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projects [2021/08/26 21:20] – mikael | projects [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, | === Phylogenetics, | ||
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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' | * Approach: Smart interrogation of experimental databases, visualisation of phylogeny juxtaposed with available data, and novel use of evolutionary models form part of our group' | ||
- | * Contacts: Gabe Foley g.foley@uq.edu.au, | + | * Contacts: Gabe Foley g.foley@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; | + | * 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; |
+ | * Contact: m.boden@uq.edu.au | ||
+ | |||
+ | == Problem: de-convoluting bulk data as a mixture of cell types: a case of using variance in replicates? == | ||
+ | |||
+ | * 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 | ||
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* 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 | * 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, | + | * Contact: a.mora@uq.edu.au, |
* Collaborator: | * Collaborator: | ||
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Increasingly genome technologies uncover spatial and temporal specificity of observations, | Increasingly genome technologies uncover spatial and temporal specificity of observations, | ||
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+ | == 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: | ||
+ | |||
+ | == 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? == | == 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 |