SYSTEMS BIOLOGY OF TRANSCRIPTION REGULATION EDITED BY : Ekaterina Shelest, Edgar Wingender and Joerg Linde PUBLISHED IN : Frontiers in Genetics, Frontiers in Plant Science and Frontiers in Bioengineering and Biotechnology 1 September 2016 | Systems Biology of Transcription Regulation Frontiers Copyright Statement © Copyright 2007-2016 Frontiers Media SA. All rights reserved. All content included on this site, such as text, graphics, logos, button icons, images, video/audio clips, downloads, data compilations and software, is the property of or is licensed to Frontiers Media SA (“Frontiers”) or its licensees and/or subcontractors. The copyright in the text of individual articles is the property of their respective authors, subject to a license granted to Frontiers. The compilation of articles constituting this e-book, wherever published, as well as the compilation of all other content on this site, is the exclusive property of Frontiers. 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For the full conditions see the Conditions for Authors and the Conditions for Website Use. ISSN 1664-8714 ISBN 978-2-88919-967-9 DOI 10.3389/978-2-88919-967-9 About Frontiers Frontiers is more than just an open-access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers Journal Series The Frontiers Journal Series is a multi-tier and interdisciplinary set of open-access, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. 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Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: researchtopics@frontiersin.org 2 September 2016 | Systems Biology of Transcription Regulation SYSTEMS BIOLOGY OF TRANSCRIPTION REGULATION Topic Editors: Ekaterina Shelest, Hans-Knoell Institute, Germany Edgar Wingender, University of Göttingen, Germany Joerg Linde, Hans-Knoell Institute, Germany Transcription regulation is a complex process that can be considered and investigated from different perspectives. Traditionally and due to technical reasons (including the evolution of our understanding of the underlying processes) the main focus of the research was made on the regulation of expression through transcription factors (TFs), the proteins directly binding to DNA. On the other hand, intensive research is going on in the field of chromatin structure, remodeling and its involvement in the regulation. Whatever direction we select, we can speak about several levels of regulation. For instance, concentrating on TFs, we should consider multiple regulatory layers, starting with signaling pathways and ending up with the TF binding sites in the promoters and other regulatory regions. However, it is obvious that the TF regulation, also including the upstream processes, represents a modest portion of all processes leading to gene expression. For more comprehensive description of the gene regulation, we need a systematic and holistic view, which brings us to the importance of systems biology approaches. Advances in methodology, especially in high-throughput methods, result in an ever-growing mass of data, which in many cases is still waiting for appropriate consideration. Moreover, the accumulation of data is going faster than the development of algorithms for their systematic evaluation. Data and methods integration is indispensable for the acquiring a systematic as well as a systemic view. In addition to the huge amount of molecular or genetic components of a biological system, the even larger number of their interactions constitutes the enormous complexity of processes occurring in a living cell (organ, organism). In systems biology, these interactions are represented by networks. Transcriptional or, more generally, gene regulatory networks are being generated from experi- mental ChIPseq data, by reverse engineering from transcriptomics data, or from computational predictions of transcription factor (TF) – target gene relations. While transcriptional networks are now available for many biological systems, mathematical models to simulate their dynamic behavior have been successfully developed for metabolic and, to some extent, for signaling networks, but relatively rarely for gene regulatory networks. Systems biology approaches provide new perspectives that raise new questions. Some of them address methodological problems, others arise from the newly obtained understanding of the data. These open questions and problems are also a subject of this Research Topic. Citation: Shelest, E., Wingender, E., Linde, J., eds. (2016). Systems Biology of Transcription Regulation. Lausanne: Frontiers Media. doi: 10.3389/978-2-88919-967-9 3 September 2016 | Systems Biology of Transcription Regulation Table of Contents 05 Editorial: Systems Biology of Transcription Regulation Ekaterina Shelest and Edgar Wingender Chapter I 07 On accounting for sequence-specific bias in genome-wide chromatin accessibility experiments: recent advances and contradictions Pedro Madrigal 11 Analysis of Genomic Sequence Motifs for Deciphering Transcription Factor Binding and Transcriptional