters representing biological families. In this seminar presentation, we will discuss the pros and cons of different agglomerative clustering methods and the performance of different linkage criteria. We also propose the cluster domain consistency (CDC) score, a new metric to validate protein cluster sets.
Leadership Auditorium, 2501 Student Center
Mutational Forks: Inferring Pathway Deregulation Based on Patient-Specific Genomics Profiles
The precise mechanism behind treatment resistance in cancer is still not fully understood. Despite advances in precision oncology, there is a lack of the tools that help to understand a mechanistic picture of treatment resistance in cancer patients. Existing enrichment methods heavily rely on quantitative data and limited to analysis of differentially expressed genes, ignoring crucial players that might be involved in this process. In order to tackle treatment resistance, the identification of deregulated flow of signal transduction is critical. Here, we introduce a bioinformatics framework that is capable of inferring deregulated flow of signal transduction given evidence-based knowledge about pathway topology and patient-specific mutations. While testing the proposed pipeline on a case study, our algorithm was able to confirm findings from biological experiment, where KRAS mutant cells developed treatment resistance to MEK inhibitor. Our model provides a framework for mechanistic understanding of acquired treatment resistance, thus, equipped clinicians with tool for searching more accurate diagnostic clues in patients with non-trivial disease representations.
240 Naka Hall
LARGE-SCALE SOYBEAN GENOME-WIDE VARIATION WORKFLOW AND ASSOCIATION ANALYSIS USING DEEP LEARNING
With the advances in next-generation sequencing technology and significant reduction in sequencing costs, it is now possible to sequence large collections of germplasm in crops for detecting genome-scale genetic variations, and apply the knowledge towards improvements in traits. To facilitate large-scale NGS resequencing data analysis of genomic variations efficiently, we developed a systematic solution using high-performance computing environment, cloud data storage resources and graphics processing unit computing with cutting-edge deep learning approach. The solution contains an integrated and optimized variant calling workflow called ‘PGen’, a quantitative phenotype prediction model using convolutional neural network and an algorithm to study genome-wide association study based on deep learning model. We reviewed and compared studies of statistical and deep learning genomic selection and genome-wide association methods, present our work on thousands of lines of soybean sequencing dataset, summarized ongoing progress of large-scale genome-associated studies and discussed the future work and development.