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Kuffler L, Skelly DA, Czechanski A, Fortin HJ, Munger SC, Baker CL, Reinholdt LG, Carter GW. Imputation of 3D genome structure by genetic-epigenetic interaction modeling in mice. eLife 2024; 12:RP88222. [PMID: 38669177 PMCID: PMC11052574 DOI: 10.7554/elife.88222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2024] Open
Abstract
Gene expression is known to be affected by interactions between local genetic variation and DNA accessibility, with the latter organized into three-dimensional chromatin structures. Analyses of these interactions have previously been limited, obscuring their regulatory context, and the extent to which they occur throughout the genome. Here, we undertake a genome-scale analysis of these interactions in a genetically diverse population to systematically identify global genetic-epigenetic interaction, and reveal constraints imposed by chromatin structure. We establish the extent and structure of genotype-by-epigenotype interaction using embryonic stem cells derived from Diversity Outbred mice. This mouse population segregates millions of variants from eight inbred founders, enabling precision genetic mapping with extensive genotypic and phenotypic diversity. With 176 samples profiled for genotype, gene expression, and open chromatin, we used regression modeling to infer genetic-epigenetic interactions on a genome-wide scale. Our results demonstrate that statistical interactions between genetic variants and chromatin accessibility are common throughout the genome. We found that these interactions occur within the local area of the affected gene, and that this locality corresponds to topologically associated domains (TADs). The likelihood of interaction was most strongly defined by the three-dimensional (3D) domain structure rather than linear DNA sequence. We show that stable 3D genome structure is an effective tool to guide searches for regulatory elements and, conversely, that regulatory elements in genetically diverse populations provide a means to infer 3D genome structure. We confirmed this finding with CTCF ChIP-seq that revealed strain-specific binding in the inbred founder mice. In stem cells, open chromatin participating in the most significant regression models demonstrated an enrichment for developmental genes and the TAD-forming CTCF-binding complex, providing an opportunity for statistical inference of shifting TAD boundaries operating during early development. These findings provide evidence that genetic and epigenetic factors operate within the context of 3D chromatin structure.
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Liu Z, Wong HM, Chen X, Lin J, Zhang S, Yan S, Wang F, Li X, Wong KC. MotifHub: Detection of trans-acting DNA motif group with probabilistic modeling algorithm. Comput Biol Med 2024; 168:107753. [PMID: 38039889 DOI: 10.1016/j.compbiomed.2023.107753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 10/30/2023] [Accepted: 11/20/2023] [Indexed: 12/03/2023]
Abstract
BACKGROUND Trans-acting factors are of special importance in transcription regulation, which is a group of proteins that can directly or indirectly recognize or bind to the 8-12 bp core sequence of cis-acting elements and regulate the transcription efficiency of target genes. The progressive development in high-throughput chromatin capture technology (e.g., Hi-C) enables the identification of chromatin-interacting sequence groups where trans-acting DNA motif groups can be discovered. The problem difficulty lies in the combinatorial nature of DNA sequence pattern matching and its underlying sequence pattern search space. METHOD Here, we propose to develop MotifHub for trans-acting DNA motif group discovery on grouped sequences. Specifically, the main approach is to develop probabilistic modeling for accommodating the stochastic nature of DNA motif patterns. RESULTS Based on the modeling, we develop global sampling techniques based on EM and Gibbs sampling to address the global optimization challenge for model fitting with latent variables. The results reflect that our proposed approaches demonstrate promising performance with linear time complexities. CONCLUSION MotifHub is a novel algorithm considering the identification of both DNA co-binding motif groups and trans-acting TFs. Our study paves the way for identifying hub TFs of stem cell development (OCT4 and SOX2) and determining potential therapeutic targets of prostate cancer (FOXA1 and MYC). To ensure scientific reproducibility and long-term impact, its matrix-algebra-optimized source code is released at http://bioinfo.cs.cityu.edu.hk/MotifHub.
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Affiliation(s)
- Zhe Liu
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, China
| | - Hiu-Man Wong
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, China
| | - Xingjian Chen
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, China
| | - Jiecong Lin
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, China
| | - Shixiong Zhang
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, China
| | - Shankai Yan
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, China
| | - Fuzhou Wang
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, China
| | - Xiangtao Li
- School of Artificial Intelligence, Jilin University, Jilin, China
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, China.
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Wu X, Liu S, Liang G. Detecting clusters of transcription factors based on a nonhomogeneous poisson process model. BMC Bioinformatics 2022; 23:535. [PMID: 36494794 PMCID: PMC9738027 DOI: 10.1186/s12859-022-05090-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 11/30/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Rapidly growing genome-wide ChIP-seq data have provided unprecedented opportunities to explore transcription factor (TF) binding under various cellular conditions. Despite the rich resources, development of analytical methods for studying the interaction among TFs in gene regulation still lags behind. RESULTS In order to address cooperative TF binding and detect TF clusters with coordinative functions, we have developed novel computational methods based on clustering the sample paths of nonhomogeneous Poisson processes. Simulation studies demonstrated the capability of these methods to accurately detect TF clusters and uncover the hierarchy of TF interactions. A further application to the multiple-TF ChIP-seq data in mouse embryonic stem cells (ESCs) showed that our methods identified the cluster of core ESC regulators reported in the literature and provided new insights on functional implications of transcrisptional regulatory modules. CONCLUSIONS Effective analytical tools are essential for studying protein-DNA relations. Information derived from this research will help us better understand the orchestration of transcription factors in gene regulation processes.
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Affiliation(s)
- Xiaowei Wu
- grid.438526.e0000 0001 0694 4940Department of Statistics, Virginia Tech, 250 Drillfield Drive, Blacksburg, VA 24061 USA
| | - Shicheng Liu
- grid.438526.e0000 0001 0694 4940Department of Mathematics, Virginia Tech, 225 Stanger Street, Blacksburg, VA 24061 USA
| | - Guanying Liang
- grid.438526.e0000 0001 0694 4940Department of Mathematics, Virginia Tech, 225 Stanger Street, Blacksburg, VA 24061 USA
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REDfly: An Integrated Knowledgebase for Insect Regulatory Genomics. INSECTS 2022; 13:insects13070618. [PMID: 35886794 PMCID: PMC9323752 DOI: 10.3390/insects13070618] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/01/2022] [Accepted: 07/06/2022] [Indexed: 11/29/2022]
Abstract
Simple Summary Understanding how genes are regulated is a vital area of current biological research and a crucial adjunct to ongoing efforts to sequence entire genomes. Knowing the DNA sequences responsible for gene regulation—transcriptional cis-regulatory modules (CRMs, e.g., “enhancers”) and transcription factor binding sites (TFBSs)—is important for many areas of research including interpretation and validation of data developed by large-scale genomics projects, providing training data for machine-learning CRM-discovery methods, genome annotation, modeling gene-regulatory networks, studying the evolution of gene regulation, and numerous aspects of the basic biology of transcriptional regulation. Knowledge of insect CRMs is also an important step in developing biotechnology methods for control of insect disease vectors and for eliminating pathogen transmission. The REDfly (Regulatory Element Database for Fly) database integrates all of the available insect cis-regulatory information from multiple sources to provide a comprehensive collection of known regulatory elements. In this paper, we describe REDfly’s basic contents and data model, emphasizing recently added features, and provide illustrated walk-throughs of some common search scenarios. Abstract We provide here an updated description of the REDfly (Regulatory Element Database for Fly) database of transcriptional regulatory elements, a unique resource that provides regulatory annotation for the genome of Drosophila and other insects. The genomic sequences regulating insect gene expression—transcriptional cis-regulatory modules (CRMs, e.g., “enhancers”) and transcription factor binding sites (TFBSs)—are not currently curated by any other major database resources. However, knowledge of such sequences is important, as CRMs play critical roles with respect to disease as well as normal development, phenotypic variation, and evolution. Characterized CRMs also provide useful tools for both basic and applied research, including developing methods for insect control. REDfly, which is the most detailed existing platform for metazoan regulatory-element annotation, includes over 40,000 experimentally verified CRMs and TFBSs along with their DNA sequences, their associated genes, and the expression patterns they direct. Here, we briefly describe REDfly’s contents and data model, with an emphasis on the new features implemented since 2020. We then provide an illustrated walk-through of several common REDfly search use cases.
