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Bioinformatics. Sep 1, 2010; 26(17): 2071–2075.
Published online Jul 27, 2010. doi:  10.1093/bioinformatics/btq405
PMCID: PMC2922897

Genome-wide histone acetylation data improve prediction of mammalian transcription factor binding sites

Abstract

Motivation: Histone acetylation (HAc) is associated with open chromatin, and HAc has been shown to facilitate transcription factor (TF) binding in mammalian cells. In the innate immune system context, epigenetic studies strongly implicate HAc in the transcriptional response of activated macrophages. We hypothesized that using data from large-scale sequencing of a HAc chromatin immunoprecipitation assay (ChIP-Seq) would improve the performance of computational prediction of binding locations of TFs mediating the response to a signaling event, namely, macrophage activation.

Results: We tested this hypothesis using a multi-evidence approach for predicting binding sites. As a training/test dataset, we used ChIP-Seq-derived TF binding site locations for five TFs in activated murine macrophages. Our model combined TF binding site motif scanning with evidence from sequence-based sources and from HAc ChIP-Seq data, using a weighted sum of thresholded scores. We find that using HAc data significantly improves the performance of motif-based TF binding site prediction. Furthermore, we find that within regions of high HAc, local minima of the HAc ChIP-Seq signal are particularly strongly correlated with TF binding locations. Our model, using motif scanning and HAc local minima, improves the sensitivity for TF binding site prediction by ~50% over a model based on motif scanning alone, at a false positive rate cutoff of 0.01.

Availability: The data and software source code for model training and validation are freely available online at http://magnet.systemsbiology.net/hac.

Contact: gro.ygoloibsmetsys@mereda; gro.ygoloibsmetsys@hcivelumhsi

Supplementary information: Supplementary data are available at Bioinformatics online.

1 INTRODUCTION

Mammalian cells exhibit diverse transcriptional profiles across different cell types and conditions, for example, in immune cells activated with different pathogen-associated molecules (Ramsey et al., 2008). To a large extent, these profiles are controlled by the arrangement and chromatin accessibility of cis-regulatory elements (Berger, 2007). Transcription factors (TFs) bind specific sequence elements in chromatin locations of permissive epigenetic or conformational states, leading to activation or repression of transcriptional activity. For mapping these regulatory interactions, it is particularly promising that the binding of a TF can now be measured genome wide using chromatin immunoprecipitation (IP) with sequence detection (ChIP-Seq, see (Johnson et al., 2007)). However, antibody and cellular material requirements preclude using ChIP-Seq to screen for all TFs mediating a transcriptional response. There remains a need for computational approaches that can, in the absence of experimental TF binding data, leverage transcriptional data and genomic information to identify the network of TFs and binding sites that underlies a transcriptional response.

An important tool for predicting mammalian TF binding sites is motif scanning, i.e. searching DNA sequence for matches within a library of sequence motifs reported to be bound by specific TFs (Lähdesmäki et al., 2008). Such a library enables mapping between a scanning-identified sequence element and one or more candidate TFs that may bind it. However, such motifs are often highly uncertain and they can be degenerate, leading to a high frequency of false positive predictions (Hannenhalli, 2008). Furthermore, mammalian cis-regulatory elements can be tens of kilobases from transcription start sites, necessitating searching large sequence regions and further increasing false positives. These issues undermine the performance of motif scanning as a standalone approach. Successful motif-based prediction of TF binding depends on identifying the sequence regions, within the relevant cell type, that are likely to contain cis-regulatory elements (Ernst et al., 2010; Wasserman and Sandelin, 2004; Whitington et al., 2009).

