Joel Graber

Education

• Michigan Technological University, 1987, B.S. Physics
• Michigan Technological University, 1987, B.S. Computer Science
• Cornell University, 1990, M.S. Physics
• Cornell University, 1993, Ph.D. Physics

Brief Biography

Dr. Graber is Director of Computational Biology, Bioinformatics, and Data Science Efforts at MDI Biological Laboratory, and is co-Director of the Maine INBRE Data Science Core.

The MDI Biological Laboratory Computational Biology/Bionformatics (CompBio) Core is focused on collaboration, analysis, and education in the computational analysis of genome-scale data. Their efforts are distributed between the linked goals of (1) providing our collaborating research groups with experimental data analysis/management and computational resources, and (2) providing training in computational biology both within MDIBL and also as part of the Maine INBRE (IDeA Networks of Biomedical Research Excellence) research program.

Biomedical research is dependent on data management and increasingly sophisticated analysis workflows that are both rigorous and reproducible. The CompBio Core has the experience and knowledge to provide training, analysis, and infrastructure that enable our collaborating researchers to accomplish their research goals. We have especially focused our recent efforts on the combined strengths of Cloud Computing (through AWS, GCP, and other resources) and Community-supported research workflows, specifically using the Nextflow NF-core pipelines.

While the Core does not accept graduate students for research positions, Dr. Graber has previously been (and currently is) a member of several GSBSE graduate student thesis committees.

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Research Interests

The study of the role and impact of genetic variation in the sequence elements that control mRNA processing, with a specific focus on polyadenylation

Our recent studies have helped to illuminate the functional significance of systematic alternative polyadenylation at different stages of development, in different cell types, and in primary tumor samples. Carrying this work forward, we will now extend these studies to explore the relationship of genetic variability and control of alternative polyadenylation. We will build upon our existing database of polyA sites (PACdb,http://harlequin.jax.org/pacdb/), including new, large-scale analyses derived from microarray and high-throughput mRNA sequencing efforts. Through this research program, we expect to generate and disseminate a genome-wide view of polyadenylation in mouse, the preeminent model mammalian system. Integration with genetic variation, reinforced with experimental validation of selected predictions, will provide new insights into the control, extent and consequences of alternative polyadenylation

The role and downstream consequences of disrupted regulation of mRNA processing in tumorigenesis

We recently developed and applied a probe-level microarray analysis to data obtained from mouse models of pre-B-cell lymphoma, resulting in the identification of genome-wide, systematic, and characteristic changes in mRNA processing. This work contributed to a growing understanding of the role of alternative processing (specifically alternative polyadenylation) as a part of tumorigenesis. This work has the potential to provide new models for, and understanding of, the disruption in regulation that accompanies tumor initiation and progression. As we move forward, we plan to broaden the studies to additional types of tumors, while also switching from microarray to mRNA-seq or other high-throughput sequencing-based methodologies.

The continued development and validation of computational approaches to regulatory motif identification and characterization

We have an interest in developing improved methods for identification and characterization of the regulatory sequences that guide mRNA processing and gene regulation. The majority of the approaches available in popular tools pay little or no attention to positioning of the motifs, despite the clear role that positioning plays in many critical processes, such as splicing and polyadenylation. Our recent work includes a novel motif characterization based on non-negative matrix factorization. Our methodology has the unique feature of simultaneously determining both sequence content and positioning. We envision a number of improvements and investigations of alternative approaches as the work progresses.

Selected Publications