Computational methods for discovering gene networks from expression data

Lee, Wei-Po; Tzou, Wen-Shyong

doi:10.1093/bib/bbp028

Cited by 163 publications

(163 citation statements)

References 83 publications

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“…Approaches using mathematical modelling, ranging from qualitative to quantitative, have been applied to discovery of GRNs from gene expression data [10]. However, the size of GRNs and the nature of the data (high dimensional, noisy, insufficient for analysis of dynamics), limit robustness when mimicking natural behaviour.…”

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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Section: Introductionmentioning

confidence: 99%

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

2014

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“…For instance, microarray experiments have yielded large amounts of genome-wide expression information under various conditions or in different tissues for several model species. Expression compendia grouping multiple microarray experiments make it possible to define correlated expression patterns between genes (Eisen et al, 1998;Lee and Tzou, 2009). Genes within a coexpression cluster are expected to have more similar functionality than those without expression similarity.…”

mentioning

confidence: 99%

Comparative Network Analysis Reveals That Tissue Specificity and Gene Function Are Important Factors Influencing the Mode of Expression Evolution in Arabidopsis and Rice

2011

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Microarray experiments have yielded massive amounts of expression information measured under various conditions for the model species Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa). Expression compendia grouping multiple experiments make it possible to define correlated gene expression patterns within one species and to study how expression has evolved between species. We developed a robust framework to measure expression context conservation (ECC) and found, by analyzing 4,630 pairs of orthologous Arabidopsis and rice genes, that 77% showed conserved coexpression. Examples of nonconserved ECC categories suggested a link between regulatory evolution and environmental adaptations and included genes involved in signal transduction, response to different abiotic stresses, and hormone stimuli. To identify genomic features that influence expression evolution, we analyzed the relationship between ECC, tissue specificity, and protein evolution. Tissue-specific genes showed higher expression conservation compared with broadly expressed genes but were fast evolving at the protein level. No significant correlation was found between protein and expression evolution, implying that both modes of gene evolution are not strongly coupled in plants. By integration of cis-regulatory elements, many ECC conserved genes were significantly enriched for shared DNA motifs, hinting at the conservation of ancestral regulatory interactions in both model species. Surprisingly, for several tissue-specific genes, patterns of concerted network evolution were observed, unveiling conserved coexpression in the absence of conservation of tissue specificity. These findings demonstrate that orthologs inferred through sequence similarity in many cases do not share similar biological functions and highlight the importance of incorporating expression information when comparing genes across species.

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“…The RNNs have significant characteristics to make it computationally feasible (e.g. resistance to noise and non-linearity [67]) in analyzing RNA-Seq data in combination with other clustering approaches [68].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Systems Genetics of Complex Diseases Using RNA-Sequencing Methods

Mazzoni¹,

Kogelman²,

Suravajhala³

et al. 2015

IJBBB

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Next generation sequencing technologies have enabled the generation of huge quantities of biological data, and nowadays extensive datasets at different 'omics levels have been generated. Systems genetics is a powerful approach that allows to integrate different 'omics level and understand the biological mechanisms behind complex diseases or traits. In the recent, transcriptomic studies with microarrays have been replaced with the new powerful RNA-seq technologies. This has led to detection of novel gene transcripts, novel regulatory mechanisms, allele specific gene expression and numerous non-coding RNAs (ncRNAs). The integration of transcriptomics data with genomic data in a systems genetics context represents a valuable possibility to go deep into the causal and regulatory mechanisms that generate complex traits and diseases. However RNA-Seq data have to be treated carefully and the choice of the right methodology could have a great impact on the final results. Furthermore the integration of different level is not trivial. Here we give a comprehensive systems genetics overview of the methods and tools for analysis and the integration of RNA-Seq data including ncRNAs. We focused principally on merits and demerits of tools for post mapping quality control, normalization, differential expression analysis, gene network analysis, and integration of different omics data in order to generate a comprehensive guideline to systems genetics analysis using RNA-Seq data.

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Computational methods for discovering gene networks from expression data

Cited by 163 publications

References 83 publications

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

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

Comparative Network Analysis Reveals That Tissue Specificity and Gene Function Are Important Factors Influencing the Mode of Expression Evolution in Arabidopsis and Rice

Systems Genetics of Complex Diseases Using RNA-Sequencing Methods

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