Regulation in Eukaryotic Cells Valentina Boeva 26 Computational Detection of Stage-Specific Transcription Factor Clusters during Heart Development Sebastian Zeidler, Cornelia Meckbach, Rebecca Tacke, Farah S. Raad, Angelica Roa, Shizuka Uchida, Wolfram-Hubertus Zimmermann, Edgar Wingender and Mehmet Gültas 43 Computational Identification of Key Regulators in Two Different Colorectal Cancer Cell Lines Darius Wlochowitz, Martin Haubrock, Jetcy Arackal, Annalen Bleckmann, Alexander Wolff, Tim Beißbarth, Edgar Wingender and Mehmet Gültas 67 A De novo Transcriptomic Approach to Identify Flavonoids and Anthocyanins “Switch-Off” in Olive ( Olea europaea L.) Drupes at Different Stages of Maturation Domenico L. Iaria, Adriana Chiappetta and Innocenzo Muzzalupo 79 Transcriptional Regulatory Network Analysis of MYB Transcription Factor Family Genes in Rice Shuchi Smita, Amit Katiyar, Viswanathan Chinnusamy, Dev M. Pandey and Kailash C. Bansal Chapter II 98 Decoding Cellular Dynamics in Epidermal Growth Factor Signaling Using a New Pathway-Based Integration Approach for Proteomics and Transcriptomics Data Astrid Wachter and Tim Beißbarth 114 Boolean Modeling Reveals the Necessity of Transcriptional Regulation for Bistability in PC12 Cell Differentiation Barbara Offermann, Steffen Knauer, Amit Singh, María L. Fernández-Cachón, Martin Klose, Silke Kowar, Hauke Busch and Melanie Boerries 4 September 2016 | Systems Biology of Transcription Regulation 129 ROMA: Representation and Quantification of Module Activity from Target Expression Data Loredana Martignetti, Laurence Calzone, Eric Bonnet, Emmanuel Barillot and Andrei Zinovyev 141 Mapping Mammalian Cell-type-specific Transcriptional Regulatory Networks Using KD-CAGE and ChIP-seq Data in the TC-YIK Cell Line Marina Lizio, Yuri Ishizu, Masayoshi Itoh, Timo Lassmann, Akira Hasegawa, Atsutaka Kubosaki, Jessica Severin, Hideya Kawaji, Yukio Nakamura, the FANTOM consortium, Harukazu Suzuki, Yoshihide Hayashizaki, Piero Carninci and Alistair R. R. Forrest Chapter III 158 Mechanisms of mutational robustness in transcriptional regulation Joshua L. Payne and Andreas Wagner 168 Robustness and Accuracy in Sea Urchin Developmental Gene Regulatory Networks Smadar Ben-Tabou de-Leon 174 A Consensus Network of Gene Regulatory Factors in the Human Frontal Lobe Stefano Berto, Alvaro Perdomo-Sabogal, Daniel Gerighausen, Jing Qin and Katja Nowick EDITORIAL published: 06 July 2016 doi: 10.3389/fgene.2016.00124 Frontiers in Genetics | www.frontiersin.org July 2016 | Volume 7 | Article 124 | Edited by: Richard D. Emes, University of Nottingham, UK Reviewed by: Ka-Chun Wong, City University of Hong Kong, China *Correspondence: Ekaterina Shelest ekaterina.shelest@hki-jena.de Specialty section: This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Genetics Received: 31 May 2016 Accepted: 22 June 2016 Published: 06 July 2016 Citation: Shelest E and Wingender E (2016) Editorial: Systems Biology of Transcription Regulation. Front. Genet. 7:124. doi: 10.3389/fgene.2016.00124 Editorial: Systems Biology of Transcription Regulation Ekaterina Shelest 1 * and Edgar Wingender 2 1 Leibniz Institute for Natural Product Research and Infection Biology, Hans-Knoell Institute, Jena, Germany, 2 Institute of Bioinformatics, University Medical Center Goettingen, Goettingen, Germany Keywords: systems biology, transcription regulation, regulatory networks, modeling The Editorial on the Research Topic Systems Biology of Transcription Regulation Systems biology (SB) is a holistic approach, an attempt to view a living system in its integrity. A system is thus considered as more than just a sum of its parts; interactions bring their flavor. Transcription regulation is in a way ideal for application of systems biology approaches, because it is complex and because it is a regulatory system. The latter puts it right in the middle of SB efforts, because regulation is central to any system: without regulation a system loses connections, its “systemic” property. Focusing on SB of transcriptional regulation, as we do in this Research Topic, is not stepping back into a reductionist approach. The complete signature of gene activities, their control, and consequences rather represents the status of a living system, for instance a single cell, in a comprehensive way. Here, we are in a good position to investigate the properties and patterns of regulatory circuits on different levels, from transcription regulation networks (TRNs) and signaling pathways to intercellular crosstalk, development, and further to physiological function on tissue and organism level—to that extent in which it depends on gene expression and its regulation. That is more or less a perspective. Systems biology of transcription regulation, as any other systems biology, is not yet a field with a well-established set of standard methods. It is also not a field with well-defined borders and unambiguously understood content. On the one hand, the subject is too complex and simultaneously too broad, which