Romulus: robust multi-state identification 
of transcription factor binding sites from 
DNase-seq data

Jankowski, Aleksander; Tiuryn, Jerzy; Prabhakar, Shyam

doi:10.1093/bioinformatics/btw209

Cited by 21 publications

(13 citation statements)

References 26 publications

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“…Second, the approach proposed here, like any of the other supervised approaches (Natarajan et al ., 2012; Arvey et al ., 2012; Luo and Hartemink, 2012; Kahara and Lahdesmaki, 2015; Kumar and Bucher, 2016; Quang and Xie, 2017; Liu et al ., 2017; Qin and Feng, 2017; Chen et al ., 2017), requires labeled training data for at least one cell type and the TF of interest to make predictions for this TF in another cell type. While the latter limitation is partly overcome by unsupervised approaches (Pique-Regi et al ., 2011; Sher-wood et al ., 2014; Gusmao et al ., 2014; Raj et al ., 2015; Jankowski et al ., 2016), this typically comes at the cost of reduced prediction accuracy (Kahara and Lahdesmaki, 2015; Liu et al ., 2017).…”

Section: Discussionmentioning

confidence: 99%

“…First, motif matches (i.e., predicted binding sites) may be used as prior information and combined with DNase-seq data to distinguish functional from non-functional binding sites (e.g., Pique-Regi et al . (2011); Jankowski et al . (2016); Raj et al .…”

Section: Supplementary Methodsmentioning

confidence: 99%

“…Following this path, a plenitude of tools (Supplementary Table S1; detailed discussion in Supplementary Text Supplementary Text S1) has been proposed over the last years (e.g., Pique-Regi et al (2011); Natarajan et al (2012); Arvey et al (2012); Luo and Hartemink (2012); Piper et al (2013); Sherwood et al (2014); Gusmao et al (2014); Raj et al (2015); Kähärä and Lähdesmäki (2015); Kumar and Bucher (2016); Jankowski et al (2016); Quang and Xie (2017); Liu et al (2017); Qin and Feng (2017); Schmidt et al (2017); Chen et al (2017)). While the general notion of combining sequence signals with chromatin accessibility data and, in some cases, other features is common to the majority of approaches, they differ in several aspects.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Learning from mistakes: Accurate prediction of cell type-specific transcription factor binding

Keilwagen

Posch

Grau

2017

Preprint

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Computational prediction of cell type-specific, in-vivo transcription factor 10 binding sites is still one of the central challenges in regulatory genomics, and 11 a variety of approaches has been proposed for this purpose. 12Here, we present our approach that earned a shared first rank in the 26To make predictions of this approach readily accessible, we predict 682 27 peak lists for a total of 31 transcription factors in 22 primary cell types and 28 tissues, which are available for download at https://www.synapse.org/#! 29 Synapse:syn11526239, and we demonstrate that these predictions may help 30 to yield biological conclusions.

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Section: Discussionmentioning

confidence: 99%

Section: Supplementary Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning from mistakes: Accurate prediction of cell type-specific transcription factor binding

Keilwagen

Posch

Grau

2017

Preprint

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“…To solve the problem, emerging studies demonstrated that the genome-wide TF and gene interactions can be accurately predicted using the open chromatin regions identified through DNase-seq (64) or ATAC-seq (65), thus providing an alternative way to establish the TF–lncRNA interactome without ChIP-seq.…”

Section: Discussionmentioning

confidence: 99%

LncFunNet: an integrated computational framework for identification of functional long noncoding RNAs in mouse skeletal muscle cells

Zhou

Zhang

Wang

et al. 2017

Nucleic Acids Research

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Long noncoding RNAs (lncRNAs) are key regulators of diverse cellular processes. Recent advances in high-throughput sequencing have allowed for an unprecedented discovery of novel lncRNAs. To identify functional lncRNAs from thousands of candidates for further functional validation is still a challenging task. Here, we present a novel computational framework, lncFunNet (lncRNA Functional inference through integrated Network) that integrates ChIP-seq, CLIP-seq and RNA-seq data to predict, prioritize and annotate lncRNA functions. In mouse embryonic stem cells (mESCs), using lncFunNet we not only recovered most of the functional lncRNAs known to maintain mESC pluripotency but also predicted a plethora of novel functional lncRNAs. Similarly, in mouse myoblast C2C12 cells, applying lncFunNet led to prediction of reservoirs of functional lncRNAs in both proliferating myoblasts (MBs) and differentiating myotubes (MTs). Further analyses demonstrated that these lncRNAs are frequently bound by key transcription factors, interact with miRNAs and constitute key nodes in biological network motifs. Further experimentations validated their dynamic expression profiles and functionality during myoblast differentiation. Collectively, our studies demonstrate the use of lncFunNet to annotate and identify functional lncRNAs in a given biological system.