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Warren TL, Lambert JT, Nord AS. AAV Deployment of Enhancer-Based Expression Constructs In Vivo in Mouse Brain. J Vis Exp 2022:10.3791/62650. [PMID: 35435902 PMCID: PMC10010840 DOI: 10.3791/62650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
Enhancers are binding platforms for a diverse array of transcription factors that drive specific expression patterns of tissue- and cell-type-specific genes. Multiple means of assessing non-coding DNA and various chromatin states have proven useful in predicting the presence of enhancer sequences in the genome, but validating the activity of these sequences and finding the organs and developmental stages they are active in is a labor-intensive process. Recent advances in adeno-associated virus (AAV) vectors have enabled the widespread delivery of transgenes to mouse tissues, enabling in vivo enhancer testing without necessitating a transgenic animal. This protocol shows how a reporter construct that expresses EGFP under the control of a minimal promoter, which does not drive significant expression on its own, can be used to study the activity patterns of candidate enhancer sequences in the mouse brain. An AAV-packaged reporter construct is delivered to the mouse brain and incubated for 1-4 weeks, after which the animal is sacrificed, and brain sections are observed under a microscope. EGFP appears in cells in which the tested enhancer is sufficient to initiate gene expression, pinpointing the location and developmental stage in which the enhancer is active in the brain. Standard cloning methods, low-cost AAV packaging, and expanding AAV serotypes and methods for in vivo delivery and standard imaging readout make this an accessible approach for the study of how gene expression is regulated in the brain.
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Affiliation(s)
- Tracy L Warren
- Department of Psychiatry and Behavioral Sciences, University of California, Davis; Department of Neurobiology, Physiology and Behavior, University of California, Davis
| | - Jason T Lambert
- Department of Psychiatry and Behavioral Sciences, University of California, Davis; Department of Neurobiology, Physiology and Behavior, University of California, Davis;
| | - Alex S Nord
- Department of Psychiatry and Behavioral Sciences, University of California, Davis; Department of Neurobiology, Physiology and Behavior, University of California, Davis;
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Yang TH, Yang YC, Tu KC. regCNN: identifying Drosophila genome-wide cis-regulatory modules via integrating the local patterns in epigenetic marks and transcription factor binding motifs. Comput Struct Biotechnol J 2022; 20:296-308. [PMID: 35035784 PMCID: PMC8724954 DOI: 10.1016/j.csbj.2021.12.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/10/2021] [Accepted: 12/10/2021] [Indexed: 11/20/2022] Open
Abstract
Transcription regulation in metazoa is controlled by the binding events of transcription factors (TFs) or regulatory proteins on specific modular DNA regulatory sequences called cis-regulatory modules (CRMs). Understanding the distributions of CRMs on a genomic scale is essential for constructing the metazoan transcriptional regulatory networks that help diagnose genetic disorders. While traditional reporter-assay CRM identification approaches can provide an in-depth understanding of functions of some CRM, these methods are usually cost-inefficient and low-throughput. It is generally believed that by integrating diverse genomic data, reliable CRM predictions can be made. Hence, researchers often first resort to computational algorithms for genome-wide CRM screening before specific experiments. However, current existing in silico methods for searching potential CRMs were restricted by low sensitivity, poor prediction accuracy, or high computation time from TFBS composition combinatorial complexity. To overcome these obstacles, we designed a novel CRM identification pipeline called regCNN by considering the base-by-base local patterns in TF binding motifs and epigenetic profiles. On the test set, regCNN shows an accuracy/auROC of 84.5%/92.5% in CRM identification. And by further considering local patterns in epigenetic profiles and TF binding motifs, it can accomplish 4.7% (92.5%–87.8%) improvement in the auROC value over the average value-based pure multi-layer perceptron model. We also demonstrated that regCNN outperforms all currently available tools by at least 11.3% in auROC values. Finally, regCNN is verified to be robust against its resizing window hyperparameter in dealing with the variable lengths of CRMs. The model of regCNN can be downloaded athttp://cobisHSS0.im.nuk.edu.tw/regCNN/.
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Affiliation(s)
- Tzu-Hsien Yang
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
| | - Ya-Chiao Yang
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
| | - Kai-Chi Tu
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
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7
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Asma H, Halfon MS. Annotating the Insect Regulatory Genome. INSECTS 2021; 12:591. [PMID: 34209769 PMCID: PMC8305585 DOI: 10.3390/insects12070591] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 06/23/2021] [Accepted: 06/25/2021] [Indexed: 11/17/2022]
Abstract
An ever-growing number of insect genomes is being sequenced across the evolutionary spectrum. Comprehensive annotation of not only genes but also regulatory regions is critical for reaping the full benefits of this sequencing. Driven by developments in sequencing technologies and in both empirical and computational discovery strategies, the past few decades have witnessed dramatic progress in our ability to identify cis-regulatory modules (CRMs), sequences such as enhancers that play a major role in regulating transcription. Nevertheless, providing a timely and comprehensive regulatory annotation of newly sequenced insect genomes is an ongoing challenge. We review here the methods being used to identify CRMs in both model and non-model insect species, and focus on two tools that we have developed, REDfly and SCRMshaw. These resources can be paired together in a powerful combination to facilitate insect regulatory annotation over a broad range of species, with an accuracy equal to or better than that of other state-of-the-art methods.
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Affiliation(s)
- Hasiba Asma
- Program in Genetics, Genomics, and Bioinformatics, University at Buffalo-State University of New York, Buffalo, NY 14203, USA;
| | - Marc S. Halfon
- Program in Genetics, Genomics, and Bioinformatics, University at Buffalo-State University of New York, Buffalo, NY 14203, USA;
- Department of Biochemistry, University at Buffalo-State University of New York, Buffalo, NY 14203, USA
- Department of Biomedical Informatics, University at Buffalo-State University of New York, Buffalo, NY 14203, USA
- Department of Biological Sciences, University at Buffalo-State University of New York, Buffalo, NY 14203, USA
- NY State Center of Excellence in Bioinformatics & Life Sciences, Buffalo, NY 14203, USA
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8
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Talukder A, Barham C, Li X, Hu H. Interpretation of deep learning in genomics and epigenomics. Brief Bioinform 2021; 22:bbaa177. [PMID: 34020542 PMCID: PMC8138893 DOI: 10.1093/bib/bbaa177] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 06/26/2020] [Accepted: 07/10/2020] [Indexed: 12/17/2022] Open
Abstract
Machine learning methods have been widely applied to big data analysis in genomics and epigenomics research. Although accuracy and efficiency are common goals in many modeling tasks, model interpretability is especially important to these studies towards understanding the underlying molecular and cellular mechanisms. Deep neural networks (DNNs) have recently gained popularity in various types of genomic and epigenomic studies due to their capabilities in utilizing large-scale high-throughput bioinformatics data and achieving high accuracy in predictions and classifications. However, DNNs are often challenged by their potential to explain the predictions due to their black-box nature. In this review, we present current development in the model interpretation of DNNs, focusing on their applications in genomics and epigenomics. We first describe state-of-the-art DNN interpretation methods in representative machine learning fields. We then summarize the DNN interpretation methods in recent studies on genomics and epigenomics, focusing on current data- and computing-intensive topics such as sequence motif identification, genetic variations, gene expression, chromatin interactions and non-coding RNAs. We also present the biological discoveries that resulted from these interpretation methods. We finally discuss the advantages and limitations of current interpretation approaches in the context of genomic and epigenomic studies. Contact:xiaoman@mail.ucf.edu, haihu@cs.ucf.edu.
