Detailing regulatory networks through large scale data integration

Huttenhower, Curtis; Mutungu, K. Tsheko; Indik, Natasha; Yang, Woongcheol; Schroeder, Mark; Forman, Joshua J.; Troyanskaya, Olga G.; Coller, Hilary A.

doi:10.1093/bioinformatics/btp588

Cited by 80 publications

(66 citation statements)

References 40 publications

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“…Leveraging independent, functional knowledge to establish priors should be straightforward, based on odds ratios of the external data in validated trait-associated SNPs, but remains a key challenge for the development of Bayesian GWAS methods because of their heterogeneity and potential bias. While exciting approaches for combining heterogeneous data are being developed (Lage et al 2008;Huttenhower et al 2009;Lee et al 2009;Battle et al 2010), these issues must be taken into consideration in the design and interpretation of truly integrative systems genetics analyses.…”

Section: The Promise Of Gwas-systems Genetics Of Complex Traitsmentioning

confidence: 99%

Progress and Promise of Genome-Wide Association Studies for Human Complex Trait Genetics

2011

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Enormous progress in mapping complex traits in humans has been made in the last 5 yr. There has been early success for prevalent diseases with complex phenotypes. These studies have demonstrated clearly that, while complex traits differ in their underlying genetic architectures, for many common disorders the predominant pattern is that of many loci, individually with small effects on phenotype. For some traits, loci of large effect have been identified. For almost all complex traits studied in humans, the sum of the identified genetic effects comprises only a portion, generally less than half, of the estimated trait heritability. A variety of hypotheses have been proposed to explain why this might be the case, including untested rare variants, and gene-gene and gene-environment interaction. Effort is currently being directed toward implementation of novel analytic approaches and testing rare variants for association with complex traits using imputed variants from the publicly available 1000 Genomes Project resequencing data and from direct resequencing of clinical samples. Through integration with annotations and functional genomic data as well as by in vitro and in vivo experimentation, mapping studies continue to characterize functional variants associated with complex traits and address fundamental issues such as epistasis and pleiotropy. This review focuses primarily on the ways in which genome-wide association studies (GWASs) have revolutionized the field of human quantitative genetics.M ANY phenotypes are quantitative in nature, and complex in etiology, with multiple environmental and genetic causes. The observation that complex traits cluster in relation to genetic relatedness suggests heritability, and advances in theoretical and experimental genetics, combined with analytical developments and high-throughput genomics, have provided an unprecedented view into the mode of inheritance and genetic architecture of complex traits.

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Section: The Promise Of Gwas-systems Genetics Of Complex Traitsmentioning

confidence: 99%

Progress and Promise of Genome-Wide Association Studies for Human Complex Trait Genetics

2011

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“…Given the challenges posed by available gene expression data and poor model robustness to date, a new direction is integration of several data types, [16], and these reports have started to appear, mostly for coarse-grained analysis [8]. These integrate expression data with other types of measurements, such as binding affinities or protein interactions, to better discriminate between candidate models, but usually are limited, (i.e.…”

Section: Introductionmentioning

confidence: 99%

EGIA – Evolutionary Optimisation of Gene Regulatory Networks, an Integrative Approach

2014

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Quantitative modelling of gene regulatory networks (GRNs) is still limited by data issues such as noise and the restricted length of available time series, creating an under-determination problem. However, large amounts of other types of biological data and knowledge are available, such as knockout experiments, annotations and so on, and it has been postulated that integration of these can improve model quality. However, integration has not been fully explored to date. Here, we present a novel integrative framework for different types of data that aims to enhance model inference. This is based on evolutionary computation and uses different types of knowledge to introduce a novel customised initialisation and mutation operator and complex evaluation criteria, used to distinguish between candidate models. Specifically, the algorithm uses information from (i) knockout experiments, (ii) annotations of transcription factors, (iii) binding site motifs (expressed as position weight matrices) and (iv) DNA sequence of gene promoters, to drive the algorithm towards more plausible network structures. Further, the evaluation basis is also extended to include structure information included in these additional data. This framework is applied to both synthetic and real gene expression data. Models obtained by data integration display both quantitative and qualitative improvement.

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“…Data Fusion by Matrix Factorization matrix factorization (Tang et al, 2009), a mixture of Markov chains associated with di↵erent graphs (Zhou & Burges, 2007), dependency-seeking clustering algorithms with variational Bayes (Klami & Kaski, 2008), latent factor analysis (Lopes et al, 2011;Luttinen & Ilin, 2009), nonparametric Bayes ensemble learning (Xing & Dunson, 2011), approaches based on Bayesian theory (Zhang & Ji, 2006;Alexeyenko & Sonnhammer, 2009;Huttenhower et al, 2009), neural networks (Carpenter et al, 2005) and module guided random forests (Chen & Zhang, 2013). These approaches either fuse input data (early integration) or predictions (late integration) and do not directly combine heterogeneous representation of objects of di↵er-ent types.…”

Section: Background and Related Workmentioning

confidence: 99%

Data Fusion by Matrix Factorization

Žitnik

Zupan

2015

IEEE Trans. Pattern Anal. Mach. Intell.

195

212

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For most problems in science and engineering we can obtain data that describe the system from various perspectives and record the behaviour of its individual components. Heterogeneous data sources can be collectively mined by data fusion. Fusion can focus on a specific target relation and exploit directly associated data together with data on the context or additional constraints. In the paper we describe a data fusion approach with penalized matrix tri-factorization that simultaneously factorizes data matrices to reveal hidden associations. The approach can directly consider any data sets that can be expressed in a matrix, including those from attribute-based representations, ontologies, associations and networks. We demonstrate its utility on a gene function prediction problem in a case study with eleven di↵erent data sources. Our fusion algorithm compares favourably to state-of-the-art multiple kernel learning and achieves higher accuracy than can be obtained from any single data source alone.

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Detailing regulatory networks through large scale data integration

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 80 publications

References 40 publications

Progress and Promise of Genome-Wide Association Studies for Human Complex Trait Genetics

Progress and Promise of Genome-Wide Association Studies for Human Complex Trait Genetics

EGIA – Evolutionary Optimisation of Gene Regulatory Networks, an Integrative Approach

Data Fusion by Matrix Factorization

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