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projects [2021/08/25 12:10] – mikaelprojects [2021/09/20 17:03] (current) – mikael
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 ==== Research projects with the Boden group ==== ==== Research projects with the Boden group ====
 +
 +Informal collection of project ideas, at different levels and duration.
  
 === Phylogenetics, ancestor sequence reconstruction, protein engineering, bioeconomy === === Phylogenetics, ancestor sequence reconstruction, protein engineering, bioeconomy ===
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 == Problem: What are the evolutionary drivers of metallo beta-lactamases? == == Problem: What are the evolutionary drivers of metallo beta-lactamases? ==
-  * Approach: Reconstruction of the enzyme super-family incorporating MBLs and analysis of evolutionary determinants relevant to their antibiotic resistance+  * Approach: Reconstruction of the enzyme super-family incorporating MBLs and analysis of evolutionary determinants relevant to establishing resistance to antibiotics 
   * Contacts: m.boden@uq.edu.au   * Contacts: m.boden@uq.edu.au
   * Collaborators: Hugenholtz, Schenk, Soo, Schofield   * Collaborators: Hugenholtz, Schenk, Soo, Schofield
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 == Problem: What ancestors in a phylogeny recapitulate a given mix of experimental properties? == == Problem: What ancestors in a phylogeny recapitulate a given mix of experimental properties? ==
   * Approach: Smart interrogation of experimental databases, visualisation of phylogeny juxtaposed with available data, and novel use of evolutionary models form part of our group's broader agenda, to be incorporated in a toolkit for ancestor reconstruction.   * Approach: Smart interrogation of experimental databases, visualisation of phylogeny juxtaposed with available data, and novel use of evolutionary models form part of our group's broader agenda, to be incorporated in a toolkit for ancestor reconstruction.
-  * Contacts: Gabe Foley g.foley@uq.edu.au, Sam Davis sam.davis@uq.edu.au+  * Contacts: Gabe Foley g.foley@uq.edu.au, Sam Davis sam.davis@uq.edu.au, m.boden@uq.edu.au
  
  
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 == Problem: Assessing reproducibility of ChIP-seq does not address intra-experimental bias == == Problem: Assessing reproducibility of ChIP-seq does not address intra-experimental bias ==
  
-  * Approach: The group previously developed ChIP-R to evaluate reproducibility of ChIP-seq replicates, by adapting the rank-product test, applied to each site independently; this approach does not consider the within-experiment dependence, exemplified by a quality metric (or even a profile) to each replicate. This project should investigate the basis for a model, and evaluate an implementation.+  * Approach: The group previously developed ChIP-R to evaluate reproducibility of ChIP-seq replicates, by adapting the rank-product test, applied to each site independently; this approach does not consider the within-experiment dependence, exemplified by a quality metric (or even a profile) for each replicate. This project should investigate the basis for a *model* (of each experimental replicate), and evaluate an implementation.
   * Contact: m.boden@uq.edu.au   * Contact: m.boden@uq.edu.au
  
-== Problem: Interrogation of single-cell ATAC-seq and integration with single-cell RNA-seq ==+== Problem: de-convoluting bulk data as a mixture of cell types: a case of using variance in replicates? ==
  
-  * Approach: Regulatory markers are present but look different in ATAC- and RNA-seq. We expect the mapping of representative cells in each data type could be based on such information, but this needs to be explored via the use of appropriate models that accommodate the uncertainty of inter-set labelling.+  * Approach: multiple biological replicates may represent different mixtures of cell types, hence variance across replicates may reveal what signals that originate from one type and not the others. Evaluate if (for instance) latent, cell type-mixture models could find coefficients that would allow de-mixing, i.e. separating the source behind each signal? This would be particularly helpful on ChIP-seq data, which is currently not at single-cell resolution, but similar ATAC-seq data can be used to benchmark by using matched single-cell data.
   * Contact: m.boden@uq.edu.au   * Contact: m.boden@uq.edu.au
 +
 +== Problem: how can we link cell states identified in one single-cell RNA-seq experiment to another? ==
 +
 +  * Approach: identify a universal representation of cell states that recapitulate statistical properties in gene expression, say by data matrix decomposition or similar with machine learning
 +  * Contact: a.mora@uq.edu.au, b.balderson@uq.edu.au, m.boden@uq.edu.au
 +  * Collaborator: Thor
 +
 +== Problem: Interrogation of single-cell ATAC-seq and integration with single-cell RNA-seq ==
 +
 +  * Approach: Regulatory markers are present but look different in ATAC- and RNA-seq. We expect the mapping of representative cells in each data type could be based on regulatory footprints; this needs to be explored via the use of appropriate models that accommodate the uncertainty of inter-set labelling.
 +  * Contact: m.boden@uq.edu.au, a.mora@uq.edu.au
   * Collaborator: Palpant   * Collaborator: Palpant
  
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 Increasingly genome technologies uncover spatial and temporal specificity of observations, but data need to be carefully and selectively pieced together. We work on integrating genomic, transcriptomic and epigenomic data, viewed together with information that can be predicted from sequence and other genomic markers, accounting for the uncertainty of their juxtaposition. With collaborators we have been using data integration to infer drivers in development (of the brain and other organs) as well as in cancer and disease. Increasingly genome technologies uncover spatial and temporal specificity of observations, but data need to be carefully and selectively pieced together. We work on integrating genomic, transcriptomic and epigenomic data, viewed together with information that can be predicted from sequence and other genomic markers, accounting for the uncertainty of their juxtaposition. With collaborators we have been using data integration to infer drivers in development (of the brain and other organs) as well as in cancer and disease.
  
-== Problem: What is the nature of DNA methylation and its role in cancer? ==+== Problem: To what extent is epigenetic regulation conserved, and how can evolution help interpret and explain epigenetic regulation? == 
 + 
 +  * Approach: The conservation of histone-modifying enzymes is broadly appreciated to exist in many higher species, and is especially relevant to the development of organs. The plan is to probe and model variance //within// species, trace conservation and model evolution //across// species via (on the one hand) positioning of epigenetic marks (at key developmental stages) and sequence and expression of epigenetic components. 
 +  * Contact: m.boden@uq.edu.au 
 +  * Collaborator: Thor 
 + 
 +== Problem: How can we leverage trends of epigenetic marks to highlight regulatory drivers in sparse single-cell and spatial transcriptomes? == 
 + 
 +  * Approach: Evaluate how key histone modifications (collected by ENCODE, say) act coordinately with gene expression; model such coordination with view of predicting epigenetic regulation 
 +  * Contact: m.boden@uq.edu.au 
 + 
 +== Problem: What is the quantitative nature of DNA methylation and its role in cancer? == 
   * Approach: Data aggregation and careful integration of massive public data sets give rise to new hypotheses.   * Approach: Data aggregation and careful integration of massive public data sets give rise to new hypotheses.
-  * Contact: Ariane Mora a.mora@uq.edu.au+  * Contact: Ariane Mora a.mora@uq.edu.au, m.boden@uq.edu.au
  
  
  
  
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  • by mikael