opens a wide field of activity. On the other hand, regulation of transcription is since long in the focus of intensive research and understanding of some (usually quite narrow) parts of it is very much advanced. There is also a historical bias toward some “favorite” processes, model organisms, where we can find examples of amazing advances; however, for other, not yet well investigated processes we are often just at the stage of collecting “bricks” from which the future building of our understanding will be constructed. This status of the SB of transcription regulation is reflected by the collection of articles in this issue. We can see the variety of views, methods, applications, and questions raised and answered: from application of state-of-the-art methods to a particular object (e.g., Wlochowitz et al.) to development of novel methods (Wachter and Beissbarth; Martignetti et al.), from discussions of critical methodological and technical issues (e.g., Madrigal) to detailed analysis of robustness mechanisms (Payne and Wagner), from first descriptions of pathways in a non-model plant (Iaria et al.) to advanced SB in well-established models (e.g., Ben-Tabou de-Leon, etc.). Let us briefly go through this collection. For transcription regulation, at least in the part considering transcription factors (TFs), TF binding sites (TFBSs) form the basis of the pyramid. Boeva in her review leads us through the forest of existing tools for prediction of motifs and TFBSs, demonstrating in the end how application of these methods can improve the accuracy of peak-calling in CHIPSeq. TFBSs are also in the focus of the investigation of heart development regulation (Zeidler et al.). The findings suggest that TF interactions are stage-specific and support the hourglass model of heart development. Wlochowitz et al. apply the state-of-the-art tools, such as Trinity (Grabherr et al., 2011) and geneXplain (http://genexplain-platform.com/bioumlweb/), 5 Shelest and Wingender Editorial: Systems Biology of Transcription Regulation to find differences between two cancer cell lines in terms of master TFs and signaling pathways. Analyzing gene regulatory networks (GRNs) and pathway interplays, the authors come to the explanation of the invasive potential of different cancer. Transcriptome analysis is also central for the papers of Iaria et al. and Smita et al. In the former, gene expression was monitored during maturation of fruits in two olive cultivars, followed by comparative analysis and reconstruction of metabolic pathways involved in olive drupe development. This is a nice example of tissue-specific functional genomics in a non-model plant species. Smita et al. used “top-down” and “guide-gene” approaches to study transcriptome-based GRN of MYB TFs in rice. The observations of differential regulation of all 233 rice MYBs in GEO-derived microarray data along with the phylogenetic analysis demonstrated that phylogenetically close pairs of MYB TFs are involved in highly similar regulatory processes. Bringing together different data layers is a typical SB challenge. In our Topic, we have two papers suggesting interesting approaches to it. Wachter and Beissbarth draw our attention to the fact that a lot of cellular signaling information is encoded in signaling dynamics. To take this into account, the authors suggest a novel pathway-based method for the analysis of coupled omics time-series data through inferring consensus profiles and time profile clusters. Another approach suggested by Offermann et al. is based on dynamic Boolean models inferred from time- resolved transcriptomes, protein, and phenotypic data. The models can be further optimized by fitting to experimental data and finally can describe temporal resolution of network events (regulation–transcription–feedback). Interestingly, in both papers the methods were applied to describe the same pathway, epidermal growth factor (EGF) signaling. Some new promising interactions were suggested by the first method. In the second application, EGF was confronted with NGF signaling with a very interesting outcome, suggesting that positive transcriptional feedback induces bistability in the switch between differentiation and proliferation, moreover, differentiation uses three redundant pathways. A less typical problem is tackled by Martignetti et al.: how to estimate activity of genes based on expression data, for instance the activity of a TF from expression of its target genes? For that, the authors developed a software ROMA for quantification of the activity of gene sets with coordinated expression. Application examples demonstrate that the activity of a signaling pathway is better reflected by the set of regulated genes than by any of these genes taken individually, which is an important message for future SB applications. The paper of Lizio et al. introduces experimental strategies to build cell-type specific TRNs. The authors use complementary approaches (CHIPseq, KD-CAGE) to identify genome-wide targets of genes of interest