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FunCoup 3.0: database of genome-wide functional coupling networks
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).
2014 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 42, no D1, D380-D388 p.Article in journal (Refereed) Published
Abstract [en]

We present an update of the FunCoup database (http://FunCoup.sbc.su.se) of functional couplings, or functional associations, between genes and gene products. Identifying these functional couplings is an important step in the understanding of higher level mechanisms performed by complex cellular processes. FunCoup distinguishes between four classes of couplings: participation in the same signaling cascade, participation in the same metabolic process, co-membership in a protein complex and physical interaction. For each of these four classes, several types of experimental and statistical evidence are combined by Bayesian integration to predict genome-wide functional coupling networks. The FunCoup framework has been completely re-implemented to allow for more frequent future updates. It contains many improvements, such as a regularization procedure to automatically downweight redundant evidences and a novel method to incorporate phylogenetic profile similarity. Several datasets have been updated and new data have been added in FunCoup 3.0. Furthermore, we have developed a new Web site, which provides powerful tools to explore the predicted networks and to retrieve detailed information about the data underlying each prediction.

Place, publisher, year, edition, pages
2014. Vol. 42, no D1, D380-D388 p.
National Category
Biochemistry and Molecular Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-102096DOI: 10.1093/nar/gkt984ISI: 000331139800057OAI: oai:DiVA.org:su-102096DiVA: diva2:707948
Funder
Swedish Research Council
Note

AuthorCount:3;

Available from: 2014-03-26 Created: 2014-03-26 Last updated: 2017-09-06Bibliographically approved
In thesis
1. 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
2. Global functional association network inference and crosstalk analysis for pathway annotation
Open this publication in new window or tab >>Global functional association network inference and crosstalk analysis for pathway annotation
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Cell functions are steered by complex interactions of gene products, like forming a temporary or stable complex, altering gene expression or catalyzing a reaction. Mapping these interactions is the key in understanding biological processes and therefore is the focus of numerous experiments and studies. Small-scale experiments deliver high quality data but lack coverage whereas high-throughput techniques cover thousands of interactions but can be error-prone. Unfortunately all of these approaches can only focus on one type of interaction at the time. This makes experimental mapping of the genome-wide network a cost and time intensive procedure. However, to overcome these problems, different computational approaches have been suggested that integrate multiple data sets and/or different evidence types. This widens the stringent definition of an interaction and introduces a more general term - functional association. 

FunCoup is a database for genome-wide functional association networks of Homo sapiens and 16 model organisms. FunCoup distinguishes between five different functional associations: co-membership in a protein complex, physical interaction, participation in the same signaling cascade, participation in the same metabolic process and for prokaryotic species, co-occurrence in the same operon. For each class, FunCoup applies naive Bayesian integration of ten different evidence types of data, to predict novel interactions. It further uses orthologs to transfer interaction evidence between species. This considerably increases coverage, and allows inference of comprehensive networks even for not well studied organisms. 

BinoX is a novel method for pathway analysis and determining the relation between gene sets, using functional association networks. Traditionally, pathway annotation has been done using gene overlap only, but these methods only get a small part of the whole picture. Placing the gene sets in context of a network provides additional evidence for pathway analysis, revealing a global picture based on the whole genome.

PathwAX is a web server based on the BinoX algorithm. A user can input a gene set and get online network crosstalk based pathway annotation. PathwAX uses the FunCoup networks and 280 pre-defined pathways. Most runs take just a few seconds and the results are summarized in an interactive chart the user can manipulate to gain further insights of the gene set's pathway associations.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2017
Keyword
biological networks, genome wide functional association networks, global gene association networks, gene networks, protein networks, functional association, functional coupling, network biology pathway analysis, pathway annotation, pathway enrichment, network-based enrichment, enrichment
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-146703 (URN)978-91-7649-950-4 (ISBN)978-91-7649-951-1 (ISBN)
Public defence
2017-10-20, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stochkolm, 13:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 2: Manuscript.

Available from: 2017-09-27 Created: 2017-09-06 Last updated: 2017-09-20Bibliographically approved

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