SeqUnwinder overview

The intuition behind SeqUnwinder is that sequence features associated with a particular annotation label should be similarly enriched across all subclasses spanned by the label (regardless of how the subclasses have been defined). SeqUnwinder’s analysis begins by defining genomic site subclasses based on the combinations of labels annotated at these sites (Fig 1B). The site subclasses are treated as distinct classes for a multi-class logistic regression model that uses k-mer frequencies as predictors. At the same time, k-mer models are also learned for each label by incorporating them in an L1 regularization term (see Methods). In other words, while the k-mer weight parameters for each subclass are learned directly from the data, the weight parameters for the labels are learned exclusively through the regularization constraint. The regularization encourages each label’s model to take the form of the features that are consistently enriched across the subclasses spanned by that label (Fig 1B). The trained classifier encapsulates weighted k-mer models specific to each label and each subclass (i.e. combination of labels). The label- or subclass-specific k-mer model is scanned across the original genomic sites to identify focused regions (which we term “hills”) that contain discriminative sequence signals (Fig 1C). Finally, to aid interpretability, SeqUnwinder identifies over-represented motifs in the hills and scores them using label- and subclass-specific k-mer models (Fig 1D).

SeqUnwinder is easy to use, taking as input a list of DNA sequences or genomic coordinates that are each annotated with a set of user-defined labels. The labels can come from any source, enabling a high degree of analysis flexibility. SeqUnwinder implements a multi-threaded version of the ADMM [12] framework to train the model and typically runs in less than a few hours for most datasets. Output includes both k-mer models and position-specific scoring matrices and weights associating these motifs with each subclass and label.

SeqUnwinder deconvolves sequence features associated with overlapping labels

To demonstrate the properties of SeqUnwinder, we simulated 9,000 regulatory regions and annotated each of them with labels from two overlapping sets: A, B, C and X, Y (Fig 2A). We assigned a different motif to each label. At 70% of the sequences associated with each label, we inserted appropriate motif instances by sampling from the distributions defined by the position-specific scoring matrices of label assigned motifs (Fig 2A). We used this collection of sequences and label assignments to compare SeqUnwinder with a simple multi-class classification approach (MCC). In MCC training, each label was treated as a distinct class and therefore each regulatory sequence is included multiple times in accordance with its annotated labels.

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Fig 2. Performance of SeqUnwinder on simulated datasets.

(A) 9000 simulated genomic sites with corresponding motif associations. (B) Label-specific scores for all de novo motifs identified using MCC (left) and SeqUnwinder (right) models on simulated genomic sites in “A”. For consistency across figures, we fix the color saturation values to -0.4 and 0.4 (C) Schematic showing 100 genomic datasets with 6000 genomic sites and varying degrees of label overlap ranging from 0.5 to 0.99. (D) Performance of MCC (multi-class logistic classifier), DREME, and SeqUnwinder on simulated datasets in “C”, measured using the F1-score, (E) true positive rates, and (F) false positive rates.

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SeqUnwinder and the MCC model correctly identify motifs similar to all inserted motifs (Fig 2B). However, the MCC approach makes several incorrect motif-label associations, potentially due to high overlap between labels. In contrast, the label-specific scores of the identified motifs in the SeqUnwinder model are not confounded by overlap between annotation labels. For example, even though labels X and A highly overlap, SeqUnwinder correctly assigns each motif to its respective label.

Next, we assessed the performance of SeqUnwinder at different levels of label overlaps. We simulated 100 datasets with 6000 simulated sequences, varying the degree of overlap between two sets of labels ({A, B} and {X, Y}) from 50% to 99% (Fig 2C). We then compared SeqUnwinder with MCC and DREME [1], a popular discriminative motif discovery tool. Since DREME takes only two classes as input: a foreground set and a background set, we ran four different DREME runs for each of the four labels. We calculated the true positive (discovered motif correctly assigned to a label) and false positive (discovered motif incorrectly assigned to a label) rates based on the true label assignments. We used these measures to calculate the F1 score (harmonic mean of precision and recall) at different overlapping levels (Fig 2D).

