SYNERGY Project
Overview
Nuclear receptors (NRs) are key factors regulating fundamental cell fate decisions during organogenesis, growth, homeostatic tissue maintenance and renewal. Through influencing the expression of genes within complex regulatory networks, NRs affect a diverse spectrum of physiological and pathological processes, including differentiation, cellular homeostasis, cancer and metabolic diseases. Prime examples are estrogen-dependent breast cancer and androgen-dependent prostate cancer.
Transcription of NR-regulated genes is a complex, tightly regulated process where distinct NRs, in conjunction with other transcription factors (TFs), the basal transcription machinery and covalent modifications to chromatin, collectively act to regulate gene expression. The major objectives of SYNERGY (Systems approach to gene regulation biology through nuclear receptors) are to characterize the roles of four nuclear receptors (NRs), RNA polymerase II and four histone marks in tumor cells and in normal breast and prostate cells. We will determine NR binding through ChIP-seq; gene expression with RNA-seq and place these datasets in context with DNA methylation and histone marks at multiple time points. These measurements will provide unique temporal datasets that will be used to design and implement computational methods to (i) identify genes regulated by NRs, (ii) infer the mechanisms of NR-triggered gene regulation, and (iii) identify pathways, biological processes and gene regulatory networks that the NR-responsive genes are involved in.
SYNERGY is built upon interactive cycles between experimental (Henk Stunnenberg, Olli A. Jänne, George Reid) and modeling oriented (Sampsa Hautaniemi, Magnus Rattray, Neil Lawrence, Antti Honkela, Genomatix Ltd.) groups. The models will be extensively validated during the project, and the predictions emerging from the models will be used to direct experiments that lead to more comprehensive understanding of gene regulation. The project is sponsored by ERA-NET Project Ref ERASysBio and is a collaboration with Prof Magnus Rattray of University of Manchester, Dr Antti Honkela of University of Helsinki, Dr Jaako Peltonen of Aalto University, Prof Henk Stunnenberg of Nijmegen Centre for Molecular Life Sciences, Dr Sampsa Hautaniemi of University of Helsinki, Dr George Reid of DKFZ Heidelberg, Prof Olli A. Janne of University of Helsinki and Dr Martin Seifert of Genomatix Software.
Personnel from ML@SITraN
- Ciira Maina post-doctoral research assistant
Software
The following software has been made available either wholly or partly as a result of work on this project:
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GPy GPy: Gaussian process modelling framework in Python
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gpsim GPSIM: Gaussian Process Modelling of single input module motif networks.
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multigp MULTIGP: Modelling multiple outputs with Gaussian processes (will eventually supercede the gpsim toolbox).
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disimrank DISIMRANK: Ranking potential targets using a driven input single input model motif.
Publications
(2015) “Genome-wide modeling of transcription kinetics reveals patterns of rna production delays” in Proc. Natl. Acad. Sci. USA In press [Google Scholar Search]
Abstract
Genes with similar transcriptional activation kinetics can display very different temporal mRNA profiles because of differences in transcrip tion time, degradation rate, and RNA-processing kinetics. Recent studies have shown that a splicing-associated RNA production delay can be significant. To investigate this issue more generally, it is useful to develop methods applicable to genome-wide datasets. We introduce a joint model of transcriptional activation and mRNA accumulation that can be used for inference of transcription rate, RNA production delay, and degradation rate given data from high-throughput sequencing time course experiments. We combine a mechanistic differential equation model with a nonparametric statistical modeling approach allowing us to capture a broad range of activation kinetics, and we use Bayesian parameter estimation to quantify the uncertainty in estimates of the kinetic parameters. We apply the model to data from estrogen receptor α activation in the MCF-7 breast cancer cell line. We use RNA polymerase II ChIP-Seq time course data to characterize transcriptional activation and mRNA-Seq time course data to quantify mature transcripts. We find that 11% of genes with a good signal in the data display a delay of more than 20 min between completing transcription and mature mRNA production. The genes displaying these long delays are significantly more likely to be short. We also find a statistical association between high delay and late intron retention in pre-mRNA data, indicating significant splicing-associated production delays in many genes.