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Affiliation(s)
- Amlan Talukder
- Computer Science, University of Central Florida, Orlando, FL 32816, USA
| | - Clayton Barham
- Computer Science, University of Central Florida, Orlando, FL 32816, USA
| | - Xiaoman Li
- Burnett School of Biomedical Science, University of Central Florida, Orlando, FL 32816, USA
| | - Haiyan Hu
- Computer Science, University of Central Florida, Orlando, FL 32816, USA
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9
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Tobias IC, Abatti LE, Moorthy SD, Mullany S, Taylor T, Khader N, Filice MA, Mitchell JA. Transcriptional enhancers: from prediction to functional assessment on a genome-wide scale. Genome 2020; 64:426-448. [PMID: 32961076 DOI: 10.1139/gen-2020-0104] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Enhancers are cis-regulatory sequences located distally to target genes. These sequences consolidate developmental and environmental cues to coordinate gene expression in a tissue-specific manner. Enhancer function and tissue specificity depend on the expressed set of transcription factors, which recognize binding sites and recruit cofactors that regulate local chromatin organization and gene transcription. Unlike other genomic elements, enhancers are challenging to identify because they function independently of orientation, are often distant from their promoters, have poorly defined boundaries, and display no reading frame. In addition, there are no defined genetic or epigenetic features that are unambiguously associated with enhancer activity. Over recent years there have been developments in both empirical assays and computational methods for enhancer prediction. We review genome-wide tools, CRISPR advancements, and high-throughput screening approaches that have improved our ability to both observe and manipulate enhancers in vitro at the level of primary genetic sequences, chromatin states, and spatial interactions. We also highlight contemporary animal models and their importance to enhancer validation. Together, these experimental systems and techniques complement one another and broaden our understanding of enhancer function in development, evolution, and disease.
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Affiliation(s)
- Ian C Tobias
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
| | - Luis E Abatti
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
| | - Sakthi D Moorthy
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
| | - Shanelle Mullany
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
| | - Tiegh Taylor
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
| | - Nawrah Khader
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
| | - Mario A Filice
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
| | - Jennifer A Mitchell
- Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, ON, M5S 3G5, Canada
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10
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Osmala M, Lähdesmäki H. Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns. BMC Bioinformatics 2020; 21:317. [PMID: 32689977 PMCID: PMC7370432 DOI: 10.1186/s12859-020-03621-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 06/19/2020] [Indexed: 12/11/2022] Open
Abstract
Background The binding sites of transcription factors (TFs) and the localisation of histone modifications in the human genome can be quantified by the chromatin immunoprecipitation assay coupled with next-generation sequencing (ChIP-seq). The resulting chromatin feature data has been successfully adopted for genome-wide enhancer identification by several unsupervised and supervised machine learning methods. However, the current methods predict different numbers and different sets of enhancers for the same cell type and do not utilise the pattern of the ChIP-seq coverage profiles efficiently. Results In this work, we propose a PRobabilistic Enhancer PRedictIoN Tool (PREPRINT) that assumes characteristic coverage patterns of chromatin features at enhancers and employs a statistical model to account for their variability. PREPRINT defines probabilistic distance measures to quantify the similarity of the genomic query regions and the characteristic coverage patterns. The probabilistic scores of the enhancer and non-enhancer samples are utilised to train a kernel-based classifier. The performance of the method is demonstrated on ENCODE data for two cell lines. The predicted enhancers are computationally validated based on the transcriptional regulatory protein binding sites and compared to the predictions obtained by state-of-the-art methods. Conclusion PREPRINT performs favorably to the state-of-the-art methods, especially when requiring the methods to predict a larger set of enhancers. PREPRINT generalises successfully to data from cell type not utilised for training, and often the PREPRINT performs better than the previous methods. The PREPRINT enhancers are less sensitive to the choice of prediction threshold. PREPRINT identifies biologically validated enhancers not predicted by the competing methods. The enhancers predicted by PREPRINT can aid the genome interpretation in functional genomics and clinical studies.
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Affiliation(s)
- Maria Osmala
- Department of Computer Science, Aalto University, Konemiehentie 2, Espoo, 02150, Finland.
| | - Harri Lähdesmäki
- Department of Computer Science, Aalto University, Konemiehentie 2, Espoo, 02150, Finland
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11
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Chen X, Gu J, Neuwald AF, Hilakivi-Clarke L, Clarke R, Xuan J. BICORN: An R package for integrative inference of de novo cis-regulatory modules. Sci Rep 2020; 10:7960. [PMID: 32409786 PMCID: PMC7224214 DOI: 10.1038/s41598-020-63043-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 01/15/2020] [Indexed: 12/18/2022] Open
Abstract
Genome-wide transcription factor (TF) binding signal analyses reveal co-localization of TF binding sites based on inferred cis-regulatory modules (CRMs). CRMs play a key role in understanding the cooperation of multiple TFs under specific conditions. However, the functions of CRMs and their effects on nearby gene transcription are highly dynamic and context-specific and therefore are challenging to characterize. BICORN (Bayesian Inference of COoperative Regulatory Network) builds a hierarchical Bayesian model and infers context-specific CRMs based on TF-gene binding events and gene expression data for a particular cell type. BICORN automatically searches for a list of candidate CRMs based on the input TF bindings at regulatory regions associated with genes of interest. Applying Gibbs sampling, BICORN iteratively estimates model parameters of CRMs, TF activities, and corresponding regulation on gene transcription, which it models as a sparse network of functional CRMs regulating target genes. The BICORN package is implemented in R (version 3.4 or later) and is publicly available on the CRAN server at https://cran.r-project.org/web/packages/BICORN/index.html.
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Affiliation(s)
- Xi Chen
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, 22203, USA
| | - Jinghua Gu
- Baylor Research Institute, 3310 Live Oak St, Dallas, TX, 75204, USA
| | - Andrew F Neuwald
- Institute for Genome Sciences and Department Biochemistry & Molecular Biology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Leena Hilakivi-Clarke
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3970 Reservoir Road, Washington, DC, 20057, USA
| | - Robert Clarke
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, 3970 Reservoir Road, Washington, DC, 20057, USA
| | - Jianhua Xuan
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, 22203, USA.
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12
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Wang X, Zhou T, Wunderlich Z, Maurano MT, DePace AH, Nuzhdin SV, Rohs R. Analysis of Genetic Variation Indicates DNA Shape Involvement in Purifying Selection. Mol Biol Evol 2019; 35:1958-1967. [PMID: 29850830 PMCID: PMC6063282 DOI: 10.1093/molbev/msy099] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Noncoding DNA sequences, which play various roles in gene expression and regulation, are under evolutionary pressure. Gene regulation requires specific protein–DNA binding events, and our previous studies showed that both DNA sequence and shape readout are employed by transcription factors (TFs) to achieve DNA binding specificity. By investigating the shape-disrupting properties of single nucleotide polymorphisms (SNPs) in human regulatory regions, we established a link between disruptive local DNA shape changes and loss of specific TF binding. Furthermore, we described cases where disease-associated SNPs may alter TF binding through DNA shape changes. This link led us to hypothesize that local DNA shape within and around TF binding sites is under selection pressure. To verify this hypothesis, we analyzed SNP data derived from 216 natural strains of Drosophila melanogaster. Comparing SNPs located in functional and nonfunctional regions within experimentally validated cis-regulatory modules (CRMs) from D. melanogaster that are active in the blastoderm stage of development, we found that SNPs within functional regions tended to cause smaller DNA shape variations. Furthermore, SNPs with higher minor allele frequency were more likely to result in smaller DNA shape variations. The same analysis based on a large number of SNPs in putative CRMs of the D. melanogaster genome derived from DNase I accessibility data confirmed these observations. Taken together, our results indicate that common SNPs in functional regions tend to maintain DNA shape, whereas shape-disrupting SNPs are more likely to be eliminated through purifying selection.