Fig 2D demonstrates the range of label overlap rates in which SeqUnwinder outperforms the alternative approaches. When the labels are uncorrelated (i.e. low or random overlap), the sequence features associated with each label do not confound one another and thus all methods perform similarly well in characterizing label-specific motifs. On the other hand, when the labels are highly correlated (i.e. high overlap), it becomes impossible for any method to correctly assign sequence features to the correct labels. SeqUnwinder performs better than the other approaches in the intermediate range of label overlaps, and accurately characterizes label-specific sequence features even when the simulated labels overlap at 90% of sites. More specifically, SeqUnwinder consistently has a false positive rate (incorrectly assigning motifs to labels) of zero at the cost of a modest decrease in true positive rates (recovering all motifs assigned to a label) (Fig 2E and Fig 2F).

SeqUnwinder uncovers co-factor driven TF binding dynamics during iMN programming

To demonstrate its unique abilities in a real analysis problem, we use SeqUnwinder to study TF binding during induced motor neuron (iMN) programming. Ectopic expression of Ngn2, Isl1, and Lhx3 in mouse embryonic stem (ES) cells efficiently converts the resident ES cells into functional spinal motor neurons [13,14]. We recently characterized the dynamics of motor neuron programming by studying TF binding, chromatin dynamics, and gene expression over the course of the 48hr programming process [14]. We found that two of the ectopically expressed TFs, Isl1 & Lhx3, bind together at the vast majority of their targets during the programming process. Using MultiGPS [15], we also found that this cooperative pair of TFs shifted their binding targets during programming, and we used three mutually exclusive labels–early, shared, and late–to annotate Isl1/Lhx3 binding sites according to their observed dynamic occupancy patterns. Early sites were bound by Isl1/Lhx3 only during earlier stages of programming, shared sites were constantly bound over the entire programming process, and late sites were only bound during the final stage of programming.

In our previous work, we demonstrated that the early Isl1/Lhx3 sites were more accessible in the initial pluripotent cells, and we suggested that some early sites are the result of opportunistic Isl1/Lhx3 binding to ES enhancer regions [14]. However, this raises a question that was not addressed in our earlier work: if we discover sequence features at early sites, how can we tell if those features are specifically associated with Isl1/Lhx3 as opposed to reflecting on coincident properties of ES enhancers?

In order to assess the potential confounding effects of ES regulatory sites, we trained a random forest classifier to further categorize all Isl1/Lhx3 bound sites using two additional labels: “ES-active and “ES-inactive” (see Methods). Annotating Isl1/Lhx3 sites using both sets of labels (Isl1/Lhx3 binding dynamics and ES activity) results in six different subclasses. As can be seen from Fig 3A, early sites have a higher propensity to also be active prior to ectopic TF expression in the starting ES cells. Conversely, the late sites are more likely to be inactive in ES cells.

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Fig 3. SeqUnwinder analysis of Lhx3 binding classes during iMN programming.

(A) Lhx3 binding sites labeled using their dynamic binding behavior and ES chromatin activity statuses. (B) Label-specific scores of de novo motifs identified at Lhx3 binding sites defined in “A” using MCC (left) and SeqUnwinder (right) models. For consistency across figures, we fix the color saturation values to -0.4 and 0.4. (C) Log-odds score distribution of de novo discovered Onecut-like motif at “ES-active”, “ES-inactive“, “Early”, “Shared”, and “Late” sites (left panel). Distribution of Onecut2 (48hr) ChIP-seq tag counts in log-scale at “ES-active”, “ES-inactive“, “Early”, “Shared”, and “Late” sites (right panel). (D) Log-odds score distribution of de novo discovered Oct4-like motif at “ES-active”, “ES-inactive“, “Early”, “Shared”, and “Late” sites (left panel). Distribution of Oct4 (0hr) ChIP-seq tag counts in log-scale at “ES-active”, “ES-inactive“, “Early”, “Shared”, and “Late” sites (right panel). Statistical significance calculated using Mann-Whitney-Wilcoxon test (*: P-value < 0.001).

