I am a researcher at the Walter and Eliza Hall institute in the Papenfuss lab. I work on machine learning and am interested in developing techniques for knowledge discovery in biological data. In particular, I'm interested in precision medicine.
Machine learning methods for somatic genome rearrangement detection
Structural variants (SVs) are large-scale genomic changes and are an important type of mutation in cancer. SVs can occur through a variety of biological mechanisms leading to insertions, deletions, duplications, inversions, and translocations in the genome. These mutations can cause cancers and affect response to therapy. A student project is available to develop machine learning methods that generate the best possible results from whole genome tumour-normal sequencing data for each patient.
Papenfuss lab student projects
BioNix is a tool for reproducible bioinformatics that unifies workflow engines, package managers, and containers. It is implemented as a lightweight library on top of the Nix deployment system. BioNix is currently in use at WEHI and is actively developed.
svaRetro & svaNUMT
svaRetro and svaNUMT are R packages for detecting retrotransposed transcripts and mitocondrial insertions into the nuclear genome (NUMT) from structural variant calls.
- Anna Trigos, Benjamin Goudey, Justin Bedő, Tom Conway, Noel Faux, and Kelly Wyres. “Collateral Sensitivity to β-Lactam Drugs in Drug-Resistant Tuberculosis Is Driven by the Transcriptional Wiring of BlaI Operon Genes.” MSphere, 6(3). MSphere, 6(3) (2021). doi:10.1128/msphere.00245-21.
- Ken Chow, Justin Bedő, Andrew Ryan, Dinesh Agarwal, Damien Bolton, Yee Chan, Philip Dundee, et al. “Ductal Variant Prostate Carcinoma Is Associated with a Significantly Shorter Metastasis-Free Survival.” European Journal of Cancer 148 (May 2021): 440–450. doi:10.1016/j.ejca.2020.12.030.
- Michael Erlichster, Justin Bedo, Efstratios Skafidas, Patrick Kwan, Adam Kowalczyk, and Benjamin Goudey. “Improved HLA-Based Prediction of Coeliac Disease Identifies Two Novel HLA Risk Modifiers, DQ6.2 and DQ7.3” (March 5, 2019). doi:10.1101/561308.
- Justin Bedő, Leon Di Stefano, and Anthony T Papenfuss. “Unifying Package Managers, Workflow Engines, and Containers: Computational Reproducibility with BioNix.” GigaScience 9, no. 11 (November 2020). doi:10.1093/gigascience/giaa121.
- Arcadi Cipponi, David L. Goode, Justin Bedo, Mark J. McCabe, Marina Pajic, David R. Croucher, Alvaro Gonzalez Rajal, et al. “MTOR Signaling Orchestrates Stress-Induced Mutagenesis, Facilitating Adaptive Evolution in Cancer.” Science 368, no. 6495 (June 4, 2020): 1127–1131. doi:10.1126/science.aau8768.
- Justin Bedő. “BioShake: a Haskell EDSL for Bioinformatics Workflows.” PeerJ 7 (July 9, 2019): e7223. doi:10.7717/peerj.7223.
- Lyu, Lingjuan, Karthik Nandakumar, Ben Rubinstein, Jiong Jin, Justin Bedo, and Marimuthu Palaniswami. “PPFA: Privacy Preserving Fog-Enabled Aggregation in Smart Grid.” IEEE Transactions on Industrial Informatics 14, no. 8 (August 2018): 3733–3744. doi:10.1109/tii.2018.2803782.
- Justin Bedo, Benjamin Goudey, Jeremy Wazny, and Zeyu Zhou. “Information Theoretic Alignment Free Variant Calling.” PeerJ Computer Science 2 (July 25, 2016): e71. doi:10.7717/peerj-cs.71.
ORCID iD: 0000-0001-5704-0212
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