(2014) “Inference of rna polymerase ii transcription dynamics from chromatin immunoprecipitation time course data” in PLoS Computat Biol 10 (5), pp e1003598 [DOI][Google Scholar Search]
(2012) “Identifying targets of multiple co-regulated transcription factors from expression time-series by Bayesian model comparison” in BMC Systems Biology 6 (53) [DOI][Google Scholar Search]
Abstract
\textbf{Background}\ \ Complete transcriptional regulatory network inference is a huge challenge because of the complexity of the network and sparsity of available data. One approach to make it more manageable is to focus on the inference of context-specific networks involving a few interacting transcription factors (TFs) and all of their target genes. \textbf{Results}\ \ We present a computational framework for Bayesian statistical inference of target genes of multiple interacting TFs from high-throughput gene expression time-series data. We use ordinary differential equation models that describe transcription of target genes taking into account combinatorial regulation. The method consists of a training and a prediction phase. During the training phase we infer the unobserved TF protein concentrations on a subnetwork of approximately known regulatory structure. During the prediction phase we apply Bayesian model selection on a genome-wide scale and score all alternative regulatory structures for each target gene. We use our methodology to identify targets of five TFs regulating Drosophila melanogaster mesoderm development. We find that confident predicted links between TFs and targets are significantly enriched for supporting ChIP-chip binding events and annotated TF-gene interations. Our method statistically significantly outperforms existing alternatives. \textbf{Conclusions}\ \ Our results show that it is possible to infer regulatory links between multiple interacting TFs and their target genes even from a single relatively short time series and in presence of unmodelled confounders and unreliable prior knowledge on training network connectivity. Introducing data from several different experimental perturbations significantly increases the accuracy.
(2012) “Identifying differentially expressed transcripts from rna-seq data with biological variation” in Bioinformatics 28 (13), pp 1721–1728 [PDF][DOI][Google Scholar Search]
Abstract
\textbf{Motivation}: High-throughput sequencing enables expression analysis at the level of individual transcripts. The analysis of transcriptome expression levels and differential expression (DE) estimation requires a probabilistic approach to properly account for ambiguity caused by shared exons and finite read sampling as well as the intrinsic biological variance of transcript expression.\ \ \textbf{Results:} We present Bayesian inference of transcripts from sequencing data (BitSeq), a Bayesian approach for estimation of transcript expression level from RNA-seq experiments. Inferred relative expression is represented by Markov chain Monte Carlo samples from the posterior probability distribution of a generative model of the read data. We propose a novel method for DE analysis across replicates which propagates uncertainty from the sample-level model while modelling biological variance using an expression-level-dependent prior. We demonstrate the advantages of our method using simulated data as well as an RNA-seq dataset with technical and biological replication for both studied conditions.\ \ \textbf{Availability:} The implementation of the transcriptome expression estimation and differential expression analysis, BitSeq, has been written in C++ and Python. The software is available online from http://code.google.com/p/bitseq/, version 0.4 was used for generating results presented in this article.\ \ \\textbf{Contact:} glaus@cs.man.ac.uk, antti.honkela@hiit.fi or m.rattray@sheffield.ac.uk
The following publications have provided background to our work in this project.