It has been observed that cis-regulatory elements tend to co-occur with chromatin or sequence features that can be grouped in three categories: (i) chromatin structural features such as DNase I hypersensitive sites; (ii) epigenetic marks such as histone acetylation (HAc); and (iii) sequence features such as high GC content and conservation across species. The HAc mark, which has been associated with active promoters and open chromatin (Vettese-Dadey et al., 1996), is of particular relevance to transcriptional regulation because the modification can be placed or removed in response to the cellular state. These observations have spurred the development of approaches that integrate data for multiple types of chromatin features to improve the accuracy of TF binding site predictions. Various data integration frameworks for binding site prediction have been used, including the support vector machine (Holloway et al., 2005; Nykter et al., 2009), probabilistic methods (Beyer et al., 2006; Ernst et al., 2010; Lähdesmäki et al., 2008), and a kernel-based classifier (Wang et al., 2009). Early studies integrating genomic data into binding site prediction were carried out in yeast (Beyer et al., 2006; Holloway et al., 2005), or in mammals using ground-truth datasets that were not cell type-specific (Lähdesmäki et al., 2008). More recent approaches have used cell type-specific mammalian epigenetic or transcriptional data to predict binding for a single TF in mammals (Nykter et al., 2009; Wang et al., 2009). Other recent studies have used genome-wide datasets for multiple TFs to develop prediction models without directly incorporating epigenetic data into the model (Won et al., 2009; Zhou et al., 2010). Two recent studies incorporated histone methylation ChIP-Seq data into multi-evidence prediction models, using ChIP-derived ground-truth datasets of 10 and 13 TFs, respectively (Whitington et al., 2009; Won et al., 2010). Whitington et al. found that predictions are improved when the methylation data are derived from the same tissue type from which the TF binding site measurements are derived.

In this study, we investigated the hypothesis that incorporating HAc ChIP-Seq data into a multi-evidence, motif scanning-based model can improve TF binding site predictions. We further studied whether prediction performance is improved when the HAc data are derived from the same cell condition from which the TF binding data (used for evaluating performance) are derived. Having observed that TF binding locations frequently occur at local minima of HAc ChIP-Seq signal within regions of high HAc (see Fig. 1 and Supplementary Fig. S1), we also studied the predictive utility of ‘valley scores’ (VS) assigned to local minima of the HAc ChIP-Seq signal. Following our previous investigation of the regulatory network underlying macrophage activation (Ramsey et al., 2008), this study was carried out using TF binding and HAc measurements in the macrophage, a key cell type of the innate immune system. When activated by exposure to a pathogen-associated molecule such as lipopolysaccharide (LPS), the macrophage undergoes extensive transcriptional reprogramming that is mediated in part by alterations in HAc (Aung et al., 2006).

Fig. 1.
Local minima in the HAc ChIP signal correlate with TF binding. (A) A 12 kbp region of mouse chromosome 11 including the gene Ccl5 [an LPS-regulated cytokine with multiple NFκB (nuclear factor of kappa light polypeptide gene enhancer in B-cells) ...

2 APPROACH

The HAc hypothesis was tested using an integrative TF binding site (TFBS) prediction framework and using an approach designed to estimate the performance that the prediction model would have on a novel TF for which only a binding site motif is available. As features, the framework used motif scanning data (Supplementary Fig. S2) along with subsets of seven non-TF-specific features selected for their potential association with TFBSs. As shown in Figure 1A and Supplementary Table S1, the features consisted of HAc (acetylated H4) ChIP-Seq data from activated and non-activated macrophages; VS derived from the HAc data; and three features based on genomic sequence (GC content, vertebrate species conservation and a nucleosome occupancy prediction score). A peak in the HAc VS signal corresponds to a local minimum in the HAc ChIP-Seq signal. As a ground-truth TFBS dataset, we used ChIP-Seq data, from activated macrophages, for five TFs (Supplementary Table S2). In keeping with the study goals, the ChIP-Seq data were not used to improve the motifs, and model performance was tested using binding data for a TF that was not used in the model training. Performance measurements obtained using such a TF-based cross-validation are, in our view, more relevant to this application (library-based motif scanning) than are results from chromosome-based cross-validation. Importantly, HAc was measured in LPS-stimulated macrophages, consistent with the conditions for TF binding measurements.

TF binding site predictions were made in adjacent 100 bp intervals spanning 10 kb promoter regions of genes that are expressed in murine macrophages. A value for each TF prediction feature was computed within each 100 bp interval, from the feature's raw data. A weighted, thresholded linear model class was used to combine the motif scanning feature with zero, one or two additional features to predict binding for the five TFs. This model class divides the range of each feature's values into three regimes: below minimum threshold (value changes within this regime are not informative), above the maximum threshold (saturated; changes are also not informative) and within the linear response range. Fifteen models, each using a different combination of features (Supplementary Table S3), were trained using the ground-truth dataset. Because the model using HAc ChIP VS performed the best among the two-feature models, the three-feature model analysis was restricted to models with motifs, HAc ChIP VS and one additional feature. For training, each model's parameters were optimized to maximize the average prediction performance for a set of four TFs, with the performance metric being the area under the sensitivity versus false positive rate (FPR) curve. The performance of the model, with the best parameter set from the training, was then tested on the fifth TF, and averaged over the leave-one-out cross-validation.

