| 1. |
- Guala, Dimitri, 1979-
(författare)
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Functional association networks for disease gene prediction
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 2. |
- Kaduk, Mateusz, 1985-
(författare)
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Functional Inference from Orthology and Domain Architecture
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Proteins are the basic building blocks of all living organisms. They play a central role in determining the structure of living beings and are required for essential chemical reactions. One of the main challenges in bioinformatics is to characterize the function of all proteins. The problem of understanding protein function can be approached by understanding their evolutionary history. Orthology analysis plays an important role in studying the evolutionary relation of proteins. Proteins are termed orthologs if they derive from a single gene in the species' last common ancestor, i.e. if they were separated by a speciation event. Orthologs are useful because they retain their function more often than other homologs. Inference of a complete set of orthologs for many species is computationally intensive. Currently, the fastest algorithms rely on graph-based approaches, which compare all-vs-all sequences and then cluster top hits into groups of orthologs. The initial step of performing all-vs-all comparisons is usually the primary computational challenge as it scales quadratically with the number of species. A new, more scalable and less computationally demanding method was developed to solve this problem without sacrificing accuracy. The Hieranoid 2 algorithm reduces computational complexity to almost linear by overcoming the necessity to perform all-vs-all similarity searches. The algorithm progresses along a known species tree, from leaves to root. Starting at the leaves, ortholog groups are predicted conventionally and then summarized at internal nodes to form pseudo-species. These pseudo-species are then re-used to search against other (pseudo-)species higher in the tree. This way the algorithm aggregates new ortholog groups hierarchically. The hierarchy is a natural structure to store and view large multi-species ortholog groups, and provides a complete picture of inferred evolutionary events. To facilitate explorative analysis of hierarchical groups of orthologs, a new online tool was created. The HieranoiDB website provides precomputed hierarchical groups of orthologs for a set of 66 species. It allows the user to search for orthology assignments using protein description, protein sequence, or species. Evolutionary events and meta information is added to the hierarchical groups of orthologs, which are shown graphically as interactive trees. This representation allows exploring, searching, and easier visual inspection of multi-species ortholog groups.The majority of orthology prediction methods focus on treating the whole protein sequence as a single evolutionary unit. However, proteins are often composed of individual units, called protein domains, that can have different evolutionary histories. To extend the full sequence based methodology to a domain-aware method, a new approach called Domainoid is proposed. Here, domains are extracted from full-length sequences and subjected to orthology inference. This allows Domainoid to find orthology that would be missed by a full sequence approach.Networks are a convenient graphical representation for showing a large number of functional associations between genes or proteins. They allow various analyses of graph properties, and can help visualize complex relationships. A framework for inferring comprehensive functional association networks was developed, called FunCoup. A major difference compared to other networks is FunCoup's extensive use of orthology relationships between species, which significantly boosts its coverage. Using naïve Bayesian classifiers to integrate 10 different evidence types and orthology transfer, FunCoup captures functional associations of many types, and provides comprehensive networks for 17 species across five gold-standards.
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| 3. |
- Morgan, Daniel, 1988-
(författare)
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Towards Reliable Gene Regulatory Network Inference
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Phenotypic traits are now known to stem from the interplay between genetic variables across many if not every level of biology. The field of gene regulatory network (GRN) inference is concerned with understanding the regulatory interactions between genes in a cell, in order to build a model that captures the behaviour of the system. Perturbation biology, whereby genes or RNAs are targeted and their activity altered, is of great value for the GRN field. By first systematically perturbing the system and then reading the system's reaction as a whole, we can feed this data into various methods to reverse engineer the key agents of change.The initial study sets the groundwork for the rest, and deals with finding common ground among the sundry methods in order to compare and rank performance in an unbiased setting. The GeneSPIDER (GS) MATLAB package is an inference benchmarking platform whereby methods can be added via a wrapper for testing in competition with one another. Synthetic datasets and networks spanning a wide range of conditions can be created for this purpose. The evaluation of methods across various conditions in the benchmark therein demonstrates which properties influence the accuracy of which methods, and thus which are more suitable for use under given characterized condition.The second study introduces a novel framework NestBoot for increasing inference accuracy within the GS environment by independent, nested bootstraps, \ie repeated inference trials. Under low to medium noise levels, this allows support to be gathered for links occurring most often while spurious links are discarded through comparison to an estimated null distribution of shuffled-links. While noise continues to plague every method, nested bootstrapping in this way is shown to increase the accuracy of several different methods.The third study applies NestBoot on real data to infer a reliable GRN from an small interfering RNA (siRNA) perturbation dataset covering 40 genes known or suspected to have a role in human cancers. Methods were developed to benchmark the accuracy of an inferred GRN in the absence of a true known GRN, by assessing how well it fits the data compared to a null model of shuffled topologies. A network of high confidence was recovered containing many regulatory links known in the literature, as well as a slew of novel links.The fourth study seeks to infer reliable networks on large scale, utilizing the high dimensional biological datasets of the LINCS L1000 project. This dataset has too much noise for accurate GRN inference as a whole, hence we developed a method to select a subset that is sufficiently informative to accurately infer GRNs. This is a first step in the direction of identifying probable submodules within a greater genome-scale GRN yet to be uncovered.
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| 4. |
- Ogris, Christoph, 1985-
(författare)
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Global functional association network inference and crosstalk analysis for pathway annotation
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 5. |
- Tjärnberg, Andreas, 1981-
(författare)
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Exploring the Boundaries of Gene Regulatory Network Inference
- 2015
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- To understand how the components of a complex system like the biological cell interact and regulate each other, we need to collect data for how the components respond to system perturbations. Such data can then be used to solve the inverse problem of inferring a network that describes how the pieces influence each other. The work in this thesis deals with modelling the cell regulatory system, often represented as a network, with tools and concepts derived from systems biology. The first investigation focuses on network sparsity and algorithmic biases introduced by penalised network inference procedures. Many contemporary network inference methods rely on a sparsity parameter such as the L1 penalty term used in the LASSO. However, a poor choice of the sparsity parameter can give highly incorrect network estimates. In order to avoid such poor choices, we devised a method to optimise the sparsity parameter, which maximises the accuracy of the inferred network. We showed that it is effective on in silico data sets with a reasonable level of informativeness and demonstrated that accurate prediction of network sparsity is key to elucidate the correct network parameters. The second investigation focuses on how knowledge from association networks can be transferred to regulatory network inference procedures. It is common that the quality of expression data is inadequate for reliable gene regulatory network inference. Therefore, we constructed an algorithm to incorporate prior knowledge and demonstrated that it increases the accuracy of network inference when the quality of the data is low. The third investigation aimed to understand the influence of system and data properties on network inference accuracy. L1 regularisation methods commonly produce poor network estimates when the data used for inference is ill-conditioned, even when the signal to noise ratio is so high that all links in the network can be proven to exist for the given significance. In this study we elucidated some general principles for under what conditions we expect strongly degraded accuracy. Moreover, it allowed us to estimate expected accuracy from conditions of simulated data, which was used to predict the performance of inference algorithms on biological data. Finally, we built a software package GeneSPIDER for solving problems encountered during previous investigations. The software package supports highly controllable network and data generation as well as data analysis and exploration in the context of network inference.
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