and warn about the problems that may arise by the usage of CHIPseq alone. This critical view is very important. Another kind of concern is expressed in the opinion paper of Madrigal, who raises a discussion of such serious issue as sequence-specific bias in chromatin assembly experiments. Indeed, this issue can be easily overlooked, and it is essential to be aware of the dangers of sequence (or any other) biases when designing an experiment or treating the results. Madrigal describes the types of bias in different analyses and the adequacy of current benchmarks. The problem of reproducibility of individual analyses is raised by Berto et al. To extract the most confident and biologically relevant information, the authors developed a method for integration of independently derived networks into a consensus network. This approach was applied to such complex and highly variable systems as cognitive disorders. Understanding of such properties as robustness can be only addressed from systemic perspective, making it central topic of several presented here papers. Payne and Wagner in their comprehensive review analyze the mechanisms of mutational robustness, discussing its causes and consequences. Another type of robustness—temporal control of developmental GRNs— is discussed by Ben-Tabou de-Leon. Analysis of network motifs helps us to understand how the network architecture supports the timely activation of regulatory and differentiation genes. Rigid motif combinations, such as a triple positive feedback loop conserved through bilateral, explain the robustness of the system, and suggest that this “approach” can be used in other systems as well. Altogether, this comprehensive collection of articles provides a nice overview of the present status of SB of transcription regulation, demonstrating the advances in different areas achieved through the application of SB approaches. AUTHOR CONTRIBUTIONS ES and EW have read all Research Topic articles, ES drafted the review, both authors wrote the paper and approved it for publication. ACKNOWLEDGMENTS This work was supported by the MetastaSys project (0316173A) within the ebio initiative of the German Ministry of Education and Research (BMBF). ES was supported by CRC 1127 ChemBioSys and CRC-Transregio FungiNet by Deutsche Forschungsgemeinschaft (DFG). REFERENCES Grabherr, M. G., Haas, B. J., Yassour, M., Levin, J. Z., Thompson, D. A., Amit, I., et al. (2011). Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29, 644–652. doi: 10.1038/nbt.1883 Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Copyright © 2016 Shelest and Wingender. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Frontiers in Genetics | www.frontiersin.org July 2016 | Volume 7 | Article 124 | 6 OPINION published: 22 September 2015 doi: 10.3389/fbioe.2015.00144 Edited by: Ekaterina Shelest, Leibniz Institute for Natural Product Research and Infection Biology – Hans-Knoell Institute, Germany Reviewed by: Gaurav Sablok, Istituto Agrario San Michele, Italy Uwe Ohler, Max Delbrueck Center, Germany *Correspondence: Pedro Madrigal pm12@sanger.ac.uk Specialty section: This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Bioengineering and Biotechnology Received: 14 June 2015 Accepted: 07 September 2015 Published: 22 September 2015 Citation: Madrigal P (2015) On accounting for sequence-specific bias in genome-wide chromatin accessibility experiments: recent advances and contradictions. Front. Bioeng. Biotechnol. 3:144. doi: 10.3389/fbioe.2015.00144 On accounting for sequence-specific bias in genome-wide chromatin accessibility experiments: recent advances and contradictions Pedro Madrigal 1,2 * 1 Wellcome Trust Sanger Institute, Cambridge, UK, 2 Department of Surgery, University of Cambridge, Cambridge, UK Keywords: next-generation sequencing, DNase-seq, ATAC-seq, chromatin accessibility, footprinting, sequence bias, ChIP-exo Next-Generation Sequencing for Chromatin Biology Uncovering the protein–DNA interactions involved in cell fate, development, and disease in a time- and cell-specific manner is a fundamental goal of molecular biology. The advent of the sequencing technologies has opened a new genomic era, uncovering the information encoded in genomes, epigenomes, and transcriptomes (McPherson, 2014). For example, the popular ChIP- based techniques ChIP-seq (Johnson et al., 2007; Robertson et al., 2007) and ChIP-exo (Rhee and Pugh, 2011) are widely used to detect transcription factor (TF)-binding sites using an antibody against a single protein of interest (Mahony and Pugh, 2015). Alternative protocols assaying the chromatin landscape, such as those based on digestion by DNase I enzyme (DNase-seq), micrococcal nuclease (MNase-seq), and Tn5 transposase attack (ATAC-seq), enable the identification of DNA- binding protein footprints of many TFs in a single experiment (Tsompana and Buck, 2014). Time- series experiments might be required for the identification of those TFs cataloged as pioneer factors, allowing their effects on chromatin to be