Virginia-Maryland College of Veterinary Medicine (VMCVM)

Permanent URI for this community

https://hdl.handle.net/10919/5559
The Virginia-Maryland College of Veterinary Medicine is a two-state, three-campus professional school operated by the land-grant universities of Virginia Tech in Blacksburg and the University of Maryland at College Park. In addition to the main campus installation at Virginia Tech, the College also operates the Avrum Gudelsky Veterinary Center at College Park, and the Marion duPont Scott Equine Medical Center in Leesburg.

Browse

Browsing Virginia-Maryland College of Veterinary Medicine (VMCVM) by Department "Computer Science"

Now showing 1 - 3 of 3
  • Thumbnail Image
    CAGm: A repository of germline microsatellite variations in the 1000 genomes project
    Kinney, N.; Titus-Glover, K.; Wren, J.D.; Varghese, Ronnie; Michalak, Pawel; Liao, H.; Anandakrishnan, Ramu; Pulenthiran, A.; Kang, L.; Garner, Harold R. (Oxford University Press, 2019-01-08)
    The human genome harbors an abundance of repetitive DNA; however, its function continues to be debated. Microsatellites-a class of short tandem repeat-are established as an important source of genetic variation. Array length variants are common among microsatellites and affect gene expression; but, efforts to understand the role and diversity of microsatellite variation has been hampered by several challenges. Without adequate depth, both long-read and short-read sequencing may not detect the variants present in a sample; additionally, large sample sizes are needed to reveal the degree of population-level polymorphism. To address these challenges we present the Comparative Analysis of Germline Microsatellites (CAGm): A database of germline microsatellites from 2529 individuals in the 1000 genomes project. A key novelty of CAGm is the ability to aggregate microsatellite variation by population, ethnicity (super population) and gender. The database provides advanced searching for microsatellites embedded in genes and functional elements. All data can be downloaded as Microsoft Excel spreadsheets. Two use-case scenarios are presented to demonstrate its utility: A mononucleotide (A) microsatellite at the BAT-26 locus and a dinucleotide (CA) microsatellite in the coding region of FGFRL1. CAGm is freely available at http://www.cagmdb.org/. © The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research.
  • Thumbnail Image
    Identifying multi-hit carcinogenic gene combinations: Scaling up a weighted set cover algorithm using compressed binary matrix representation on a GPU
    Al Hajri, Qais; Dash, Sajal; Feng, Wu-chun; Garner, Harold R.; Anandakrishnan, Ramu (Nature Publishing Group, 2020-02-06)
    Despite decades of research, effective treatments for most cancers remain elusive. One reason is that different instances of cancer result from different combinations of multiple genetic mutations (hits). Therefore, treatments that may be effective in some cases are not effective in others. We previously developed an algorithm for identifying combinations of carcinogenic genes with mutations (multi-hit combinations), which could suggest a likely cause for individual instances of cancer. Most cancers are estimated to require three or more hits. However, the computational complexity of the algorithm scales exponentially with the number of hits, making it impractical for identifying combinations of more than two hits. To identify combinations of greater than two hits, we used a compressed binary matrix representation, and optimized the algorithm for parallel execution on an NVIDIA V100 graphics processing unit (GPU). With these enhancements, the optimized GPU implementation was on average an estimated 12,144 times faster than the original integer matrix based CPU implementation, for the 3-hit algorithm, allowing us to identify 3-hit combinations. The 3-hit combinations identified using a training set were able to differentiate between tumor and normal samples in a separate test set with 90% overall sensitivity and 93% overall specificity. We illustrate how the distribution of mutations in tumor and normal samples in the multi-hit gene combinations can suggest potential driver mutations for further investigation. With experimental validation, these combinations may provide insight into the etiology of cancer and a rational basis for targeted combination therapy.
  • Thumbnail Image
    Identifying Transcriptional Regulatory Modules Among Different Chromatin States in Mouse Neural Stem Cells
    Banerjee, Sharmi; Zhu, Hongxiao; Tang, Man; Feng, Wu-chun; Wu, Xiaowei; Xie, Hehuang David (Frontiers, 2019-01-15)
    Gene expression regulation is a complex process involving the interplay between transcription factors and chromatin states. Significant progress has been made toward understanding the impact of chromatin states on gene expression. Nevertheless, the mechanism of transcription factors binding combinatorially in different chromatin states to enable selective regulation of gene expression remains an interesting research area. We introduce a nonparametric Bayesian clustering method for inhomogeneous Poisson processes to detect heterogeneous binding patterns of multiple proteins including transcription factors to form regulatory modules in different chromatin states. We applied this approach on ChIP-seq data for mouse neural stem cells containing 21 proteins and observed different groups or modules of proteins clustered within different chromatin states. These chromatin-state-specific regulatory modules were found to have significant influence on gene expression. We also observed different motif preferences for certain TFs between different chromatin states. Our results reveal a degree of interdependency between chromatin states and combinatorial binding of proteins in the complex transcriptional regulatory process. The software package is available on Github at - https://github.com/BSharmi/DPM-LGCP.