Lab Information

Robert Nadon (PhD)

McGill University
Department of Human Genetics (McGill)

Research Profile

 Fundamental: 100%
 Clinical: 0%
 Epidemiology: 0%
 Evaluation: 0%
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Microarrays • experimental design • random error models • bias correction • normalization • affymetrix chips • high-throughput screening

Research Interests

My primary research interests during the last 10 years have been the development of data analysis approaches for high-throughput data for the following biotechnology applications:
  1. High-throughput screening (HTS) of small molecule and RNAi data
  2. Genome-wide mRNA translation
  3. Image-based high content screening (HCS)
  4. Microarray gene expression

    1. Team Members

      Name Position

      Latest Publications

      1. Charlier, J., Nadon, R. & Makarenkov, V. (2021). Accurate deep learning off-target prediction with novel sgRNA-DNA sequence encoding in CRISPR-Cas9 gene editing. Bioinformatics (Oxford, England), vol. 37, p. 2299-2307.
      2. Yarborough, M., Nadon, R. & Karlin, D. G. (2019). Four erroneous beliefs thwarting more trustworthy research. eLife, vol. 8.
      3. Piper, S. K., Grittner, U., Rex, A., Riedel, N., Fischer, F., Nadon, R., Siegerink, B. & Dirnagl, U. (2019). Exact replication: Foundation of science or game of chance?. PLoS biology, vol. 17, p. e3000188.
      4. Aguilar-Valles, A., Haji, N., De Gregorio, D., Matta-Camacho, E., Eslamizade, M. J., Popic, J., Sharma, V., Cao, R., Rummel, C., Tanti, A., Wiebe, S., Nuñez, N., Comai, S., Nadon, R., Luheshi, G., Mechawar, N., Turecki, G., Lacaille, J.-C., Gobbi, G. & Sonenberg, N. (2018). Translational control of depression-like behavior via phosphorylation of eukaryotic translation initiation factor 4E. Nature communications, vol. 9, p. 2459.
      5. Nadon, R. & Kayne, P. (2018). Statistics and Biology: Not Your Average Relationship. SLAS discovery : advancing life sciences R & D, vol. 23, p. 437-439.
      6. Mazoure, B., Caraus, I., Nadon, R. & Makarenkov, V. (2018). Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening. SLAS discovery : advancing life sciences R & D, vol. 23, p. 448-458.
      7. Mazoure, B., Nadon, R. & Makarenkov, V. (2017). Identification and correction of spatial bias are essential for obtaining quality data in high-throughput screening technologies. Scientific reports, vol. 7, p. 11921.
      8. Caraus, I., Mazoure, B., Nadon, R. & Makarenkov, V. (2017). Detecting and removing multiplicative spatial bias in high-throughput screening technologies. Bioinformatics (Oxford, England), vol. 33, p. 3258-3267.
      More on PubMed