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Affiliation(s)
- Xiaofei Wang
- Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA
| | - Tianyin Zhou
- Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA
| | - Zeba Wunderlich
- Department of Developmental and Cell Biology, University of California Irvine, Irvine, CA
| | - Matthew T Maurano
- Institute for Systems Genetics, New York University Medical Center, New York, NY
| | - Angela H DePace
- Department of Systems Biology, Harvard Medical School, Boston, MA
| | - Sergey V Nuzhdin
- Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA
| | - Remo Rohs
- Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA.,Departments of Chemistry, Physics and Astronomy, and Computer Science, University of Southern California, Los Angeles, CA
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13
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Asma H, Halfon MS. Computational enhancer prediction: evaluation and improvements. BMC Bioinformatics 2019; 20:174. [PMID: 30953451 PMCID: PMC6451241 DOI: 10.1186/s12859-019-2781-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 03/27/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Identifying transcriptional enhancers and other cis-regulatory modules (CRMs) is an important goal of post-sequencing genome annotation. Computational approaches provide a useful complement to empirical methods for CRM discovery, but it is critical that we develop effective means to evaluate their performance in terms of estimating their sensitivity and specificity. RESULTS We introduce here pCRMeval, a pipeline for in silico evaluation of any enhancer prediction tools that are flexible enough to be applied to the Drosophila melanogaster genome. pCRMeval compares the result of predictions with the extensive existing knowledge of experimentally-validated Drosophila CRMs in order to estimate the precision and relative sensitivity of the prediction method. In the case of supervised prediction methods-when training data composed of validated CRMs are used-pCRMeval can also assess the sensitivity of specific training sets. We demonstrate the utility of pCRMeval through evaluation of our SCRMshaw CRM prediction method and training data. By measuring the impact of different parameters on SCRMshaw performance, as assessed by pCRMeval, we develop a more robust version of SCRMshaw, SCRMshaw_HD, that improves the number of predictions while maintaining sensitivity and specificity. Our analysis also demonstrates that SCRMshaw_HD, when applied to increasingly less well-assembled genomes, maintains its strong predictive power with only a minor drop-off in performance. CONCLUSION Our pCRMeval pipeline provides a general framework for evaluation that can be applied to any CRM prediction method, particularly a supervised method. While we make use of it here primarily to test and improve a particular method for CRM prediction, SCRMshaw, pCRMeval should provide a valuable platform to the research community not only for evaluating individual methods, but also for comparing between competing methods.
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Affiliation(s)
- Hasiba Asma
- Program in Genetics, Genomics, and Bioinformatics, University at Buffalo-State University of New York, 701 Ellicott St, Buffalo, NY, 14203, USA
| | - Marc S Halfon
- Program in Genetics, Genomics, and Bioinformatics, University at Buffalo-State University of New York, 701 Ellicott St, Buffalo, NY, 14203, USA.
- Department of Biochemistry, University at Buffalo-State University of New York, 701 Ellicott St, Buffalo, NY, 14203, USA.
- Department of Biological Sciences, University at Buffalo-State University of New York, 701 Ellicott St, Buffalo, NY, 14203, USA.
- Department of Biomedical Informatics, University at Buffalo-State University of New York, 701 Ellicott St, Buffalo, NY, 14203, USA.
- NY State Center of Excellence in Bioinformatics and Life Sciences, 701 Ellicott St, Buffalo, NY, 14203, USA.
- Molecular and Cellular Biology Department and Program in Cancer Genetics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA.
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14
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Ho EYK, Cao Q, Gu M, Chan RWL, Wu Q, Gerstein M, Yip KY. Shaping the nebulous enhancer in the era of high-throughput assays and genome editing. Brief Bioinform 2019; 21:836-850. [PMID: 30895290 DOI: 10.1093/bib/bbz030] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 02/15/2019] [Accepted: 02/26/2019] [Indexed: 01/22/2023] Open
Abstract
Since the 1st discovery of transcriptional enhancers in 1981, their textbook definition has remained largely unchanged in the past 37 years. With the emergence of high-throughput assays and genome editing, which are switching the paradigm from bottom-up discovery and testing of individual enhancers to top-down profiling of enhancer activities genome-wide, it has become increasingly evidenced that this classical definition has left substantial gray areas in different aspects. Here we survey a representative set of recent research articles and report the definitions of enhancers they have adopted. The results reveal that a wide spectrum of definitions is used usually without the definition stated explicitly, which could lead to difficulties in data interpretation and downstream analyses. Based on these findings, we discuss the practical implications and suggestions for future studies.
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Affiliation(s)
| | - Qin Cao
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Mengting Gu
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA
| | - Ricky Wai-Lun Chan
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Qiong Wu
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong.,School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong
| | - Mark Gerstein
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, USA.,Program in Computational Biology and Bioinformatics.,Department of Computer Science, Yale University, New Haven, Connecticut, USA
| | - Kevin Y Yip
- Department of Biomedical Engineering.,Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong.,Hong Kong Bioinformatics Centre.,CUHK-BGI Innovation Institute of Trans-omics.,Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong
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15
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A New Algorithm for Identifying Cis-Regulatory Modules Based on Hidden Markov Model. BIOMED RESEARCH INTERNATIONAL 2018; 2017:6274513. [PMID: 28497059 PMCID: PMC5405574 DOI: 10.1155/2017/6274513] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2016] [Revised: 03/06/2017] [Accepted: 03/23/2017] [Indexed: 11/24/2022]
Abstract
The discovery of cis-regulatory modules (CRMs) is the key to understanding mechanisms of transcription regulation. Since CRMs have specific regulatory structures that are the basis for the regulation of gene expression, how to model the regulatory structure of CRMs has a considerable impact on the performance of CRM identification. The paper proposes a CRM discovery algorithm called ComSPS. ComSPS builds a regulatory structure model of CRMs based on HMM by exploring the rules of CRM transcriptional grammar that governs the internal motif site arrangement of CRMs. We test ComSPS on three benchmark datasets and compare it with five existing methods. Experimental results show that ComSPS performs better than them.
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16
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Banf M, Rhee SY. Computational inference of gene regulatory networks: Approaches, limitations and opportunities. BIOCHIMICA ET BIOPHYSICA ACTA-GENE REGULATORY MECHANISMS 2016; 1860:41-52. [PMID: 27641093 DOI: 10.1016/j.bbagrm.2016.09.003] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Revised: 09/08/2016] [Accepted: 09/08/2016] [Indexed: 10/21/2022]
Abstract
Gene regulatory networks lie at the core of cell function control. In E. coli and S. cerevisiae, the study of gene regulatory networks has led to the discovery of regulatory mechanisms responsible for the control of cell growth, differentiation and responses to environmental stimuli. In plants, computational rendering of gene regulatory networks is gaining momentum, thanks to the recent availability of high-quality genomes and transcriptomes and development of computational network inference approaches. Here, we review current techniques, challenges and trends in gene regulatory network inference and highlight challenges and opportunities for plant science. We provide plant-specific application examples to guide researchers in selecting methodologies that suit their particular research questions. Given the interdisciplinary nature of gene regulatory network inference, we tried to cater to both biologists and computer scientists to help them engage in a dialogue about concepts and caveats in network inference. Specifically, we discuss problems and opportunities in heterogeneous data integration for eukaryotic organisms and common caveats to be considered during network model evaluation. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.
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Affiliation(s)
- Michael Banf
- Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford 93405, United States.
| | - Seung Y Rhee
- Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford 93405, United States.
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17
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Guo H, Huo H, Yu Q. SMCis: An Effective Algorithm for Discovery of Cis-Regulatory Modules. PLoS One 2016; 11:e0162968. [PMID: 27637070 PMCID: PMC5026350 DOI: 10.1371/journal.pone.0162968] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Accepted: 08/31/2016] [Indexed: 12/02/2022] Open
Abstract
The discovery of cis-regulatory modules (CRMs) is a challenging problem in computational biology. Limited by the difficulty of using an HMM to model dependent features in transcriptional regulatory sequences (TRSs), the probabilistic modeling methods based on HMMs cannot accurately represent the distance between regulatory elements in TRSs and are cumbersome to model the prevailing dependencies between motifs within CRMs. We propose a probabilistic modeling algorithm called SMCis, which builds a more powerful CRM discovery model based on a hidden semi-Markov model. Our model characterizes the regulatory structure of CRMs and effectively models dependencies between motifs at a higher level of abstraction based on segments rather than nucleotides. Experimental results on three benchmark datasets indicate that our method performs better than the compared algorithms.
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Affiliation(s)
- Haitao Guo
- School of Computer Science and Technology, Xidian University, Xi’an, Shaanxi, China
| | - Hongwei Huo
- School of Computer Science and Technology, Xidian University, Xi’an, Shaanxi, China
- * E-mail:
| | - Qiang Yu
- School of Computer Science and Technology, Xidian University, Xi’an, Shaanxi, China
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18
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Lewis J, van der Burg K, Mazo-Vargas A, Reed R. ChIP-Seq-Annotated Heliconius erato Genome Highlights Patterns of cis -Regulatory Evolution in Lepidoptera. Cell Rep 2016; 16:2855-2863. [DOI: 10.1016/j.celrep.2016.08.042] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Revised: 07/14/2016] [Accepted: 08/12/2016] [Indexed: 12/11/2022] Open
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19
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Westermark PO. Linking Core Promoter Classes to Circadian Transcription. PLoS Genet 2016; 12:e1006231. [PMID: 27504829 PMCID: PMC4978467 DOI: 10.1371/journal.pgen.1006231] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Accepted: 07/08/2016] [Indexed: 01/09/2023] Open
Abstract
Circadian rhythms in transcription are generated by rhythmic abundances and DNA binding activities of transcription factors. Propagation of rhythms to transcriptional initiation involves the core promoter, its chromatin state, and the basal transcription machinery. Here, I characterize core promoters and chromatin states of genes transcribed in a circadian manner in mouse liver and in Drosophila. It is shown that the core promoter is a critical determinant of circadian mRNA expression in both species. A distinct core promoter class, strong circadian promoters (SCPs), is identified in mouse liver but not Drosophila. SCPs are defined by specific core promoter features, and are shown to drive circadian transcriptional activities with both high averages and high amplitudes. Data analysis and mathematical modeling further provided evidence for rhythmic regulation of both polymerase II recruitment and pause release at SCPs. The analysis provides a comprehensive and systematic view of core promoters and their link to circadian mRNA expression in mouse and Drosophila, and thus reveals a crucial role for the core promoter in regulated, dynamic transcription.
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Affiliation(s)
- Pål O. Westermark
- Institute for Theoretical Biology, Charité –Universitätsmedizin Berlin, Berlin, Germany
- * E-mail:
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20
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Chatzou M, Magis C, Chang JM, Kemena C, Bussotti G, Erb I, Notredame C. Multiple sequence alignment modeling: methods and applications. Brief Bioinform 2015; 17:1009-1023. [PMID: 26615024 DOI: 10.1093/bib/bbv099] [Citation(s) in RCA: 84] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2015] [Revised: 10/16/2015] [Indexed: 12/20/2022] Open
Abstract
This review provides an overview on the development of Multiple sequence alignment (MSA) methods and their main applications. It is focused on progress made over the past decade. The three first sections review recent algorithmic developments for protein, RNA/DNA and genomic alignments. The fourth section deals with benchmarks and explores the relationship between empirical and simulated data, along with the impact on method developments. The last part of the review gives an overview on available MSA local reliability estimators and their dependence on various algorithmic properties of available methods.
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21
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Leoncini M, Montangero M, Pellegrini M, Tillan KP. CMStalker: A Combinatorial Tool for Composite Motif Discovery. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:1123-1136. [PMID: 26451824 DOI: 10.1109/tcbb.2014.2359444] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Controlling the differential expression of many thousands different genes at any given time is a fundamental task of metazoan organisms and this complex orchestration is controlled by the so-called regulatory genome encoding complex regulatory networks: several Transcription Factors bind to precise DNA regions, so to perform in a cooperative manner a specific regulation task for nearby genes. The in silico prediction of these binding sites is still an open problem, notwithstanding continuous progress and activity in the last two decades. In this paper, we describe a new efficient combinatorial approach to the problem of detecting sets of cooperating binding sites in promoter sequences, given in input a database of Transcription Factor Binding Sites encoded as Position Weight Matrices. We present CMStalker, a software tool for composite motif discovery which embodies a new approach that combines a constraint satisfaction formulation with a parameter relaxation technique to explore efficiently the space of possible solutions. Extensive experiments with 12 data sets and 11 state-of-the-art tools are reported, showing an average value of the correlation coefficient of 0.54 (against a value 0.41 of the closest competitor). This improvements in output quality due to CMStalker is statistically significant.
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22
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Phylogenomic identification of regulatory sequences in bacteria: an analysis of statistical power and an application to Borrelia burgdorferi sensu lato. mBio 2015; 6:mBio.00011-15. [PMID: 25873371 PMCID: PMC4453575 DOI: 10.1128/mbio.00011-15] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
UNLABELLED Phylogenomic footprinting is an approach for ab initio identification of genome-wide regulatory elements in bacterial species based on sequence conservation. The statistical power of the phylogenomic approach depends on the degree of sequence conservation, the length of regulatory elements, and the level of phylogenetic divergence among genomes. Building on an earlier model, we propose a binomial model that uses synonymous tree lengths as neutral expectations for determining the statistical significance of conserved intergenic spacer (IGS) sequences. Simulations show that the binomial model is robust to variations in the value of evolutionary parameters, including base frequencies and the transition-to-transversion ratio. We used the model to search for regulatory sequences in the Lyme disease species group (Borrelia burgdorferi sensu lato) using 23 genomes. The model indicates that the currently available set of Borrelia genomes would not yield regulatory sequences shorter than five bases, suggesting that genome sequences of additional B. burgdorferi sensu lato species are needed. Nevertheless, we show that previously known regulatory elements are indeed strongly conserved in sequence or structure across these Borrelia species. Further, we predict with sufficient confidence two new RpoS binding sites, 39 promoters, 19 transcription terminators, 28 noncoding RNAs, and four sets of coregulated genes. These putative cis- and trans-regulatory elements suggest novel, Borrelia-specific mechanisms regulating the transition between the tick and host environments, a key adaptation and virulence mechanism of B. burgdorferi. Alignments of IGS sequences are available on BorreliaBase.org, an online database of orthologous open reading frame (ORF) and IGS sequences in Borrelia. IMPORTANCE While bacterial genomes contain mostly protein-coding genes, they also house DNA sequences regulating the expression of these genes. Gene regulatory sequences tend to be conserved during evolution. By sequencing and comparing related genomes, one can therefore identify regulatory sequences in bacteria based on sequence conservation. Here, we describe a statistical framework by which one may determine how many genomes need to be sequenced and at what level of evolutionary relatedness in order to achieve a high level of statistical significance. We applied the framework to Borrelia burgdorferi, the Lyme disease agent, and identified a large number of candidate regulatory sequences, many of which are known to be involved in regulating the phase transition between the tick vector and mammalian hosts.
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23
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Blatti C, Kazemian M, Wolfe S, Brodsky M, Sinha S. Integrating motif, DNA accessibility and gene expression data to build regulatory maps in an organism. Nucleic Acids Res 2015; 43:3998-4012. [PMID: 25791631 PMCID: PMC4417154 DOI: 10.1093/nar/gkv195] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2015] [Accepted: 02/24/2015] [Indexed: 11/17/2022] Open
Abstract
Characterization of cell type specific regulatory networks and elements is a major challenge in genomics, and emerging strategies frequently employ high-throughput genome-wide assays of transcription factor (TF) to DNA binding, histone modifications or chromatin state. However, these experiments remain too difficult/expensive for many laboratories to apply comprehensively to their system of interest. Here, we explore the potential of elucidating regulatory systems in varied cell types using computational techniques that rely on only data of gene expression, low-resolution chromatin accessibility, and TF–DNA binding specificities (‘motifs’). We show that static computational motif scans overlaid with chromatin accessibility data reasonably approximate experimentally measured TF–DNA binding. We demonstrate that predicted binding profiles and expression patterns of hundreds of TFs are sufficient to identify major regulators of ∼200 spatiotemporal expression domains in the Drosophila embryo. We are then able to learn reliable statistical models of enhancer activity for over 70 expression domains and apply those models to annotate domain specific enhancers genome-wide. Throughout this work, we apply our motif and accessibility based approach to comprehensively characterize the regulatory network of fruitfly embryonic development and show that the accuracy of our computational method compares favorably to approaches that rely on data from many experimental assays.
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Affiliation(s)
- Charles Blatti
- Department of Computer Science, University of Illinois, Urbana, IL 61801, USA
| | - Majid Kazemian
- National Heart Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Scot Wolfe
- Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, MA 01655, USA Department of Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01655, USA
| | - Michael Brodsky
- Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, MA 01655, USA Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01655, USA
| | - Saurabh Sinha
- Department of Computer Science, University of Illinois, Urbana, IL 61801, USA Institute of Genomic Biology, University of Illinois, Urbana, IL 61801, USA
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24
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Suryamohan K, Halfon MS. Identifying transcriptional cis-regulatory modules in animal genomes. WILEY INTERDISCIPLINARY REVIEWS. DEVELOPMENTAL BIOLOGY 2015; 4:59-84. [PMID: 25704908 PMCID: PMC4339228 DOI: 10.1002/wdev.168] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 11/04/2014] [Accepted: 11/16/2014] [Indexed: 11/08/2022]
Abstract
UNLABELLED Gene expression is regulated through the activity of transcription factors (TFs) and chromatin-modifying proteins acting on specific DNA sequences, referred to as cis-regulatory elements. These include promoters, located at the transcription initiation sites of genes, and a variety of distal cis-regulatory modules (CRMs), the most common of which are transcriptional enhancers. Because regulated gene expression is fundamental to cell differentiation and acquisition of new cell fates, identifying, characterizing, and understanding the mechanisms of action of CRMs is critical for understanding development. CRM discovery has historically been challenging, as CRMs can be located far from the genes they regulate, have few readily identifiable sequence characteristics, and for many years were not amenable to high-throughput discovery methods. However, the recent availability of complete genome sequences and the development of next-generation sequencing methods have led to an explosion of both computational and empirical methods for CRM discovery in model and nonmodel organisms alike. Experimentally, CRMs can be identified through chromatin immunoprecipitation directed against TFs or histone post-translational modifications, identification of nucleosome-depleted 'open' chromatin regions, or sequencing-based high-throughput functional screening. Computational methods include comparative genomics, clustering of known or predicted TF-binding sites, and supervised machine-learning approaches trained on known CRMs. All of these methods have proven effective for CRM discovery, but each has its own considerations and limitations, and each is subject to a greater or lesser number of false-positive identifications. Experimental confirmation of predictions is essential, although shortcomings in current methods suggest that additional means of validation need to be developed. For further resources related to this article, please visit the WIREs website. CONFLICT OF INTEREST The authors have declared no conflicts of interest for this article.
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Affiliation(s)
- Kushal Suryamohan
- Department of Biochemistry, University at Buffalo-State University of New York, Buffalo, NY 14203, USA
- NY State Center of Excellence in Bioinformatics and Life Sciences, Buffalo, NY 14203, USA
| | - Marc S. Halfon
- Department of Biochemistry, University at Buffalo-State University of New York, Buffalo, NY 14203, USA
- Department of Biological Sciences, University at Buffalo-State University of New York, Buffalo, NY 14203, USA
- Department of Biomedical Informatics, University at Buffalo-State University of New York, Buffalo, NY 14203, USA
- NY State Center of Excellence in Bioinformatics and Life Sciences, Buffalo, NY 14203, USA
- Molecular and Cellular Biology Department and Program in Cancer Genetics, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
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25
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Zheng Y, Li X, Hu H. Comprehensive discovery of DNA motifs in 349 human cells and tissues reveals new features of motifs. Nucleic Acids Res 2015; 43:74-83. [PMID: 25505144 PMCID: PMC4288161 DOI: 10.1093/nar/gku1261] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2014] [Revised: 11/13/2014] [Accepted: 11/17/2014] [Indexed: 01/15/2023] Open
Abstract
Comprehensive motif discovery under experimental conditions is critical for the global understanding of gene regulation. To generate a nearly complete list of human DNA motifs under given conditions, we employed a novel approach to de novo discover significant co-occurring DNA motifs in 349 human DNase I hypersensitive site datasets. We predicted 845 to 1325 motifs in each dataset, for a total of 2684 non-redundant motifs. These 2684 motifs contained 54.02 to 75.95% of the known motifs in seven large collections including TRANSFAC. In each dataset, we also discovered 43 663 to 2 013 288 motif modules, groups of motifs with their binding sites co-occurring in a significant number of short DNA regions. Compared with known interacting transcription factors in eight resources, the predicted motif modules on average included 84.23% of known interacting motifs. We further showed new features of the predicted motifs, such as motifs enriched in proximal regions rarely overlapped with motifs enriched in distal regions, motifs enriched in 5' distal regions were often enriched in 3' distal regions, etc. Finally, we observed that the 2684 predicted motifs classified the cell or tissue types of the datasets with an accuracy of 81.29%. The resources generated in this study are available at http://server.cs.ucf.edu/predrem/.