investigated (Zaret and Carroll, 2011; Pajoro et al., 2014; Sherwood et al., 2014). Despite the initial promise of detecting the majority of TFs in one assay, DNA sequence- specific biases, together with TF-dependent binding kinetics, have been recently pinpointed as major confounding factors in DNase-seq experiments (Koohy et al., 2013; He et al., 2014; Raj and McVicker, 2014; Rusk, 2014; Sung et al., 2014). These influencing factors were not considered by any of the previous computational approaches for the analysis of next-generation sequencing chromatin accessibility data (Madrigal and Krajewski, 2012); neither those strategies based on TF- generic DNase signature nor those based on TF-specific DNase signature (Luo and Hartemink, 2013). Alleviating Sequence-Specific Biases in DNase-seq To partly address these challenges, four recent approaches have been published that model, predict, or explain DNase I sequence specificity in order to improve the detection of TF occupancy events at high resolution (digital genomic footprinting). The first method, FootprintMixture, uses a multinomial mixture model in which one mixture models the footprint component, and the other the background component taking into account the sequence bias (Yardimci et al., 2014). The background can be either uniform or derived from naked DNA measurements – this is the main difference with respect to the footprint component in CENTIPEDE (Pique-Regi et al., 2011), which assumes a uniform background. Alternatively, more than two components may be set to detect variability in the footprint model. Thus, the cleavage signature (number of DNase I cuts that map Frontiers in Bioengineering and Biotechnology | www.frontiersin.org September 2015 | Volume 3 | Article 144 | 7 Madrigal Sequence-bias correction in chromatin assays to each nucleotide) is used in a multinomial mixture model to classify candidate sites as either “bound” or “unbound” aided by 6-mer DNase sequence bias cleavage frequencies (Yardimci et al., 2014). Remarkably, the authors found that sequence bias is DNase-seq protocol specific. They also found that the sig- nature of a footprint could be formed by a mixture of DNase digestion profiles identified by unsupervised k -means clustering, in agreement with the observations found in an earlier study (Tewari et al., 2012). For TFs CTCF and ZNF143, variants of the consensus sequence motif associated to different footprint shapes were observed. In the second, the DNase2TF algorithm is able to correct din- ucleotide bias, detecting footprints with accuracy better or com- parable to existing approaches (Sung et al., 2014). Furthermore, Sung et al. (2014) were able to predict DNase signatures using solely tetranucleotide frequency information. Although this 4- nucleotide region has the highest information content, Koohy et al. (2013) and Lazarovici et al. (2013) demonstrated information beyond a context longer than four nucleotides. Consequently, using naked (deproteinized) DNA control datasets specific to a protocol and an enzyme as well as high sequencing depth (Hes- selberth et al., 2009) are now suggested recommendations for DNase-seq experiments aiming to detect footprints (Meyer and Liu, 2014). A third approach, an improved version of HINT [HMM-based identification of TF footprints (Gusmao et al., 2014)], named as HINT-BC/HINT-BCN (Bias Correction based on hypersensitivity sites/Bias Correction based on Naked DNase-seq) includes k -mer based bias correction in DNase-seq data as in He et al. (2014), leading to substantial changes in the average DNase I cleavage patterns surrounding the TFs. These changes result beneficial to footprinting method accuracy (personal communication with the author). Contradictorily, a fourth study using DNase-seq has shown that bias correction does not significantly improve the accuracy of TF binding identification (Kähärä and Lähdesmäki, 2015). In addition, this study poses a second counterintuitive idea in the field: accuracy saturates at a modest sequencing depth (30–60 million reads), and only a few TFs present improvement at deeper sequencing. ATAC-seq Shows Sequence Cleavage Bias It is unknown if ATAC-seq derived footprints are factor depen- dent or affected by Tn5 cleavage preferences (Tsompana and Buck, 2014). As expected, bioinformatic analysis of chromo- some 22 in the published human datasets for 50,000 cells reveals sequence biases in ATAC-seq experiments (Buenrostro et al., 2013) ( Figure 1 ), similar to those found by Koohy et al. (2013) in DNase-seq. As ATAC-seq might replace DNase-seq in the fore- seeable future due to its cost and time efficiencies, and because it simultaneously allows the identification of nucleosome positions (Buenrostro et al., 2013), new computational models are necessary to evaluate intrinsic confounding factors in ATAC-seq. A novel approach, msCentipede (Raj et al., 2014), has extended CENTIPEDE (Pique-Regi et al., 2011) from a mutinomial model to a hierarchical multiscale model. It has been evaluated on “single-hit” UW DNase-seq (Hesselberth et al., 2009) and on paired-end (PE) ATAC-seq data. Surprisingly, the “flexible model” for background DNase I cleavage rate (msCentipede- flexbg) shows very little improvement for a broad range of fac- tors when taking into account naked DNA information from Lazarovici et al. (2013) datasets. This finding clearly contradicts those of He et al. (2014) and Sung et al. (2014). In msCen- tipede, the footprint signature (or cleavage profile) pattern within a factor-bound motif instance was, therefore, found to be infor- mative when increasing the sensitivity and specificity of the TF binding site prediction. Raj et al. (2014) suggest that this might be explained by the different range of read count data between the matched consensus sequence of the candidate site/motif (10–30 bp) and the data matrix used typically by the software packages (larger sequence window, around 100–150 bp extension at each flank of the motif), which can mask the effects produced by not accounting for sequence biases within the core motif. Are Current Benchmarks Adequate to Evaluate Bias-Corrected DNase-seq Data? So far, a footprint of a TF, therefore, might be either detectable (and better detectable when accounting, or not, for influencing factors), or undetectable. In many studies, both problems are FIGURE 1 | Tn5 transposase shows sequence cleavage bias . Data represented correspond to read-start sites in reads aligned to forward and reverse strands in chromosome 22 in four ATAC-seq replicates (50 k cells per replicate) reported in Buenrostro et al. (2013). Of total, 50 bp PE reads were pre-processed with Trimmomatic v0.32 under default parameters, and then aligned to hg19 using BWA v0.7.4-r385 (Li and Durbin, 2010; Bolger et al., 2014). Sequence logos were generated using WebLogo (Crooks et al., 2004). Y -axis: 0.0–0.3 bits. Frontiers in Bioengineering and Biotechnology | www.frontiersin.org September 2015 | Volume 3 | Article 144 | 8 Madrigal Sequence-bias correction in chromatin assays convoluted and addressed using the same “gold standard” datasets, such as ChIP-seq, which do not have nucleotide-level resolution. Hence, on these methods and gold standards, no repro- ducible improvements can be seen. This was already noted in Cuellar-Partida et al. (2012), when it was showed that simply scanning for position weight matrices in DNase I hypersensi- tive sites (DHSs) had the same power as CENTIPEDE. These issues also complicate data integration with TF ChIP-seq, as peaks without a footprint in DNase-seq/ATAC-seq, considered weak/indirect binding or false positives (ChIP artifacts), might instead be explained by a class of TFs with rapid kinetics. And vice versa, DNase I cleavage patterns located within “ChIP-seq unbound” sites – noted previously, e.g., in the MILLIPEDE frame- work, especially in yeast (Luo and Hartemink, 2013) – could support the hypothesis of footprint shape dominated by DNA sequence specificities. Future Directions There is room for improvement in current methodologies by mak- ing use of the sequence specificity of each enzyme/assay, including ATAC-seq, but there is no clear consensus in its importance for digital genomic footprinting. This situation is not exclusive for genome-wide chromatin accessibility experiments: modeling the sequence-specific lambda exonuclease bias in ChIP-exo did not significantly increase the identification of TF binding sites (Wang et al., 2014). Similarly, there is no clear consensus if footprint signatures at the core motif, whether they are unique or not for an individual factor, are really important for footprint identification. Establishing better benchmarks to compare performance of the algorithms across different protocols is a fundamental task. These benchmarks could be based on “differential footprints” (sites within DHSs that are bound by a factor in one condition but not the other) as a more appropriate metric to evaluate foot- print identification performance instead of using ChIP-seq data (Yardimci et al., 2014). In addition, are DNase-seq software tools equally applicable to ATAC-seq without modification? If enzyme- specific biases are taken into account in a comparable experi- mental set-up, will DNase-seq and ATAC-seq report the same footprints for an identical sample using same algorithm param- eters? This is unlikely, based on a previous comparison between open chromatin DHSs and FAIRE sites, which revealed unique regions produced in each assay (Song et al., 2011). It has been also proposed that performing, and combining, experiments with different nucleases can be an alternative to mitigate biases (He et al., 2014; Mahony and Pugh, 2015). A greater challenge is dealing with proteins with very short residency time in the DNA as they produce mostly negligible footprints (Rusk, 2014; Sung et al., 2014). Optimizing and imple- menting new methods is necessary in order to enable biological insights that current methods cannot reveal. 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