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We next trained SeqUnwinder on the multi-label Isl1/Lhx3 dataset, and compared the results with those of DREME and the simple MCC approach described in the previous section (Fig 3B, S1 Fig, S2 Fig, S1 Table). All methods discover similar sets of motifs. For example, both the SeqUnwinder and MCC approaches find motifs corresponding to the binding preferences of Oct4, Zfp281, Onecut-family TFs, and homeodomain TF motifs corresponding to the cognate Isl1/Lhx3 binding preference (Fig 3B). However, the different approaches produce different associations between motifs and labels. For example, the MCC approach associates the Oct4 motif with both the “early” and “ES-active” labels, and it associates the Onecut motif with both “late” and “ES-inactive” labels (S1 Fig). DREME similarly makes ambiguous associations (S2 Fig). SeqUnwinder, in contrast, makes much cleaner associations; the Oct4 motif is only associated with the “early” label, and the Onecut motif is only associated with the “late” label, suggesting that these motifs are not merely coincidental features due to the ES activity status of the binding sites.

The SeqUnwinder motif-label associations suggest that Isl1/Lhx3 bind cooperatively with Oct4 and Onecut TFs at subsets of early and late binding sites, respectively. As described in our earlier work, we characterized Onecut2 binding to be highly enriched at late Isl1/Lhx3 sites during iMN programming [14]. We also found that late sites are not bound by Isl1/Lhx3 (and iMN programming does not proceed) in cellular conditions under which Onecut TFs are not expressed [14], supporting a model in which late Isl1/Lhx3 binding is dependent on Onecut TFs. Analysis of motif log-odds scores and Onecut2 ChIP enrichment further support SeqUnwinder’s prediction that the Onecut motif is not merely a general feature of ES-inactive sites (Fig 3C).

Conversely, Oct4 is predicted by SeqUnwinder to be a specific feature of “early” binding sites, and not merely an artifact associated with “ES-active” sites. Using ChIP-seq, we profiled the binding of Oct4 immediately before NIL induction. As shown in Fig 3D, Oct4 motif log-odds scores and ChIP-seq tags show a preferential enrichment at early Isl1/Lhx3 sites, in line with SeqUnwinder’s prediction.

Interestingly, the motif features that are most highly associated with shared binding sites all correspond to homeodomain TF motifs of the type bound by Isl1/Lhx3 (Fig 3B and S1 Fig). One possible explanation is that there are stronger or more frequent cognate motif instances at sites bound by a given TF across multiple timepoints, or indeed across multiple unrelated cell types. We further assess this hypothesis in the following section.

Our analysis of Isl1/Lhx3 binding during iMN programming thus serves as an example analysis scenario in which SeqUnwinder identifies motif features associated with multiple overlapping labels, leading to testable hypotheses about co-factors that serve mechanistic roles at subsets of binding sites.

Multi-condition TF binding sites are characterized by stronger cognate motif instances

The sequence properties of tissue-specific TF binding sites have been extensively studied [4,5,16]. As might be expected, sites that are bound by a given TF in only one cell type are often enriched for motifs of other TFs expressed in that cell type. Therefore, a given TF’s cell-specific binding activity is likely determined by context-specific interactions with other expressed regulators.

Most TFs also display cell-invariant binding activities; each TF typically has a cohort of sites that appear bound in all or most cellular conditions in which that TF is active. Despite the potential regulatory significance of such multi-condition binding sites, little is known about the sequence properties that enable a TF to bind them regardless of cellular conditions. Studies of individual TFs suggest that binding affinity to cognate motif instances may play a role in distinguishing multi-condition binding sites from tissue-specific sites [15,17].

In order to characterize sequence discriminants of multi-condition TF binding sites across a wider range of TFs, we curated multi-condition ChIP-seq experiments from the ENCODE project. We restricted our analysis to the 17 sequence-specific TFs profiled in all 3 primary ENCODE cell-lines (K562, GM12878, and H1-hESC; see Methods) [18]. For each TF, we used MultiGPS analysis to curate sets of tissue-specific sites in each cell type, and a further set of sites that are “shared” across all three cell types (see Methods).

For most examined TFs, the majority of shared binding sites were located in promoter proximal regions (S3 Fig). As outlined in the Introduction, promoter proximal sites are known to have distinct sequence biases, which could confound the discovery of sequence features associated with shared sites. We therefore further labeled each TF’s binding sites as being located proximal or distal to annotated TSSs. Introducing the proximal and distal labels should marginalize the proximal bias at shared sites, as the sequence features learned by SeqUnwinder at shared sites must be consistently enriched at both proximal and distal sites. Each examined TF’s binding sites is thus categorized into eight subclasses, each of which is composed of combinations of six distinct labels.

