Ebbert, Mark T

Ebbert Computational Lab

Miller Computational Lab

Research Overview:

Drs. Mark Ebbert and Justin Miller are both working on various projects that would benefit from the supercomputer resources. All data stored on the supercomputer would be completely de-identified (IRB approval not required) or downloaded from publicly-available datasets (IRB approval not required).

Dr. Ebbert's research interests focus on identifying structural genomic variants unique to people with Alzheimer's disease. Additionally, Dr. Ebbert is developing novel algorithms to interpret data generated from long-read sequencing/imaging technologies such as the Oxford Nanopore Technologies (ONT) PromethION sequencer and the Bionano Saphyr genome imager. No patient information or identifiable data will be stored on the supercomputer. Drs. Ebbert and Miller are not involved in the deidentification of the data and do not have access to any protected health information that could be used to identify any of the samples.

Dr. Miller's research interests focus on the identification of Alzheimer's disease subtypes with different long-term health trajectories. Additionally, Dr. Miller specializes in algorithmic design and development with an emphasis in machine learning. Dr. Miller is currently assessing functional interactions between proteins by developing a novel algorithm to detect evolutionarily-conserved regions within two or more proteins. Dr. Miller has previously developed several phylogenomic algorithms to assess species relatedness, and he plans to continue that work by integrating phylogenic biases that he previously published (e.g., codon pairing, ramp sequences and codon aversion) into a single phylogenomic metric. Data used for those analyses would originate from publicly available repositories (e.g., RefSeq, NCBI, GenBank).    

Software:

We will use various open-source software packages that Drs. Miller and Ebbert have published (e.g., ExtRamp, JustOrthologs, The Protein Interactions Calculator [PIC], Kmer-SSR, The Polygenic Risk Score Knowledge Base, Ramps Online, CAM, codon pairing) to analyze the genomic data. Additionally, we plan to use various genomic analysis tools (e.g., GATK, SAMtools, bcftools, vcftools, blast) to analyze the genomic data. We will continue to develop novel software packages to integrate data generated from the Bionano Saphyr machine and the ONT PromethION sequencer to facilitate data integration between these recently-developed sequencing machines. Most applications will be written in Python, bash, C++, or Perl.

Experience:

Both Drs. Mark Ebbert and Justin Miller have degrees in Bioinformatics with Minors in Computer Science and have been working with large computing clusters for a combined experience of approximately 20 years, including completely managing small clusters. Most recently, Dr. Ebbert managed a shared compute cluster at the Mayo Clinic, and Dr. Miller directed a team of undergraduate and graduate students on a supercomputer at Brigham Young University.

Key Personnel:

Mark Ebbert, Ph.D. (Ebbert Computational Lab)

Justin Miller, Ph.D. (Miller Computational Lab)

Megan M Biesinger, Laboratory Technician, Added 04/14/2021

Timothy P Shannon, Research Data Solutions Director, Added 08/23/2021

Mark Wadsworth, Bioinformatics Analyst Senior, Added on MCC, 03/21/2022 

Students:

Bernardo Heberle

Erik Huckvale, added 10/13/2021

Sabrina M Krause, Dept of Neuroscience, Added 11/06/2021 on MCC cluster

Brittney A Williams, Graduate COM Office of Biomedical Education, Added on MCC cluster on 09/06/2022 

Grant A Fox, Graduate, Added 01/09/23 on MCC cluster

Bikram Karki, Graduate, Added 05/11/2023 on MCC cluster

Brendan J White, Added on MCC cluster 05/15/2023, Added on LCC cluster 08/08/2023  

Ketsile Ipeleng Dikobe, Added on MCC on 09/18/2023 

Research Associate:

Madeline L Page

Undergraduate:

Matthew W. Hodgman, Tech Scientific/UKHC Added, 9/24/2021

Grants:

Publications: