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Comparative interactomics with Funcoup 2.0
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
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2012 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 40, no D1, D821-D828 p.Article in journal (Refereed) Published
Abstract [en]

FunCoup (http://FunCoup.sbc.su.se) is a database that maintains and visualizes global gene/protein networks of functional coupling that have been constructed by Bayesian integration of diverse high-throughput data. FunCoup achieves high coverage by orthology-based integration of data sources from different model organisms and from different platforms. We here present release 2.0 in which the data sources have been updated and the methodology has been refined. It contains a new data type Genetic Interaction, and three new species: chicken, dog and zebra fish. As FunCoup extensively transfers functional coupling information between species, the new input datasets have considerably improved both coverage and quality of the networks. The number of high-confidence network links has increased dramatically. For instance, the human network has more than eight times as many links above confidence 0.5 as the previous release. FunCoup provides facilities for analysing the conservation of subnetworks in multiple species. We here explain how to do comparative interactomics on the FunCoup website.

Place, publisher, year, edition, pages
2012. Vol. 40, no D1, D821-D828 p.
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
URN: urn:nbn:se:su:diva-76759DOI: 10.1093/nar/gkr1062ISI: 000298601300123OAI: oai:DiVA.org:su-76759DiVA: diva2:527142
Note

AuthorCount; 6

Available from: 2013-04-11 Created: 2012-05-16 Last updated: 2017-09-29Bibliographically approved
In thesis
1. Network and gene expression analyses for understanding protein function
Open this publication in new window or tab >>Network and gene expression analyses for understanding protein function
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2013. 86 p.
Keyword
biological networks, network inference, network analysis, clustering, network module, network crosstalk, expression analysis, gene signature, biomarker
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
urn:nbn:se:su:diva-89055 (URN)978-91-7447-674-3 (ISBN)
Public defence
2013-05-23, Nordenskiöldsalen, Geovetenskapens hus, Svante Arrhenius väg 12, Stockholm, 09:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 5: Accepted.

 

Available from: 2013-05-01 Created: 2013-04-10 Last updated: 2013-04-22Bibliographically approved
2. Inference of functional association networks and gene orthology
Open this publication in new window or tab >>Inference of functional association networks and gene orthology
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Most proteomics and genomics experiments are performed on a small set of well-studied model organisms and their results are generalized to other species. This is possible because all species are evolutionarily related. When transferring information across species, orthologs are the most likely candidates for functional equivalence. The InParanoid algorithm, which predicts orthology relations by sequence similarity based clustering, was improved by increasing its robustness for low complexity sequences and the corresponding database was updated to include more species.

A plethora of different orthology inference methods exist, each featuring different formats. We have addressed the great need for standardization this creates with the development of SeqXML and OrthoXML, two formats that standardize the input and output of ortholog inference.

Essentially all biological processes are the result of a complex interplay between different biomolecules. To fully understand the function of genes or gene products one needs to identify these relations. Integration of different types of high-throughput data allows the construction of genome-wide functional association networks that give a global picture of the relation landscape.

FunCoup is a framework that performs this integration to create functional association networks for 11 model organisms. Orthology assignments from InParanoid are used to transfer high-throughput data between species, which contributes with more than 50% to the total functional association evidence. We have developed procedures to incorporate new evidence types, improved the procedures of existing evidence types, created networks for additional species, and added significantly more data. Furthermore, the integration procedure was improved to account for data redundancy and to increase its overall robustness. Many of these changes were possible because the computational framework was re-implemented from scratch.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2013. 83 p.
Keyword
orthology, InParanoid, FunCoup, systems biology, biological networks, network inference, functional coupling, functional association
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
urn:nbn:se:su:diva-92682 (URN)978-91-7447-740-5 (ISBN)
Public defence
2013-10-04, Nordenskiöldsalen, Geovetenskapens hus, Svante Arrhenius väg 12, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Submitted.

Available from: 2013-09-12 Created: 2013-08-14 Last updated: 2017-08-25Bibliographically approved
3. Functional association networks for disease gene prediction
Open this publication in new window or tab >>Functional association networks for disease gene prediction
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Mapping of the human genome has been instrumental in understanding diseasescaused by changes in single genes. However, disease mechanisms involvingmultiple genes have proven to be much more elusive. Their complexityemerges from interactions of intracellular molecules and makes them immuneto the traditional reductionist approach. Only by modelling this complexinteraction pattern using networks is it possible to understand the emergentproperties that give rise to diseases.The overarching term used to describe both physical and indirect interactionsinvolved in the same functions is functional association. FunCoup is oneof the most comprehensive networks of functional association. It uses a naïveBayesian approach to integrate high-throughput experimental evidence of intracellularinteractions in humans and multiple model organisms. In the firstupdate, both the coverage and the quality of the interactions, were increasedand a feature for comparing interactions across species was added. The latestupdate involved a complete overhaul of all data sources, including a refinementof the training data and addition of new class and sources of interactionsas well as six new species.Disease-specific changes in genes can be identified using high-throughputgenome-wide studies of patients and healthy individuals. To understand theunderlying mechanisms that produce these changes, they can be mapped tocollections of genes with known functions, such as pathways. BinoX wasdeveloped to map altered genes to pathways using the topology of FunCoup.This approach combined with a new random model for comparison enables BinoXto outperform traditional gene-overlap-based methods and other networkbasedtechniques.Results from high-throughput experiments are challenged by noise and biases,resulting in many false positives. Statistical attempts to correct for thesechallenges have led to a reduction in coverage. Both limitations can be remediedusing prioritisation tools such as MaxLink, which ranks genes using guiltby association in the context of a functional association network. MaxLink’salgorithm was generalised to work with any disease phenotype and its statisticalfoundation was strengthened. MaxLink’s predictions were validatedexperimentally using FRET.The availability of prioritisation tools without an appropriate way to comparethem makes it difficult to select the correct tool for a problem domain.A benchmark to assess performance of prioritisation tools in terms of theirability to generalise to new data was developed. FunCoup was used for prioritisationwhile testing was done using cross-validation of terms derived fromGene Ontology. This resulted in a robust and unbiased benchmark for evaluationof current and future prioritisation tools. Surprisingly, previously superiortools based on global network structure were shown to be inferior to a localnetwork-based tool when performance was analysed on the most relevant partof the output, i.e. the top ranked genes.This thesis demonstrates how a network that models the intricate biologyof the cell can contribute with valuable insights for researchers that study diseaseswith complex genetic origins. The developed tools will help the researchcommunity to understand the underlying causes of such diseases and discovernew treatment targets. The robust way to benchmark such tools will help researchersto select the proper tool for their problem domain.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2017. 64 p.
Keyword
network biology, biological networks, network prediction, functional association, functional coupling, network integration, functional association networks, genome-wide association networks, gene networks, protein networks, fret, functional enrichment analysis, network cross-talk, pathway annotation, gene prioritisation, network-based gene prioritization, benchmarking
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-147217 (URN)978-91-7649-976-4 (ISBN)978-91-7649-977-1 (ISBN)
Public defence
2017-11-10, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 14:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 5: Manuscript. Paper 6: Manuscript.

Available from: 2017-10-18 Created: 2017-09-29 Last updated: 2017-10-16Bibliographically approved

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