(2011) “Computationally efficient convolved multiple output Gaussian processes” in Journal of Machine Learning Research 12, pp 1425–1466 [Software][PDF][Google Scholar Search]
Abstract
Recently there has been an increasing interest in regression methods that deal with multiple outputs. This has been motivated partly by frameworks like multitask learning, multisensor networks or structured output data. From a Gaussian processes perspective, the problem reduces to specifying an appropriate covariance function that, whilst being positive semi-definite, captures the dependencies between all the data points and across all the outputs. One approach to account for non-trivial correlations between outputs employs convolution processes. Under a latent function interpretation of the convolution transform we establish dependencies between output variables. The main drawbacks of this approach are the associated computational and storage demands. In this paper we address these issues. We present different efficient approximations for dependent output Gaussian processes constructed through the convolution formalism. We exploit the conditional independencies present naturally in the model. This leads to a form of the covariance similar in spirit to the so called PITC and FITC approximations for a single output. We show experimental results with synthetic and real data, in particular, we show results in school exams score prediction, pollution prediction and gene expression data
(2010) “Model-based method for transcription factor target identification with limited data” in Proc. Natl. Acad. Sci. USA 107 (17), pp 7793–7798 [Software][DOI][Google Scholar Search]
Abstract
We present a computational method for identifying potential targets of a transcription factor (TF) using wild-type gene expression time series data. For each putative target gene we fit a simple differential equation model of transcriptional regulation, and the model likelihood serves as a score to rank targets. The expression profile of the TF is modeled as a sample from a Gaussian process prior distribution that is integrated out using a nonparametric Bayesian procedure. This results in a parsimonious model with relatively few parameters that can be applied to short time series datasets without noticeable overfitting. We assess our method using genome-wide chromatin immunoprecipitation (ChIP-chip) and loss-of-function mutant expression data for two TFs, Twist, and Mef2, controlling mesoderm development in Drosophila. Lists of top-ranked genes identified by our method are significantly enriched for genes close to bound regions identified in the ChIP-chip data and for genes that are differentially expressed in loss-of-function mutants. Targets of Twist display diverse expression profiles, and in this case a model-based approach performs significantly better than scoring based on correlation with TF expression. Our approach is found to be comparable or superior to ranking based on mutant differential expression scores. Also, we show how integrating complementary wild-type spatial expression data can further improve target ranking performance.
N. D. Lawrence, M. Girolami, M. Rattray and G. Sanguinetti (eds) (2010) “Learning and inference in computational systems biology”, MIT Press, Cambridge, MA.
Synopsis
Computational systems biology aims to develop algorithms that uncover the structure and parameterization of the underlying mechanistic model—in other words, to answer specific questions about the underlying mechanisms of a biological system—in a process that can be thought of as learning or inference. This volume offers state-of-the-art perspectives from computational biology, statistics, modeling, and machine learning on new methodologies for learning and inference in biological networks. The chapters offer practical approaches to biological inference problems ranging from genome-wide inference of genetic regulation to pathway-specific studies. Both deterministic models (based on ordinary differential equations) and stochastic models (which anticipate the increasing availability of data from small populations of cells) are considered. Several chapters emphasize Bayesian inference, so the editors have included an introduction to the philosophy of the Bayesian approach and an overview of current work on Bayesian inference. Taken together, the methods discussed by the experts in Learning and Inference in Computational Systems Biology provide a foundation upon which the next decade of research in systems biology can be built.
(2008) “Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities” in Bioinformatics 24, pp i70–i75 [Software][PDF][DOI][Google Scholar Search]
Abstract
Motivation: Inference of latent chemical species in biochemical interaction networks is a key problem in estimation of the structure and parameters of the genetic, metabolic and protein interaction networks that underpin all biological processes. We present a framework for Bayesian marginalisation of these latent chemical species through Gaussian process priors.\ \ Results: We demonstrate our general approach on three different biological examples of single input motifs, including both activation and repression of transcription. We focus in particular on the problem of inferring transcription factor activity when the concentration of active protein cannot easily be measured. We show how the uncertainty in the inferred transcription factor activity can be integrated out in order to derive a likelihood function that can be used for the estimation of regulatory model parameters. An advantage of our approach is that we avoid the use of a coarse-grained discretization of continuous-time functions, which would lead to a large number of additional parameters to be estimated. We develop efficient exact and approximate inference schemes, which are much more efficient than competing sampling-based schemes and therefore provide us with a practical toolkit for model-based inference.\ \ Availability: The software and data for recreating all the experiments in this paper is available in MATLAB from http://staffwww.dcs.shef.ac.uk/people/N.Lawrence/gpsim\ \ Contact: Neil Lawrence
(2010) “Introduction to learning and inference in computational systems biology” in N. D. Lawrence, M. Girolami, M. Rattray and G. Sanguinetti (eds) Learning and Inference in Computational Systems Biology, MIT Press, Cambridge, MA. [MIT Press Site][Google Scholar Search]
Abstract
Computational systems biology aims to develop algorithms that uncover the structure and parameterization of the underlying mechanistic model—in other words, to answer specific questions about the underlying mechanisms of a biological system—in a process that can be thought of as learning or inference. This volume offers state-of-the-art perspectives from computational biology, statistics, modeling, and machine learning on new methodologies for learning and inference in biological networks. The chapters offer practical approaches to biological inference problems ranging from genome-wide inference of genetic regulation to pathway-specific studies. Both deterministic models (based on ordinary differential equations) and stochastic models (which anticipate the increasing availability of data from small populations of cells) are considered. Several chapters emphasize Bayesian inference, so the editors have included an introduction to the philosophy of the Bayesian approach and an overview of current work on Bayesian inference. Taken together, the methods discussed by the experts in Learning and Inference in Computational Systems Biology provide a foundation upon which the next decade of research in systems biology can be built.
