TFInfer: A Tool for Probabilistic Inference of Transcription Factor Activities

School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UK.
Bioinformatics (Impact Factor: 4.98). 10/2010; 26(20):2635-6. DOI: 10.1093/bioinformatics/btq469
Source: PubMed


TFInfer is a novel open access, standalone tool for genome-wide inference of transcription factor activities from gene expression data. Based on an earlier MATLAB version, the software has now been extended in a number of ways. It has been significantly optimised in terms of performance, and it was given novel functionality, by allowing the user to model both time series and data from multiple independent conditions. With a full documentation and intuitive graphical user interface, together with an in-built data base of yeast and Escherichia coli transcription factors, the software does not require any mathematical or computational expertise to be used effectively.
Supplementary data are available at Bioinformatics online.

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Available from: Magnus Rattray, Nov 10, 2015
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    • "TFInfer [11] was used to deduce the activities of 134 TFs simultaneously. TFInfer is based on a probabilistic model of transcription [8] that relies on a log-linear approximation to transcriptional dynamics. "
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    ABSTRACT: Understanding gene regulation requires knowledge of changes in transcription factor (TF) activities. Simultaneous direct measurement of numerous TF activities is currently impossible. Nevertheless, statistical approaches to infer TF activities have yielded non-trivial and verifiable predictions for individual TFs. Here, global statistical modelling identifies changes in TF activities from transcript profiles of Escherichia coli growing in stable (fixed oxygen availabilities) and dynamic (changing oxygen availability) environments. A core oxygen-responsive TF network, supplemented by additional TFs acting under specific conditions, was identified. The activities of the cytoplasmic oxygen-responsive TF, FNR, and the membrane-bound terminal oxidases implied that, even on the scale of the bacterial cell, spatial effects significantly influence oxygen-sensing. Several transcripts exhibited asymmetrical patterns of abundance in aerobic to anaerobic and anaerobic to aerobic transitions. One of these transcripts, ndh, encodes a major component of the aerobic respiratory chain and is regulated by oxygen-responsive TFs ArcA and FNR. Kinetic modelling indicated that ArcA and FNR behaviour could not explain the ndh transcript profile, leading to the identification of another TF, PdhR, as the source of the asymmetry. Thus, this approach illustrates how systematic examination of regulatory responses in stable and dynamic environments yields new mechanistic insights into adaptive processes.
    Full-text · Article · Jul 2012 · Open Biology
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    • "Data were analysed using the TFInfer statistical modelling tool, which integrates the architecture of the regulatory network with gene expression data to infer the activity profile of TFs (Sanguinetti et al., 2006; Asif et al., 2010) (see Methods for the mathematical details and further comments on the model). The analysis of the transcriptomic data generated from the Zn shift focused on the role of ten key regulators: ArsR, BaeR, CpxR, CusR, Fur, OxyR, SoxS, ZntR, ZraR and Zur. "
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    ABSTRACT: We describe a hybrid transcriptomic and modelling analysis of the dynamics of a bacterial response to stress, namely the addition of 200 µM Zn to Escherichia coli growing in severely Zn-depleted medium and of cells growing at different Zn concentrations at steady state. Genes that changed significantly in response to the transition were those reported previously to be associated with zinc deficiency (zinT, znuA, ykgM) or excess (basR, cpxP, cusF). Cellular Zn levels were confirmed by ICP-AES to be 14- to 28-fold greater after Zn addition but there was also 6- to 8-fold more cellular Fe 30 min after Zn addition. Statistical modelling of the transcriptomic data generated from the Zn shift focused on the role of ten key regulators; ArsR, BaeR, CpxR, CusR, Fur, OxyR, SoxS, ZntR, ZraR and Zur. The data and modelling reveal a transient change in the activity of the iron regulator Fur and of the oxidative stress regulator SoxS, neither of which is evident from the steady-state transcriptomic analyses. We hypothesize a competitive binding mechanism that combines these observations and existing data on the physiology of Zn and Fe uptake. Formalizing the mechanism in a differential equation model shows that it can reproduce qualitatively the behaviour seen in the data. This gives new insights into the interplay of these two fundamental metal ions in gene regulation and bacterial physiology, as well as highlighting the importance of dynamic studies to reverse-engineer systems behaviour.
    Full-text · Article · Jan 2012 · Microbiology
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    • "These datasets are the classic and much studied yeast cell cycle data set (Spellman et al., 1998), the yeast metabolic cycle data set (Tu et al., 2005) and the E. coli micro-aerobic shift data set (Partridge et al., 2007). Finally, we compare our results with those obtained with two different methods: a simplified version of the method by Shi et al. (2009) and the TFInfer method o Sanguinetti et al. (2006); Shahzad Asif et al. (2010) "
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    ABSTRACT: Knowledge of the activation patterns of transcription factors (TFs) is fundamental to elucidate the dynamics of gene regulation in response to environmental conditions. Direct experimental measurement of TFs' activities is, however, challenging, resulting in a need to develop statistical tools to infer TF activities from mRNA expression levels of target genes. Current models, however, neglect important features of transcriptional regulation; in particular, the combinatorial nature of regulation, which is fundamental for signal integration, is not accounted for. We present a novel method to infer combinatorial regulation of gene expression by multiple transcription factors in large-scale transcriptional regulatory networks. The method implements a factorial hidden Markov model with a non-linear likelihood to represent the interactions between the hidden transcription factors. We explore our model's performance on artificial datasets and demonstrate the applicability of our method on genome-wide scale for three expression datasets. The results obtained using our model are biologically coherent and provide a tool to explore the concealed nature of combinatorial transcriptional regulation.
    Preview · Article · Mar 2011 · Bioinformatics
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