Research in the Moses Lab
The research projects we pursue typically weave together many threads from disciplines such as evolutionary genetics, systems biology, machine learning, sequence analysis, computer vision, and more. The projects we have pursued over the years reflect the diverse interests of the graduate students and postdocs who worked in the lab. Below is a listing of the main themes that define and guide many of our research projects. For a complete picture of the work that we’ve accomplished, please visit our Publications page.
Evolution and Dynamics of Regulatory Networks
Most complex cellular processes are carried out by groups of genes working together in so-called pathways or networks. We seek to understand how these networks are encoded in genome sequences, how they create dynamic biological phenotypes, and they are created by evolution.
LATEST PAPERS:
| Duncan AG, Mitchell JA, Moses AM. Improving the performance of supervised deep learning for regulatory genomics using phylogenetic augmentation.Bioinformatics. Published online April 8, 2024. Bioinformatics Link PubMed Abstract Article Deep Learning Phylogenetics Data Augmentation |
| Hsu IS, Strome B, Lash E, Robbins N, Cowen LE, Moses AM. A functionally divergent intrinsically disordered region underlying the conservation of stochastic signaling PLoS Genet. 2021 Sep 10; 17(9):e1009629 PLoS Genetics Link PDF Mirror PubMed Abstract Article Regulatory networks Intrinsically Disordered Regions Time lapse microscopy Cell signalingMolecular evolution |
Microscope images are big data
Automated microscopy has made it possible to measuring protein abundance and subcellular localization in millions of single cells. We are developing computational tools to extract basic biology from huge collections of microscope images without have to look at each one.
LATEST PAPERS:
| Hua SBZ, Lu AX, Moses AM. CytoImageNet: A large-scale pretraining dataset for bioimage transfer learning Learning Meaningful Representations of Life (LMRL 2021) arxiv Link PDF Mirror Article Deep learning Image Analysis Microscopy Screens |
| Lu AX Unsupervised machine learning for hypothesis discovery and representation learning in biological image and sequence analysis University of Toronto 2021 Thesis Link PhD thesis Deep Learning Self-supervised Microscopy screensImage Analysis Intrinsically Disordered Regions Subcellular localization |
Molecular Evolution of Disordered Regions
Intrinsically Disordered Regions (or IDRs) are enigmatic protein regions that are involved in a wide variety of biological processes. Although they are widespread, they usually show little evolutionary conservation. Is this rapid evolution a sign that they are just "junk" protein, or do they facilitate evolutionary diversity? This question is also of medical relevance: when we find mutations in patients' IDRs we currently cannot tell what impact (if any) they are having.
LATEST PAPERS:
| Duffy EE, Finander B, Choi G, Carter AC, Pritisanac I, Alam A, Luria V, Karger A, Phu W, Sherman MA, Assad EG, Pajarillo N, Khitun A, Crouch EE, Ganesh S, Chen J, Berger B, Sestan N, O'Donnell-Luria A, Huang EJ, Griffith EC, Forman-Kay JD, Moses AM, Kalish BT, Greenberg ME. Developmental dynamics of RNA translation in the human brain. Nat Neurosci. Oct;25(10):1353-1365. Nature Neuroscience Link PDF Mirror PubMed Abstract Article Proteomics Intrinsically Disordered Regions Proteomics Brain |
| Sangster AG, Zarin T, Moses AM. Evolution of short linear motifs and disordered proteins Topic: yeast as model system to study evolution. Curr Opin Genet Dev Oct;76:101964 Curr Opin Genet Dev Link PDF Mirror PubMed Abstract Article Intrinsically Disordered Regions Molecular Evolution |
Beautiful bioinformatics for genomics and proteomics
Complete sequencing of genomes is now routine, and yields thousands of genes and proteins, and information about the genetic differences in populations. All of this data needs to be organized and analyzed: bioinformatics!
LATEST PAPERS:
| Duncan AG, Mitchell JA, Moses AM. Improving the performance of supervised deep learning for regulatory genomics using phylogenetic augmentation.Bioinformatics. Published online April 8, 2024. Bioinformatics Link PubMed Abstract Article Deep Learning Phylogenetics Data Augmentation |
| Duffy EE, Finander B, Choi G, Carter AC, Pritisanac I, Alam A, Luria V, Karger A, Phu W, Sherman MA, Assad EG, Pajarillo N, Khitun A, Crouch EE, Ganesh S, Chen J, Berger B, Sestan N, O'Donnell-Luria A, Huang EJ, Griffith EC, Forman-Kay JD, Moses AM, Kalish BT, Greenberg ME. Developmental dynamics of RNA translation in the human brain. Nat Neurosci. Oct;25(10):1353-1365. Nature Neuroscience Link PDF Mirror PubMed Abstract Article Proteomics Intrinsically Disordered Regions Proteomics Brain |