We applied SeqUnwinder to each labeled sequence collection in order to characterize label-specific sequence features (see S2 Table for cross-validation classification performance values). We illustrate the process with SeqUnwinder’s results for YY1. We started with a total of ~35,000 YY1 binding events called by MultiGPS across the three cell types, categorized into the eight aforementioned subclasses (Fig 4A). SeqUnwinder identifies several de novo motifs in this collection (Fig 4B). Interestingly, SeqUnwinder predicts that a motif matching the cognate YY1 motif is strongly associated with the “shared” label. The cell-type specific, proximal and distal labels had low or negative scores for this cognate motif. Note here that a non-positive label-specific score for a motif does not necessarily imply complete absence of instances of that motif. A significant depletion of motif instances at sites annotated by a label compared to other labels can very likely result in non-positive scores. Cell-type specific sites had higher scores for co-factor motifs. For example, H1-hESC specific sites were enriched in instances of a TEAD-like motif, while K562-specific sites and GM12878-specific sites were enriched for a GATA-like motif and an IRF-like motif, respectively. In fact, GATA2 ChIP-seq reads in K562, IRF4 ChIP-seq reads in GM12878, and TEAD4 ChIP-seq reads in H1hESC showed striking enrichment at corresponding cell-specific YY1 binding sites (Fig 4A).

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Fig 4. SeqUnwinder analysis of sequence features at multi-condition TF binding sites for ENCODE YY1 datasets.

(A) Heatmaps showing the YY1 ChIP-seq reads at curated YY1 binding sites, stratified based on binding across cell-lines and distance from annotated mRNA TSS. The order of subclasses is: Shared and Proximal, Shared and Distal, K562 and Proximal, K562 and Distal, GM12878 and Proximal, GM12878 and Distal, H1-hESC and Proximal, and H1-hESC and Distal. (B) De novo motifs and corresponding label-specific scores identified using SeqUnwinder at events defined in A). For consistency across figures, we fix the color saturation values to -0.4 and 0.4.

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Analogous results were observed for many of the examined factors. For 13 out of the 17 examined factors, SeqUnwinder predicts that the cognate motif is highly associated with the “shared” label (Fig 5A). Despite significant overlaps between shared sites and promoter proximal sites (S3 Fig), the cognate motifs were not found to be predictive of any TF’s “proximal” label (Fig 5A). Furthermore, the cognate motif was not specifically predictive of cell-type-specific labels for the examined TFs, with the exception of H1-hESC-specific sites for CEBPB, NRSF and SRF. An orthogonal analysis of log-odds motif scoring distributions across each TF’s labels is consistent with the SeqUnwinder results (S4 Fig). When we ran DREME on the same datasets for comparison, the association of cognate motif to shared sites was less clear. For 9 of the tested factors, DREME results associated the cognate motif with more than one label (S5 Fig).

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Fig 5. SeqUnwinder analysis of sequence features at multi-condition TF binding sites for 17 ENCODE TFs.

(A) Label-specific scores of de novo discovered cognate motifs across all 17 ENCODE TF datasets. SeqUnwinder did not discover a cognate motif for ZNF143. GM12878-enriched sites for NRF1 and H1-hESC-specific sites for SRF were excluded because of low number of binding sites. (B) Label-specific scores of de novo discovered GATA-like, IRF-like, RUNX-like and TEAD-like motifs. (C) Collective degree distributions at distal shared and cell-specific sites further stratified based on presence of cognate motif. For consistency across figures, we fix the color saturation values to -0.4 and 0.4.

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We also examined the motifs that SeqUnwinder predicts to be associated with cell-type-specific binding labels. Interestingly, we found IRF and RUNX motifs enriched at GM12878-specific binding sites for 11 and 7 of the 17 examined TFs, respectively. Similarly, the GATA motif was predictive of K562-specific binding for 14 of the 17 examined TFs. A TEAD-like motif was predictive of H1-hESC specific sites for 11 of the 17 TFs (Fig 5B). The observation that cell-type-specific sites are depleted for cognate motif instances but are enriched for motif instances of other lineage-specific regulators is consistent with the “TF collective” model proposed by Junion and colleagues [19]. Under this model, the cooperative binding of large numbers of TFs is driven by the presence of motifs for a subset of lineage-specific factors that drive recruitment of the rest (i.e. the motifs for some TFs need not always be present).