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Affiliation(s)
- Yiyu Zheng
- Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA
| | - Xiaoman Li
- Burnett School of Biomedical Science, College of Medicine, University of Central Florida, Orlando, FL 32816, USA
| | - Haiyan Hu
- Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA
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26
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Yang TH, Wang CC, Hung PC, Wu WS. cisMEP: an integrated repository of genomic epigenetic profiles and cis-regulatory modules in Drosophila. BMC SYSTEMS BIOLOGY 2014; 8 Suppl 4:S8. [PMID: 25521507 PMCID: PMC4290730 DOI: 10.1186/1752-0509-8-s4-s8] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND Cis-regulatory modules (CRMs), or the DNA sequences required for regulating gene expression, play the central role in biological researches on transcriptional regulation in metazoan species. Nowadays, the systematic understanding of CRMs still mainly resorts to computational methods due to the time-consuming and small-scale nature of experimental methods. But the accuracy and reliability of different CRM prediction tools are still unclear. Without comparative cross-analysis of the results and combinatorial consideration with extra experimental information, there is no easy way to assess the confidence of the predicted CRMs. This limits the genome-wide understanding of CRMs. DESCRIPTION It is known that transcription factor binding and epigenetic profiles tend to determine functions of CRMs in gene transcriptional regulation. Thus integration of the genome-wide epigenetic profiles with systematically predicted CRMs can greatly help researchers evaluate and decipher the prediction confidence and possible transcriptional regulatory functions of these potential CRMs. However, these data are still fragmentary in the literatures. Here we performed the computational genome-wide screening for potential CRMs using different prediction tools and constructed the pioneer database, cisMEP (cis-regulatory module epigenetic profile database), to integrate these computationally identified CRMs with genomic epigenetic profile data. cisMEP collects the literature-curated TFBS location data and nine genres of epigenetic data for assessing the confidence of these potential CRMs and deciphering the possible CRM functionality. CONCLUSIONS cisMEP aims to provide a user-friendly interface for researchers to assess the confidence of different potential CRMs and to understand the functions of CRMs through experimentally-identified epigenetic profiles. The deposited potential CRMs and experimental epigenetic profiles for confidence assessment provide experimentally testable hypotheses for the molecular mechanisms of metazoan gene regulation. We believe that the information deposited in cisMEP will greatly facilitate the comparative usage of different CRM prediction tools and will help biologists to study the modular regulatory mechanisms between different TFs and their target genes.
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27
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iRegulon: from a gene list to a gene regulatory network using large motif and track collections. PLoS Comput Biol 2014; 10:e1003731. [PMID: 25058159 PMCID: PMC4109854 DOI: 10.1371/journal.pcbi.1003731] [Citation(s) in RCA: 608] [Impact Index Per Article: 60.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2014] [Accepted: 05/27/2014] [Indexed: 01/17/2023] Open
Abstract
Identifying master regulators of biological processes and mapping their downstream gene networks are key challenges in systems biology. We developed a computational method, called iRegulon, to reverse-engineer the transcriptional regulatory network underlying a co-expressed gene set using cis-regulatory sequence analysis. iRegulon implements a genome-wide ranking-and-recovery approach to detect enriched transcription factor motifs and their optimal sets of direct targets. We increase the accuracy of network inference by using very large motif collections of up to ten thousand position weight matrices collected from various species, and linking these to candidate human TFs via a motif2TF procedure. We validate iRegulon on gene sets derived from ENCODE ChIP-seq data with increasing levels of noise, and we compare iRegulon with existing motif discovery methods. Next, we use iRegulon on more challenging types of gene lists, including microRNA target sets, protein-protein interaction networks, and genetic perturbation data. In particular, we over-activate p53 in breast cancer cells, followed by RNA-seq and ChIP-seq, and could identify an extensive up-regulated network controlled directly by p53. Similarly we map a repressive network with no indication of direct p53 regulation but rather an indirect effect via E2F and NFY. Finally, we generalize our computational framework to include regulatory tracks such as ChIP-seq data and show how motif and track discovery can be combined to map functional regulatory interactions among co-expressed genes. iRegulon is available as a Cytoscape plugin from http://iregulon.aertslab.org. Gene regulatory networks control developmental, homeostatic, and disease processes by governing precise levels and spatio-temporal patterns of gene expression. Determining their topology can provide mechanistic insight into these processes. Gene regulatory networks consist of interactions between transcription factors and their direct target genes. Each regulatory interaction represents the binding of the transcription factor to a specific DNA binding site near its target gene. Here we present a computational method, called iRegulon, to identify master regulators and direct target genes in a human gene signature, i.e. a set of co-expressed genes. iRegulon relies on the analysis of the regulatory sequences around each gene in the gene set to detect enriched TF motifs or ChIP-seq peaks, using databases of nearly 10.000 TF motifs and 1000 ChIP-seq data sets or “tracks”. Next, it associates enriched motifs and tracks with candidate transcription factors and determines the optimal subset of direct target genes. We validate iRegulon on ENCODE data, and use it in combination with RNA-seq and ChIP-seq data to map a p53 downstream network with new predicted co-factors and targets. iRegulon is available as a Cytoscape plugin, supporting human, mouse, and Drosophila genes, and provides access to hundreds of cancer-related TF-target subnetworks or “regulons”.
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28
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Di L, Pagan PE, Packer D, Martin CL, Akther S, Ramrattan G, Mongodin EF, Fraser CM, Schutzer SE, Luft BJ, Casjens SR, Qiu WG. BorreliaBase: a phylogeny-centered browser of Borrelia genomes. BMC Bioinformatics 2014; 15:233. [PMID: 24994456 PMCID: PMC4094996 DOI: 10.1186/1471-2105-15-233] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2014] [Accepted: 06/26/2014] [Indexed: 11/29/2022] Open
Abstract
Background The bacterial genus Borrelia (phylum Spirochaetes) consists of two groups of pathogens represented respectively by B. burgdorferi, the agent of Lyme borreliosis, and B. hermsii, the agent of tick-borne relapsing fever. The number of publicly available Borrelia genomic sequences is growing rapidly with the discovery and sequencing of Borrelia strains worldwide. There is however a lack of dedicated online databases to facilitate comparative analyses of Borrelia genomes. Description We have developed BorreliaBase, an online database for comparative browsing of Borrelia genomes. The database is currently populated with sequences from 35 genomes of eight Lyme-borreliosis (LB) group Borrelia species and 7 Relapsing-fever (RF) group Borrelia species. Distinct from genome repositories and aggregator databases, BorreliaBase serves manually curated comparative-genomic data including genome-based phylogeny, genome synteny, and sequence alignments of orthologous genes and intergenic spacers. Conclusions With a genome phylogeny at its center, BorreliaBase allows online identification of hypervariable lipoprotein genes, potential regulatory elements, and recombination footprints by providing evolution-based expectations of sequence variability at each genomic locus. The phylo-centric design of BorreliaBase (http://borreliabase.org) is a novel model for interactive browsing and comparative analysis of bacterial genomes online.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Wei-Gang Qiu
- Department of Biological Sciences, Hunter College, The City University of New York, 10065 New York, NY, USA.
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Rouault H, Santolini M, Schweisguth F, Hakim V. Imogene: identification of motifs and cis-regulatory modules underlying gene co-regulation. Nucleic Acids Res 2014; 42:6128-45. [PMID: 24682824 PMCID: PMC4041412 DOI: 10.1093/nar/gku209] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Cis-regulatory modules (CRMs) and motifs play a central role in tissue and condition-specific gene expression. Here we present Imogene, an ensemble of statistical tools that we have developed to facilitate their identification and implemented in a publicly available software. Starting from a small training set of mammalian or fly CRMs that drive similar gene expression profiles, Imogene determines de novocis-regulatory motifs that underlie this co-expression. It can then predict on a genome-wide scale other CRMs with a regulatory potential similar to the training set. Imogene bypasses the need of large datasets for statistical analyses by making central use of the information provided by the sequenced genomes of multiple species, based on the developed statistical tools and explicit models for transcription factor binding site evolution. We test Imogene on characterized tissue-specific mouse developmental CRMs. Its ability to identify CRMs with the same specificity based on its de novo created motifs is comparable to that of previously evaluated ‘motif-blind’ methods. We further show, both in flies and in mammals, that Imogene de novo generated motifs are sufficient to discriminate CRMs related to different developmental programs. Notably, purely relying on sequence data, Imogene performs as well in this discrimination task as a previously reported learning algorithm based on Chromatin Immunoprecipitation (ChIP) data for multiple transcription factors at multiple developmental stages.
