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A novel method for crosstalk analysis of biological networks: improving accuracy of pathway annotation
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).
Number of Authors: 42017 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 45, no 2, article id e8Article in journal (Refereed) Published
Abstract [en]

Analyzing gene expression patterns is a mainstay to gain functional insights of biological systems. A plethora of tools exist to identify significant enrichment of pathways for a set of differentially expressed genes. Most tools analyze gene overlap between gene sets and are therefore severely hampered by the current state of pathway annotation, yet at the same time they run a high risk of false assignments. A way to improve both true positive and false positive rates (FPRs) is to use a functional association network and instead look for enrichment of network connections between gene sets. We present a new network crosstalk analysis method BinoX that determines the statistical significance of network link enrichment or depletion between gene sets, using the binomial distribution. This is a much more appropriate statistical model than previous methods have employed, and as a result BinoX yields substantially better true positive and FPRs than was possible before. A number of benchmarks were performed to assess the accuracy of BinoX and competing methods. We demonstrate examples of how BinoX finds many biologically meaningful pathway annotations for gene sets from cancer and other diseases, which are not found by other methods.

Place, publisher, year, edition, pages
2017. Vol. 45, no 2, article id e8
National Category
Biological Sciences
Research subject
Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-141250DOI: 10.1093/nar/gkw849ISI: 000396576300003PubMedID: 27664219OAI: oai:DiVA.org:su-141250DiVA, id: diva2:1088469
Available from: 2017-04-12 Created: 2017-04-12 Last updated: 2017-09-29Bibliographically approved
In thesis
1. 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
Keywords
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: 2018-04-27Bibliographically approved
2. 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. p. 64
Keywords
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: 2018-04-27Bibliographically approved

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