To further support the “TF collective” interpretation of SeqUnwinder’s results, we tested the degree to which TSS-distal cell-type-specific sites are bound by numerous other TFs. We first defined a binding site’s “collective degree” as the number of distinct TFs with nearby ChIP-seq peaks. To calculate collective degree, we used a total of 158, 102, and 202 ChIP-seq datasets in GM12878, H1-hESC, and K562 cell-types, respectively. From Fig 5C, it is clear that distal K562- and GM12878-specific sites lacking a cognate motif instance have higher collective degrees. Similar findings were previously identified at the “high occupancy of transcription-related factors (HOT)” regions [20].

SeqUnwinder identifies sequence features at shared and cell-specific DHS in six different ENCODE cell-lines

Finally, we aim to demonstrate the utility of SeqUnwinder in identifying sequence features at large numbers of genomic loci annotated with several labels. We first annotated a large collection of DNase I hypersensitive (DHS) sites with six cell-line labels depending on the enrichment of DNase-seq reads (Fig 6A). If we had used analysis methods that rely on mutually exclusive categories, we would need to restrict analysis to ~97,000 sites labeled as either shared or exclusive to one of the six cell types [21]. Indeed, these strict category definitions may introduce sequence composition biases into each category. However, by taking advantage of SeqUnwinder’s unique framework to pool information from all subclasses, we can analyze ~140,000 DHS sites that we annotate into 22 subclasses as shared (i.e. enriched in 5 or more cell types) or specific to one or two cell types (Fig 6A, S3 Table).

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Fig 6. Discriminative sequence feature analysis at DHS sites in 6 different ENCODE cell-lines using SeqUnwinder.

(A) ~140K DHS sites annotated with 6 different cell-line labels used to identify cell-line specific and shared sequence features. (B) Label-specific scores of all the de novo motifs identified at DHSs sites in “A”. For consistency across figures, we fix the color saturation values to -0.4 and 0.4.

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SeqUnwinder identifies several motifs in this large collection of DHS sites, including those previously associated with specific cell-types [22–24] (Fig 6B). For example, components of the CTCF motif were highly predictive of shared DHS sites. This result is consistent with previous findings suggesting relatively invariant CTCF binding across cellular contexts [25,26]. RUNX, IRF and NF-κB motifs were enriched at GM12878-specific DHS sites. These motifs were also discovered by others at GM12878-specific DHS sites [5,23]. Motifs corresponding to GATA TFs, key regulators of erythroid development [27–29], were enriched at K562-specific DHS sites. SNAI and TEAD motifs were enriched at H1-hESC sites, consistent with previous observations [5]. JUND and FOS motifs were enriched at HeLa-S3-specific DHS sites. Motifs for HNF4A and FOX TFs, known master regulator of hepatocytes [30–33], were enriched at HepG2-specific DHS sites. Finally, motifs belonging to the ETS class of TFs were enriched at HUVEC-specific DHS sites (Fig 6B). ETS factors have been shown to directly convert human fibroblasts to endothelial cells [34]. Interestingly, some of the motifs associated with cell-type specific DHS sites were also found in our analyses of cell-type specific TF binding sites above (Fig 5B). For example, IRF, GATA, and TEAD motifs associated with GM12878, K562, and H1-hESC specific DHSs were also predictive of corresponding cell-type specific binding for many of the analyzed TFs.

These results demonstrate that SeqUnwinder scales effectively in characterizing sequence features at thousands of regulatory regions annotated by several different overlapping labels.

SeqUnwinder model

The core of SeqUnwinder is a multi-class logistic regression classifier trained on subclasses of genomic sites. The training features for the classifier are based on k-mer frequencies in a fixed window around input loci. The value or range of k is user-definable in the SeqUnwinder software, but all analyses in this work use models based on all 4-mers and 5-mers. When counting frequencies, we map each k-mer to the same entry as its reverse complement. To account for differences in the ranges of k-mer frequencies, we standardize the feature vectors such that each k-mer has a zero mean and unit variance across the entire training dataset.