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“…Interestingly, IRF4 and IRF5, which also have GAA-and GAAA-rich motif features similar to that of other IRFs ( Figure S4C), are found positioned in different areas of the network. IRF4, in particular, is localized to the extreme lower left ( Figure S4A; Cluster 1, Chromatin Modifiers) in proximity to pioneer factors and repressors that target large numbers of genes (Jankowski et al, 2016).…”

Section: Network Clustering Differential Gene Expression Analysis Amentioning

confidence: 99%

A comprehensive map of the dendritic cell transcriptional network engaged upon innate sensing of HIV

Johnson

Veaux

Rives

et al. 2019

Preprint

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Transcriptional programming of the innate immune response is pivotal for host protection. However, the transcriptional mechanisms that link pathogen sensing with innate activation remain poorly understood. During infection with HIV-1, human dendritic cells (DCs) can detect the virus through an innate sensing pathway leading to antiviral interferon and DC maturation. Here, we developed an iterative experimental and computational approach to map the innate response circuitry during HIV-1 infection. By integrating genome-wide chromatin accessibility with expression kinetics, we inferred a gene regulatory network that links 542 transcription factors with 21,862 target genes. We observed that an interferon response is required, yet insufficient to drive DC maturation, and identified PRDM1 and RARA as essential regulators of the interferon response and DC maturation, respectively. Our work provides a resource for interrogation of regulators of HIV replication and innate immunity, highlighting complexity and cooperativity in the regulatory circuit controlling the DC response to infection. Key Wordsnetwork inference, DNA sensing, innate signaling, cGAS, STING, IRF3, NF-κB, chromatin modification, ATAC-seq, RNA-seq of a TF's predicted gene targets ( Figure 3C). This step approximates the effect of unmeasured parameters, such as post-translational regulation and protein-protein interactions, on TF activity in a condition-dependent manner. We were thus able to model changes in TF activity that are semi-independent of changes in TF expression, as observed for IRF3 ( Figure S3A, S3B). IRF3 activity was predicted to increase in response to LPS, pIC and HIV sensing, which are all known to drive IFN production through IRF3 phosphorylation. Our inference model also predicted that activity and expression for STAT2, IRF7, and RELA correlated with innate stimulation, as expected, given their well-defined roles in the innate response (Cao, 2016).Once TF activity was estimated, the network structure prior was then used to bias model selection of TF-to-gene target regulatory interactions towards edges with prior information during network inference ( Figure 3D). To improve the overall performance and stability of network inference, several aspects of the method (and key inputs) were tested, such as: different sources of priors (publicly available ENCODE data vs our ATAC-seq data), different TF binding motif databases (HOCOMOCO (Kulakovskiy et al., 2013) vs CisBp 2.0 (Weirauch et al., 2014)), and different model selection methods (Bayesian Best Subset Regression (BBSR) vsElastic Net (EN)). Additionally, since every computational run is subject to stochasticity in the inference procedure, we evaluated whether network performance could be improved by combining hundreds of individual computation runs (selecting model components such as TF activity estimates that were stable across random subsamples of the structure prior; see STAR Methods). To evaluate the performance of networks inferred using the parameters described above, we used area...

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Romulus: robust multi-state identification of transcription factor binding sites from DNase-seq data

Cited by 21 publications

References 26 publications

Learning from mistakes: Accurate prediction of cell type-specific transcription factor binding

Learning from mistakes: Accurate prediction of cell type-specific transcription factor binding

LncFunNet: an integrated computational framework for identification of functional long noncoding RNAs in mouse skeletal muscle cells

A comprehensive map of the dendritic cell transcriptional network engaged upon innate sensing of HIV

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