3 METHODS

Complete methods are described in Supplementary Material, Section S1.

Ground-truth dataset: ChIP-Seq assays were performed for the TFs ATF3, C/EBPδ, IRF1, NFκB/p50 and NFκB/p65 in macrophages activated through treatment with purified Toll-like receptor agonists for 1–6 h (see Supplementary Table S2 and Section S1.4). Binding locations were identified from above-threshold locations in the ChIP-Seq signal, as described in Supplementary Section S1.7.

Prediction features: TF predictions were made in 100 bp intervals (as used in Won et al., 2010) of transcript-proximal regions comprising ~7% of the genome, selected as described in Supplementary Section S1.2. Combinations of eight features, individually listed in Supplementary Table S1 and labeled by index f, were used for TF binding prediction. Feature f = 1, which conferred TF specificity to the predictions, was based on motif scanning. For each TF, motif position-weight matrices (PWMs) corresponding to the TF were obtained from TRANSFAC (Supplementary Table S2 and Section S1.3). Sequences were scanned for motif matches using a likelihood-based algorithm (Lähdesmäki et al., 2008), and combined to obtain, within each interval and for each TF, a score representing the strength of the best match for any motif corresponding to that TF, at any position within the interval. Features 2–5 of Supplementary Table S1 were derived from HAc ChIP-Seq assays of unstimulated macrophages or macrophages stimulated for 1, 4 or 6 h with LPS (Supplementary Sections S1.4–1.5). VS for HAc local minima were computed as described in Supplementary Section S1.6. Features 6–8 were based on genomic sequence, and thus are not macrophage specific. For the stimulated-cell HAc ChIP-Seq features (Supplementary Table S1, rows 2 and 4), the time point for the HAc dataset that was used was always the same as the time point of the ground-truth dataset for the TF for which predictions were being made.

Prediction model: within each interval i, the model integrates a set F of up to three features (always including the motif feature, f = 1) by a weighted sum of thresholded feature values. Feature values may depend on the TF t, as is the case for motif scanning, or on the cellular condition for which TF binding predictions are being made (as is the case for HAc-derived features). The value for feature f at interval i and TF t is therefore denoted by vfit. The feature value vfit is passed through a piecewise-linear function θf that is defined by feature-specific thresholds λf and μf,

equation image
(1)

The prediction score σit that the TF t binds within interval i is obtained by a weighted sum of thresholded contributions, but with a multiplicative factor enforcing a minimum TF-specific motif match value for a non-zero σit,

equation image
(2)

where the weight vector An external file that holds a picture, illustration, etc.
Object name is btq405i1.jpg has unit L1 norm (a negative component would represent a feature that is anti-correlated with TF binding), and where θ is defined by θ(x) = 0 if x ≤ 0 and θ(x) = 1 if x > 0. Importantly, a given model instance x2133, defined by the tuple An external file that holds a picture, illustration, etc.
Object name is btq405i2.jpg, is TF independent.

Performance metric: for a given model x2133, TF t, and prediction score cutoff σ, the set of intervals Π(σ, t) for which σit ≥ σ were predicted to contain binding sites for t (remaining intervals were predicted to have no t binding). The set of intervals containing ground-truth binding sites (based on ChIP-Seq) is denoted by Σ(t). Because the typical ChIP-Seq fragment size was ~160 bp, some TF binding locations appeared as adjacent intervals in Σ(t); these were counted as single binding sites. The number of ground-truth binding sites B(t) was counted (Supplementary Table S2), and the fraction of these binding sites that coincided with at least one interval i [set membership] Π(σ, t), was computed as the sensitivity S(σ, t). The FPR E(σ, t) was computed by dividing the number of intervals in the set difference Π(σ, t)\Σ(t) by the number of intervals not contained in Σ(t). The cutoff σ was varied and the resulting (E(σ, t), S(σ, t)) function [receiver operating characteristic (ROC) curve] was numerically integrated over the range 0 < E ≤ 0.01 to obtain the TF-specific performance score A(t). For model training (Supplementary Section S1.12), the cost function used was C(t) = 1 − A(t)/0.01. During training, cases where it was not possible to obtain a sufficient number of (S, E) samples were handled using a penalty, as described in Supplementary Section S1.11.