The parameters of SeqUnwinder are k-mer weights for each subclass (combination of annotation labels). In addition, SeqUnwinder also models the label-specific k-mer weights by incorporating them in the L1 regularization term. Briefly, label-specific k-mer weights are encouraged to be similar to k-mer weights in all subclasses the label spans by regularizing on the differences of k-mer weights. Note that our approach is conceptually similar to hierarchical classification approaches such as that described by [45], although we use L1 regularization as opposed to L2.

The overall objective function of SeqUnwinder is: - (1) In the above equation; M is the total number of genomic loci in all subclasses, T is the set of all subclasses, b_i is the weight given to the genomic site i, w_n is the k-mer weight vector for subclass n, x_i is a vector of k-mer counts for the genomic site i, y_in is a binary indicator variable denoting the subclass of genomic site i, λ is the regularization co-efficient, ∏(n) is the set of all labels spanning the subclass n, and w_p is the k-mer weight vector for label p. Values for b_i are chosen to account for class imbalances. Hence, the value of b_i for a genomic site i belonging to class n is defined as |n_max|/|n|, where |n| denotes the number of genomic sites in subclass n and |n_max| denotes the number of genomic sites in the subclass with maximum sites.

Training the SeqUnwinder model

The w_n and w_p update steps separate out and are iteratively updated until convergence. The w_p update step has a simple closed form solution given by the equation: Where is the k^th term of the label-p weight vector. is a set of the k^th terms of the weight vectors of all the subclasses the label p spans.

The w_n update step is: - The above equation is solved using the scaled alternating direction method of multipliers (ADMM) framework [12]. Briefly, the ADMM framework splits the above problem into 2 smaller sub-problems, which are much easier to solve. ADMM introduces an additional variable z_np initialized as follows w_n and z_np are iteratively estimated until convergence of the ADMM algorithm.

Sub-problem 1: Where u_np is the scaled dual variable. The above sub-problem is solved using the LBFGS (limited-memory Broyden Fletcher Goldfarb Shanno) algorithm [46].

Sub-problem 2: The solution to the above equation is given by the shrinkage function defined as follows: - The update step for the scaled dual variable u_np is: - , , and are iteratively estimated until convergence. The stopping criteria for the ADMM algorithm is: and Where ϵ^abs and ϵ^rel are the absolute and relative tolerance, respectively. Of note, to speed up the implementation of SeqUnwinder, a distributed version of ADMM was implemented. Intuitively, the update step is distributed across multiple threads by splitting the M training examples into smaller subsets. The and the update steps act as pooling steps where the estimates of different threads are averaged. To further speed up convergence, a relaxed version of ADMM was implemented as described in [12]. In the relaxed version, is replaced by for the and update steps, where α is the over-relaxation parameter and is set to 1.9 as suggested in [12].

Converting weighted k-mer models into interpretable sequence features

While SeqUnwinder models label-specific sequence features using high-dimensional k-mer weight vectors, it is often desirable to visualize these sequence features in terms of a collection of interpretable position-specific scoring matrices. To do so, we use a combination of k-mer model scanning and local motif-finding in an approach similar to that used by SeqGL for producing interpretable motifs [5]. Specifically, we first scan the k-mer models learned during the training process across fixed-sized sequence windows around the input genomic loci to identify local high-scoring regions called “hills”. Label-specific hills are focused regions ranging from 10 to 15bp and are composed of high scoring k-mers. We use a threshold of 0.1 to define hills. Next, we cluster the hills using K-means clustering with Euclidean distance metric and k-mer counts as features. To speed-up implementation, we restrict the unbiased k-mer features to only those k-mers that are present in at least 5% of the hills. We use silhouette index [47] to choose the appropriate value for K. Briefly, we test a range of K values from 2 to 6. For each value of K, we calculate the silhouette index on 30 bootstrap samples. The value of K with highest median silhouette index is chosen as the best value for K. Finally, any clusters with membership size (i.e. numbers of clustered hills) less than 10% of the largest cluster’s membership size are merged with their next closest cluster.

