| 1. |
- Frings, Oliver, 1982-
(author)
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Network and gene expression analyses for understanding protein function
- 2013
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Doctoral thesis (other academic/artistic)abstract
- Biological function is the result of a complex network of functional associations between genes or their products. Modeling the dynamics underlying biological networks is one of the big challenges in bioinformatics. A first step towards solving this problem is to predict and study the networks of functional associations underlying various conditions.An improved version of the FunCoup network inference method that features networks for three new species and updated versions of the existing networks is presented. Network clustering, i.e. partitioning networks into highly connected components is an important tool for network analysis. We developed MGclus, a clustering method for biological networks that scores shared network neighbors. We found MGclus to perform favorably compared to other methods popular in the field. Studying sets of experimentally derived genes in the context of biological networks is a common strategy to shed light on their underlying biology. The CrossTalkZ method presented in this work assesses the statistical significance of crosstalk enrichment, i.e. the extent of connectivity between or within groups of functionally coupled genes or proteins in biological networks. We further demonstrate that CrossTalkZ is a valuable method to functionally annotate experimentally derived gene sets.Males and females differ in the expression of an extensive number of genes. The methods developed in the first part of this work were applied to study sex-biased genes in chicken and several network properties related to the molecular mechanisms of sex-biased gene regulation in chicken were deduced. Cancer studies have shown that tumor progression is strongly determined by the tumor microenvironment. We derived a gene expression signature of PDGF-activated fibroblasts that shows a strong prognostic significance in breast cancer in univariate and multivariate survival analyses when compared to established markers for prognosis.
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| 2. |
- Östlund, Gabriel, 1980-
(author)
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Data integration for robust network-based disease gene prediction
- 2013
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Doctoral thesis (other academic/artistic)abstract
- For many complex diseases the cause/mechanism can be tied not to a single gene and in order to cope with the complexity a systems wide approach is needed. By combining evidence indicative of functional association it is possible to infer networks of protein functional coupling. The reliability of these networks is dependent on having sufficient data and on the data being informative.By combining evidence from multiple species, functional coupling networks can reach higher coverage and accuracy. Genes in different species derived from the same gene by a speciation event are orthologous and likely to have a conserved function. In order to enable the transfer of information across species we inferred orthology with the InParanoid algorithm and made the inferences available to the public in the associated database.Identification of genes involved in diseases is an important biomedical goal. Based on the "guilt by association" principle, we implemented an approach, Maxlink, for identifying and prioritizing novel disease genes. By searching the FunCoup network for genes functionally coupled to cancer genes we identified some 1800 novel cancer gene candidates showing characteristics of cancer genes.While proteins are the active components, mRNA is often used as a proxy due to the difficulty of measuring protein abundance. We examined the relationship between mRNA and protein, using properties of expression profiles to identify subsets of genes with higher mRNA-protein concordance.If technical and biological differences between patient/control studies of gene expression have a large impact, the results of studies of the same disease might be inconsistent. To determine this impact we examined the consistency in differential (co)expression between different studies of cancer, as well as non-cancer studies. Such consistency could generally be found, even between studies of different diseases, but only when common pitfalls of gene expression analysis are avoided.
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| 3. |
- Forslund, Kristoffer, 1980-
(author)
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The relationship between orthology, protein domain architecture and protein function
- 2011
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Doctoral thesis (other academic/artistic)abstract
- Lacking experimental data, protein function is often predicted from evolutionary and protein structure theory. Under the 'domain grammar' hypothesis the function of a protein follows from the domains it encodes. Under the 'orthology conjecture', orthologs, related through species formation, are expected to be more functionally similar than paralogs, which are homologs in the same or different species descended from a gene duplication event. However, these assumptions have not thus far been systematically evaluated. To test the 'domain grammar' hypothesis, we built models for predicting function from the domain combinations present in a protein, and demonstrated that multi-domain combinations imply functions that the individual domains do not. We also developed a novel gene-tree based method for reconstructing the evolutionary histories of domain architectures, to search for cases of architectures that have arisen multiple times in parallel, and found this to be more common than previously reported. To test the 'orthology conjecture', we first benchmarked methods for homology inference under the obfuscating influence of low-complexity regions, in order to improve the InParanoid orthology inference algorithm. InParanoid was then used to test the relative conservation of functionally relevant properties between orthologs and paralogs at various evolutionary distances, including intron positions, domain architectures, and Gene Ontology functional annotations. We found an increased conservation of domain architectures in orthologs relative to paralogs, in support of the 'orthology conjecture' and the 'domain grammar' hypotheses acting in tandem. However, equivalent analysis of Gene Ontology functional conservation yielded spurious results, which may be an artifact of species-specific annotation biases in functional annotation databases. I discuss possible ways of circumventing this bias so the 'orthology conjecture' can be tested more conclusively.
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| 4. |
- Messina, David N., 1974-
(author)
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Biological data exchange and the discovery of new protein families in metagenomic samples
- 2012
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Doctoral thesis (other academic/artistic)abstract
- The rise in sequence data has brought both challenges to the way we exchange biological information and opportunities to discover new protein families, primarily through the investigation of uncultured metagenomic samples.The Distributed Annotation System, or DAS, provided a means for exchanging protein sequence data, but there were no open source, stand-alone DAS clients optimized for integrating and viewing these data. To address this need, we developed DASher. Complementary to visualizing DAS data with DASher, we also created and made available ten servers to offer real-time protein feature predictions via DAS. While DAS works well for genomic data, there was no such framework for exchanging orthology data in a consistent way. Consequently, we developed the first standards for orthology data exchange, SeqXML and OrthoXML. 64 reference proteomes are now available in SeqXML, and 14 orthology providers have agreed to offer their predictions in OrthoXML. Besides creating a uniform representation of common data types, these standards enable direct comparison and assessment of competing methods for the first time.A substantial percentage of newly sequenced genes are ORFans, which have no match to previously known sequences. Metagenomics samples uncover sequences from uncultivable and therefore previously unseen species, and ORFans constitute much of the metagenomics data that are completely uncharacterized. ORFans are by definition impervious to standard similarity-based methods, and the few existing metagenomics gene-finding methods performed poorly on short, error-prone next-generation sequence data. Therefore, we designed a new approach to predict protein-coding gene families from metagenomic data and applied it to 17 virally-enriched metagenomes derived from human patients. Of the 456 putative ORFan families we found in the nearly 1 billion nucleotides sequenced from these libraries, we identified 32 putative novel protein families with strong support.
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