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Epfl edcb ph.d. candidate presentation

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Here are Jérémie Kalfon's presentation slide for his EPFL's PhD application www.jkobject.com

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Epfl edcb ph.d. candidate presentation

  1. 1. EPFL/EDCB Ph.D. Candidate Presentation Jérémie KALFON, ECE paris, University of Kent jkobject.com, linkedin.com/jkobject, github.com/jkobject, @jkobject
  2. 2. CaImAn: Calcium Imaging Analysis 1. A Giovannucci, J Friedrich, P Gunn, J Kalfon, et. al. “CaImAn: An open source tool for scalable Calcium Imaging data Analysis”, eLife
  3. 3. CaImAn: Calcium Imaging Analysis 1. A Giovannucci, J Friedrich, P Gunn, J Kalfon, et. al. “CaImAn: An open source tool for scalable Calcium Imaging data Analysis”, eLife
  4. 4. PyCUB: Hidden Patterns of the Codon Usage Bias 1. J Kalfon, “PyCUB: A machine exploration of the Codon Usage Bias”, University of Kent. 2. Y Deng, J Kalfon, et. al., “Hidden pattersn of the Codon Usage Bias”, Nature Communication, in review ● GC content ● tRNA pool ● replication speed ● environment temperature ● nitrogen availability ● biased random mutations
  5. 5. PyCUB: Hidden Patterns of the Codon Usage Bias 1. J Kalfon, “PyCUB: A machine exploration of the Codon Usage Bias”, University of Kent. 2. Y Deng, J Kalfon, et. al., “Hidden pattersn of the Codon Usage Bias”, Nature Communication, in review ● frequency measures ● deviation-to-reference measures ● entropy measures
  6. 6. PyCUB: Methods ● 500 species from ensembl, python pipeline, scikit learn... ● Vector comparison ● Preprocessing (wide range of measures ~20)
  7. 7. PyCUB: Methods ● Entropy → Force driving the CUB ● DBscan to cluster with outliers ● t-SNE & PCA to represent the data ● modelisation of the process
  8. 8. Results ❖ Specific distribution by species groups ❖ Importance sequence’s age ❖ Correlation to sequence’s position. ❖ multiplicity of latent factors ❖ Most Species have specific CUBs 1. J Kalfon, “PyCUB: A machine exploration of the Codon Usage Bias”, University of Kent. 2. Y Deng, J Kalfon, et. al., “Hidden pattersn of the Codon Usage Bias”, Nature Communication, in review
  9. 9. Results ❖ Consistent results ❖ A python package to analyse the CUB across species ❖ A new measure of the CUB with a fast computation time. 1. J Kalfon, “PyCUB: A machine exploration of the Codon Usage Bias”, University of Kent. 2. Y Deng, J Kalfon, et. al., “Hidden pattersn of the Codon Usage Bias”, Nature Communication, in review
  10. 10. Conclusion ❖ Not one determinant of the CUB ❖ The entropy measure is a suitable one ❖ There is specific distribution across genes (SLS). Future research and ideas: Using more big data specific approach to analyze other/richer kingdoms. Remarks: ➔ The data was displaying a lot of improbable sequences, homologies, etc… ➔ t-SNE allowed to see clearly driving mechanisms 1. J Kalfon, “PyCUB: A machine exploration of the Codon Usage Bias”, University of Kent. 2. Y Deng, J Kalfon, et. al., “Hidden pattersn of the Codon Usage Bias”, Nature Communication, in review
  11. 11. The things I loved ● Machine Learning / Data Science ● genomics / multi-omics & visual data ● understand and model how cells work. ● translational applications in biomedicine ● working with teams, freedom to explore and create
  12. 12. Computer Science + Biology = <3 1. see: statement of research objectives (jkobject.com) 2. VCF2ancestry, github/jkobject Thank you! goals > topics 🎉 reproducible research

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