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Justin Bedő

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.

=> Gemlog

Projects

=> Git repositories | GitHub

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

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.

=> Git repository

svaRetro & svaNUMT

svaRetro and svaNUMT are R packages for detecting retrotransposed transcripts and mitocondrial insertions into the nuclear genome (NUMT) from structural variant calls.

=> svaRetro | svaNUMT

Recent publications

=> Google Scholar

• 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.

Contact

=> GPG key | Matrix (@jb:vk3.wtf) | ORCID iD: 0000-0001-5704-0212

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