Joel Graber


  • 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,, 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

  • Loss of Krüppel-like factor 9 deregulates both physiological gene expression and development. Drepanos L. et al. Sci. Rep . 2023 Jul 28;13(1):12239. doi: 10.1038/s41598-023-39453-3
  • GLH-1/Vasa represses neuropeptide expression and drives spermiogenesis in the C. elegans germline. Rochester JD et al. Dev Biol . 2022 Dec;492:200-211. doi: 10.1016/j.ydbio.2022.10.003. Epub 2022 Oct 21.
  • Macrophage differentiation is marked by increased abundance of the mRNA 3′ end processing machinery, altered poly(A) site usage, and sensitivity to the level of CstF64. Mukherjee S et al. Front Immunol . 2023 Jan 25;14:1091403. doi: 10.3389/fimmu.2023.1091403. eCollection 2023.
  • A Genomically and Clinically Annotated Patient-Derived Xenograft Resource for Preclinical Research in Non-Small Cell Lung Cancer. Woo XY et al. Cancer Res . 2022 Nov 15;82(22):4126-4138. doi: 10.1158/0008-5472.CAN-22-0948.
  • Differential effects of RASA3 mutations on hematopoiesis are profoundly influenced by genetic background and molecular variant. Robledo RF et al. PLoS Genet . 2020 Dec 28;16(12):e1008857. doi: 10.1371/journal.pgen.1008857. eCollection 2020 Dec.
  • Klf9 is a key feedforward regulator of the transcriptomic response to glucocorticoid receptor activity. Gans I et al. Sci Rep . 2020 Jul 10;10(1):11415. doi: 10.1038/s41598-020-68040-z.
  • Salisbury J, Hutchison KW, Wigglesworth K, Eppig JJ, Graber JH. 2009. Probe-level analysis of expression microarrays characterizes isoform-specific degradation during mouse oocyte maturation. PLoS One. 4(10):e7479
  • Singh P, Alley TL, Wright SM, Kamdar S, Schott W, Wilpan RY, Mills KD, Graber JH. 2009. Global changes in processing of mRNA 3’ untranslated regions characterize clinically distinct cancer subtypes. Cancer Research. (in press)
  • Hutchins LN, Murphy SM, Singh P, Graber JH. 2008. Position-dependent motif characterization using non-negative matrix factorization. Bioinformatics. 24(23):2684-90.
  • DeVries WN, Evsikov AV, Brogan LJ, Anderson CP, Graber JH, Knowles BB, Solter D. 2008. Reprogramming and Differentiation in Mammals: Motifs and Mechanisms. Cold Spring Harbor Symp Quant Biol. 73:33-8.
  • Paigen K, Szatkiewicz JP, Sawyer K, Leahy N, Parvanov ED, Ng SH, Graber JH, Broman KW, Petkov PM. 2008. The recombinational anatomy of a mouse chromosome. PLoS Genet. 4(7):e1000119.
  • Graber JH, Salisbury J, Hutchins LN, Blumenthal T. 2007. C. elegans sequences that control trans-splicing and operon pre-mRNA processing. RNA. 13(9):1409-26
  • Liu D, Brockman JM, Dass B, Hutchins LN, Singh P, McCarrey JR, MacDonald CC, Graber JH. 2007. Systematic variation in mRNA 3′-processing signals during mouse spermatogenesis. Nucleic Acids Res. 35(1):234-46.
  • Liu D, Graber JH. 2006. Quantitative comparison of EST libraries requires compensation for systematic biases in cDNA generation. BMC Bioinformatics 7:77.
  • Salisbury J, Hutchison KW, Graber JH. 2006. A multispecies comparison of the metazoan 3′-processing downstream elements and the CstF-64 RNA recognition motif. BMC Genomics 7:55.
  • Evsikov AV, Graber JH, Brockman JM, Hampl A, Holbrook AE, Singh P, Eppig JJ, Solter D, Knowles BB. 2006. Cracking the egg: molecular dynamics and evolutionary aspects of the transition from the fully grown oocyte to embryo. Genes Dev. 20(19):2713-27.
  • Graber JH, Churchill GA, Dipetrillo KJ, King BL, Petkov PM, Paigen K. 2006. Patterns and mechanisms of genome organization in the mouse. J Exp Zoolog. 305A(9):683-8.
  • Brockman JM, Singh P, Liu D, Quinlan S, Salisbury J, Graber JH. 2005. PACdb: PolyA cleavage site and 3′-UTR database. Bioinformatics 21:3691-3693.
  • Petkov PM, Graber JH, Churchill GA, Dipetrillo K, King BL, Paigen K. 2005. Evidence of a large-scale functional organization of mammalian chromosomes. PLoS Genet 1:e33.
  • Peaston AE, Evsikov AV, Graber JH, de Vries WN, Holbrook AE, Solter D, Knowles BB. 2004. Retrotransposons regulate host genes in mouse oocytes and preimplantation embryos. Dev Cell 7:597-606.
  • Graber JH. 2003. Variations of 3’-processing cis-elements in yeast correlate with transcript stability. Trends Genet 19:473-6.