MEME [48] is used to identify motifs in different clusters resulting in label-specific discriminative motifs. Each k-mer model further scores MEME-identified motifs as follows: where j ∈ motif_x is the set of all k-mers that belong to motif “motif_x”. Note that the heatmaps in each figure which display these label-specific discriminative scores have been generated with a shared color scheme; i.e., the maximum shade of yellow is defined to correspond to a model-specific score of +0.4, while the maximum shade of blue is set to a score of -0.4. In each figure, individual motifs sometimes have scores outside of these bounds, but we chose to use a shared color scheme for consistency of interpretation across figures.

In our experience, the above “hill-finding” method provides a convenient way to convert high-dimensional k-mer models into interpretable position-specific scoring matrices, and is less error-prone than alternative k-mer clustering or assembly approaches. One advantage of the “hill-finding” approach is that it implicitly takes into account positional relationships between high-scoring k-mers on the genome; short stretches that contain multiple high-scoring k-mers will form larger “hills”. Focused motif searches in the hills thus can find motifs that are longer than the longest k-mers in the underlying SeqUnwinder model.

Generation of synthetic datasets

To test SeqUnwinder in simulated settings, we generated various synthetic datasets. The sizes of simulated datasets (6,000–9,000 sequences) were chosen to roughly reflect the number of peaks in a typical ChIP-seq dataset. First, we generated 150bp sequences by sampling from a 2^nd-order Markov model of the human genome. Our use of a 2^nd-order Markov model is motivated by a desire to capture typical di- and tri-nucleotide compositional biases of vertebrate genomes (e.g. CG dinucleotide depletion and poly-A tracts). The exact choice of order of the background Markov model (i.e. 2^nd-order versus a higher order) is arbitrary, but should not be expected to affect the relative performances of the methods in correctly associating embedded motifs with correct labels.

Next, we randomly assigned labels to the simulated sequences at different frequencies. The overlap between the labels at the sequences was varied from 0.5 to 0.99. Arbitrarily chosen TF binding motifs were assigned to labels. Each motif instance was sampled from the probability density function defined by the PWM of the motif. Sampled motif instances were inserted at labeled sites at a frequency of 0.7.

Processing iMN programming data-sets

Processing ENCODE datasets

TF ChIP-seq datasets: We analyzed 17 TF ChIP-seq ENCODE datasets in three primary cell-lines (GM12878, K562, and H1-hESC). The binding profiles for the factors were profiled using MultiGPS [15]. All called binding events for TFs were required to have significant enrichment over corresponding input samples (q-value <0.01) as assessed using MultiGPS’ internal binomial test. For a site to be labeled as “shared”, the binding site was required to be called in all the 3 cell-lines. Further, binding sites showing significantly differential binding in any of the possible 3 pair-wise comparisons were removed from the shared set. Binding sites labeled as cell-type specific sites were required to have significantly higher ChIP enrichment compared to other cell-lines. All TF binding sites within 5Kbp of a known TSS (defined using UCSC hg19 gene annotations) were labeled as “promoter proximal”, while all sites that were more than 5Kbp from known TSSs were labeled as “distal”.

DNase-seq datasets: We analyzed the DHS sites at 6 different tier 1 and 2 ENCODE cell-lines (GM12878, K562, H1-hESC, HeLa-S3, HepG2, HUVEC). The DHS sites were called using in-house scripts. Briefly, contiguous 50bp genomic bins with significantly higher read enrichment compared to an input experiment were identified (binomial test, p-value < 0.01). Further, contiguous blocks within 200bp were stitched together to call enriched domains. A 150bp window around the maximum point of read density at enriched domains was considered as the DHS.

Annotation of de novo identified motifs

All de novo motifs identified using SeqUnwinder were annotated using the cis-bp database. Briefly, de novo motifs were matched against the cis-bp database using STAMP [49]. The best matching hit with a p-value of less than 10e-6 was used to name the de novo identified motifs.

Availability and reproducibility

SeqUnwinder is freely available under the MIT open source license from: https://github.com/seqcode/sequnwinder. Complete output files produced by the SeqUnwinder runs described in this work, along with scripts and data for reproducing all analysis figures, are available from: https://github.com/ikaka89/sequnwinderPaper.