Network analyses, such as gene co-expression networks are an important approach for the systems-level study of biological data. For example, understanding patterns of \linebreak co-regulation in mental disorders can contribute to the development of new therapies and treatments.
In a gene regulatory process a particular TF or ncRNA can up- or down-regulate other genes, therefore it is important to explicitly consider both positive and negative interactions.
Although exists a variety of software and libraries for constructing and investigating such networks, none considers the sign of interaction.
It is also required that the represented networks have high accuracy, where the interactions found have to be relevant and not found by chance or background noise.
Another issue derived from building co-expression networks is the reproducibility of those.
When constructing independent networks for the same phenotype, though, using different expression datasets, the output network can be remarkably distinct due to biological or technical noise in the data.
However, most of the times the interest is not only to characterise a network but to compare its features to others.
A series of questions arise from understanding phenotypes using co-expression networks: i) how to construct highly accurate networks; ii) how to combine multiple networks derived from different platforms; iii) how to compare multiple networks.
For answering those questions, i) I improved the wTO method to construct highly accurate networks, where now each interaction in a network receives a probability. This method showed to be much more efficient in finding correct interactions than other well-known methods; ii) I developed a method that is able to combine multiple networks into one building a CN. This method enables the correction for background noise; iii) I developed a completely novel method for the comparison of multiple co-expression networks, CoDiNA. This method identifies genes specific to at least one network.
It is natural that after associating genes to phenotypes, an inference whether those genes are enriched for a particular disorder is needed. I also present here a tool, RichR, that enables enrichment analysis and background correction.
I applied the methods proposed here in two important studies. In the first one, the aim was to understand the neurogenesis process and how certain genes would affect it. The combination of the methods shown here pointed one particular TF, ZN787, as playing an important role in this process.
Moreover, the application of this toolset to networks derived from brain samples of individuals with cognitive disorders identified genes and network connections that are specific to certain disorders, but also found an overlap between neurodegenerative disorders and brain development and between evolutionary changes and psychological disorders.
CoDiNA also pointed out that there are genes involved in those disorders that are not only human-specific.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:33743 |
| Date | 11 April 2019 |
| Creators | Morselli Gysi, Deisy |
| Contributors | Universität Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/acceptedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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