(2010) “A brief introduction to Bayesian inference” in N. D. Lawrence, M. Girolami, M. Rattray and G. Sanguinetti (eds) Learning and Inference in Computational Systems Biology, MIT Press, Cambridge, MA. [MIT Press Site][Google Scholar Search]
Abstract
Computational systems biology aims to develop algorithms that uncover the structure and parameterization of the underlying mechanistic model—in other words, to answer specific questions about the underlying mechanisms of a biological system—in a process that can be thought of as learning or inference. This volume offers state-of-the-art perspectives from computational biology, statistics, modeling, and machine learning on new methodologies for learning and inference in biological networks. The chapters offer practical approaches to biological inference problems ranging from genome-wide inference of genetic regulation to pathway-specific studies. Both deterministic models (based on ordinary differential equations) and stochastic models (which anticipate the increasing availability of data from small populations of cells) are considered. Several chapters emphasize Bayesian inference, so the editors have included an introduction to the philosophy of the Bayesian approach and an overview of current work on Bayesian inference. Taken together, the methods discussed by the experts in Learning and Inference in Computational Systems Biology provide a foundation upon which the next decade of research in systems biology can be built.
(2010) “Gaussian processes for missing species in biochemical systems” in N. D. Lawrence, M. Girolami, M. Rattray and G. Sanguinetti (eds) Learning and Inference in Computational Systems Biology, MIT Press, Cambridge, MA. [Pubmed][MIT Press Site][Google Scholar Search]
Abstract
Computational systems biology aims to develop algorithms that uncover the structure and parameterization of the underlying mechanistic model—in other words, to answer specific questions about the underlying mechanisms of a biological system—in a process that can be thought of as learning or inference. This volume offers state-of-the-art perspectives from computational biology, statistics, modeling, and machine learning on new methodologies for learning and inference in biological networks. The chapters offer practical approaches to biological inference problems ranging from genome-wide inference of genetic regulation to pathway-specific studies. Both deterministic models (based on ordinary differential equations) and stochastic models (which anticipate the increasing availability of data from small populations of cells) are considered. Several chapters emphasize Bayesian inference, so the editors have included an introduction to the philosophy of the Bayesian approach and an overview of current work on Bayesian inference. Taken together, the methods discussed by the experts in Learning and Inference in Computational Systems Biology provide a foundation upon which the next decade of research in systems biology can be built.
(2011) “Markov chain Monte Carlo algorithms for Gaussian processes” in D. Barber, A. T. Cemgil and S. Chiappa (eds) Bayesian Time Series Models, Cambridge University Press, . [Google Scholar Search]
Abstract
`What’s going to happen next?’ Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical models, the book covers approximation schemes, both Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent-based models in a variety of application environments. It demonstrates that the basic framework supports the rapid creation of models tailored to specific applications and gives insight into the computational complexity of their implementation. The authors span traditional disciplines such as statistics and engineering and the more recently established areas of machine learning and pattern recognition. Readers with a basic understanding of applied probability, but no experience with time series analysis, are guided from fundamental concepts to the state-of-the-art in research and practice.