Model training: groups of four TFs at a time were selected for model training, and for a given model x2133, the cost was averaged over the four TFs, C = left angle bracketC(t)right angle brackett. Model parameters were varied to minimize C subject to constraints on An external file that holds a picture, illustration, etc.
Object name is btq405i3.jpg, An external file that holds a picture, illustration, etc.
Object name is btq405i4.jpg and An external file that holds a picture, illustration, etc.
Object name is btq405i5.jpg, using a two-stage optimization process (Supplementary Section S1.12), to obtain the best parameter set for the model with features F.

Model testing: for both training and testing purposes, the performance A(t′) of the model with the best parameter set from the training, was measured on the fifth TF t′ using leave-one-out cross-validation. The five values for A(t′) were compared between different feature groups F using a paired t-test, and summarized in terms of the mean and SD (Supplementary Table S3).

4 RESULTS

Feature distributions: first, the TF specificity of the motif scanning was investigated. Across all five TFs, the motif scanning score distribution from TFBSs was significantly higher than the distribution from non-binding sites (Supplementary Fig. S2). Next, HAc VS representing local minima were computed, and the distributions of VS at TFBSs and non-binding sites were compared. In LPS-stimulated cells, HAc VS were significantly higher at TFBSs than at non-binding sites (Fig. 1B); this motivated the use of HAc ChIP data to improve predictions. Furthermore, LPS-dependent TFBSs were correlated with LPS-inducible HAc local minima (Supplementary Table S4 and Fig. S3).

Model performance: first, two-feature models (motifs plus one other feature) were compared with a motifs-only reference model. Based on the area under the sensitivity versus FPR curve (Fig. 2, Supplementary Fig. S4 and Table S3), the model with HAc VS from stimulated cells had the highest performance improvement relative to the reference model (52% increase, P < 10−3). The HAc ChIP-Seq signal also improved prediction performance (by 14%), but the improvement was highly variable from TF to TF (coefficient of variation = 27%; see Supplementary Table S5). The model using the stimulated-cell HAc VS also outperformed the unstimulated-cell HAc VS data (by 31%, P < 0.01). In contrast to the HAc ChIP-derived datasets, the three genomic features (GC content, conservation and nucleosome occupancy score) did not substantially improve prediction performance. However, the improvements due to GC content (5% increase) and conservation (3%) were more consistent from TF to TF, and thus in both cases were statistically significant (P < 0.05). Next, models with motifs plus two other features were compared with the best previous model (motifs + HAc VS). None of the models gave a statistically significant improvement over the best two-feature model (Supplementary Fig. S5). These findings suggest that more TF binding data would be required to discriminate prediction performances of three-feature models.

Fig. 2.
HAc data improve motif scanning-based TFBS predictions. (A) Prediction performance (area under the sensitivity versus FPR curve, or ‘ROC’ curve) for models with motif scanning and one additional feature, and a motifs-only reference model ...

5 CONCLUSIONS

Using cell type-specific HAc ChIP-Seq data improves motif scanning-based prediction of TFBSs in primary macrophages. This prediction strategy could be applied to any cell type in which HAc can be globally measured. Overall, these findings suggest that within histone-acetylated regions, local minima of HAc ChIP-Seq signal may indicate sites of active transcriptional regulation.

Supplementary Material

Supplementary Data:

ACKNOWLEDGEMENTS

We thank K. Deutsch, S. Bloom and M. Gundapuneni for technical assistance. H. Lähdesmäki and M. Nykter kindly provided some MATLAB functions. S.A.R. thanks A. Diercks, E. Fu and V. Thorsson for helpful discussions. We thank A. Nachman, B. Marzolf, D. Rodriguez and L. Rowen for coordinating the contributions of their groups.

Funding: The National Heart, Lung, and Blood Institute (K25HL098807 to S.A.R.); the National Institute of Allergy and Infectious Diseases (HHSN272200700038C); and the National Institute of General Medical Sciences (R01GM072855 to I.S. and P50GM076547).

Conflict of Interest: none declared.

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