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Affiliation(s)
- Hervé Rouault
- Developmental and Stem Cell Biology Department, Institut Pasteur, F-75015 Paris, France CNRS, URA2578, F-75015 Paris, France
| | - Marc Santolini
- Laboratoire de Physique Statistique, CNRS, École Normale Supérieure, Université P. et M. Curie, Université Paris-Diderot
| | - François Schweisguth
- Developmental and Stem Cell Biology Department, Institut Pasteur, F-75015 Paris, France CNRS, URA2578, F-75015 Paris, France
| | - Vincent Hakim
- Laboratoire de Physique Statistique, CNRS, École Normale Supérieure, Université P. et M. Curie, Université Paris-Diderot
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Gan Y, Guan J, Zhou S, Zhang W. Identifying Cis-Regulatory Elements and Modules Using Conditional Random Fields. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2014; 11:73-82. [PMID: 26355509 DOI: 10.1109/tcbb.2013.131] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Accurate identification of cis-regulatory elements and their correlated modules is essential for analysis of transcriptional regulation, which is a challenging problem in computational biology. Unsupervised learning has the advantage of compensating for missing annotated data, and is thus promising to be effective to identify cis-regulatory elements and modules. We introduced a Conditional Random Fields model, referred to as CRFEM, to integrate sequence features and long-range dependency of genomic sequences such as epigenetic features to identify cis-regulatory elements and modules at the same time. The proposed method is able to automatically learn model parameters with no labeled data and explicitly optimize the predictive probability of cis-regulatory elements and modules. In comparison with existing methods, our method is more accurate and can be used for genome-wide studies of gene regulation.
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Diez D, Hutchins AP, Miranda-Saavedra D. Systematic identification of transcriptional regulatory modules from protein-protein interaction networks. Nucleic Acids Res 2013; 42:e6. [PMID: 24137002 PMCID: PMC3874207 DOI: 10.1093/nar/gkt913] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Transcription factors (TFs) combine with co-factors to form transcriptional regulatory modules (TRMs) that regulate gene expression programs with spatiotemporal specificity. Here we present a novel and generic method (rTRM) for the reconstruction of TRMs that integrates genomic information from TF binding, cell type-specific gene expression and protein–protein interactions. rTRM was applied to reconstruct the TRMs specific for embryonic stem cells (ESC) and hematopoietic stem cells (HSC), neural progenitor cells, trophoblast stem cells and distinct types of terminally differentiated CD4+ T cells. The ESC and HSC TRM predictions were highly precise, yielding 77 and 96 proteins, of which ∼75% have been independently shown to be involved in the regulation of these cell types. Furthermore, rTRM successfully identified a large number of bridging proteins with known roles in ESCs and HSCs, which could not have been identified using genomic approaches alone, as they lack the ability to bind specific DNA sequences. This highlights the advantage of rTRM over other methods that ignore PPI information, as proteins need to interact with other proteins to form complexes and perform specific functions. The prediction and experimental validation of the co-factors that endow master regulatory TFs with the capacity to select specific genomic sites, modulate the local epigenetic profile and integrate multiple signals will provide important mechanistic insights not only into how such TFs operate, but also into abnormal transcriptional states leading to disease.
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Affiliation(s)
- Diego Diez
- World Premier International (WPI) Immunology Frontier Research Center (IFReC), Osaka University, 3-1 Yamadaoka, Suita 565-0871, Osaka, Japan, South China Institute for Stem Cell Biology and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, 190 Kaiyuan Ave, Guangzhou 510663, China and Fibrosis Laboratories, Institute of Cellular Medicine, Newcastle University Medical School, Framlington Place, Newcastle upon Tyne NE2 4HH, United Kingdom
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Abstract
By its very nature, genomics produces large, high-dimensional datasets that are well suited to analysis by machine learning approaches. Here, we explain some key aspects of machine learning that make it useful for genome annotation, with illustrative examples from ENCODE.
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Affiliation(s)
- Kevin Y Yip
- Program in Computational Biology and Bioinformatics, Yale University, 260/266 Whitney Avenue, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, 260/266 Whitney Avenue, New Haven, CT 06520, USA
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
- Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
- CUHK-BGI Innovation Institute of Trans-omics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Chao Cheng
- Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, USA
- Institute for Quantitative Biomedical Sciences, Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH 03766, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, 260/266 Whitney Avenue, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, 260/266 Whitney Avenue, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06511, USA
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Dickel DE, Visel A, Pennacchio LA. Functional anatomy of distant-acting mammalian enhancers. Philos Trans R Soc Lond B Biol Sci 2013; 368:20120359. [PMID: 23650633 DOI: 10.1098/rstb.2012.0359] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Transcriptional enhancers are a major class of functional element embedded in the vast non-coding portion of the human genome. Acting over large genomic distances, enhancers play critical roles in the tissue and cell type-specific regulation of genes, and there is mounting evidence that they contribute to the aetiology of many human diseases. Methods for genome-wide mapping of enhancer regions are now available, but the functional architecture contained within human enhancer elements remains unclear. Here, we review recent approaches aimed at understanding the functional anatomy of individual enhancer elements, using systematic qualitative and quantitative assessments of mammalian enhancer variants in cultured cells and in vivo. These studies provide direct insight into common architectural characteristics of enhancers including the presence of multiple transcription factor-binding sites and the mixture of both transcriptionally activating and repressing domains within the same enhancer. Despite such progress in understanding the functional composition of enhancers, the inherent complexities of enhancer anatomy continue to limit our ability to predict the impact of sequence changes on in vivo enhancer function. While providing an initial glimpse into the mutability of mammalian enhancers, these observations highlight the continued need for experimental enhancer assessment as genome sequencing becomes routine in the clinic.
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Affiliation(s)
- D E Dickel
- Genomics Division, MS 84-171, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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Kheradpour P, Ernst J, Melnikov A, Rogov P, Wang L, Zhang X, Alston J, Mikkelsen TS, Kellis M. Systematic dissection of regulatory motifs in 2000 predicted human enhancers using a massively parallel reporter assay. Genome Res 2013; 23:800-11. [PMID: 23512712 PMCID: PMC3638136 DOI: 10.1101/gr.144899.112] [Citation(s) in RCA: 234] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2012] [Accepted: 03/14/2013] [Indexed: 01/06/2023]
Abstract
Genome-wide chromatin annotations have permitted the mapping of putative regulatory elements across multiple human cell types. However, their experimental dissection by directed regulatory motif disruption has remained unfeasible at the genome scale. Here, we use a massively parallel reporter assay (MPRA) to measure the transcriptional levels induced by 145-bp DNA segments centered on evolutionarily conserved regulatory motif instances within enhancer chromatin states. We select five predicted activators (HNF1, HNF4, FOXA, GATA, NFE2L2) and two predicted repressors (GFI1, ZFP161) and measure reporter expression in erythroleukemia (K562) and liver carcinoma (HepG2) cell lines. We test 2104 wild-type sequences and 3314 engineered enhancer variants containing targeted motif disruptions, each using 10 barcode tags and two replicates. The resulting data strongly confirm the enhancer activity and cell-type specificity of enhancer chromatin states, the ability of 145-bp segments to recapitulate both, the necessary role of regulatory motifs in enhancer function, and the complementary roles of activator and repressor motifs. We find statistically robust evidence that (1) disrupting the predicted activator motifs abolishes enhancer function, while silent or motif-improving changes maintain enhancer activity; (2) evolutionary conservation, nucleosome exclusion, binding of other factors, and strength of the motif match are predictive of enhancer activity; (3) scrambling repressor motifs leads to aberrant reporter expression in cell lines where the enhancers are usually inactive. Our results suggest a general strategy for deciphering cis-regulatory elements by systematic large-scale manipulation and provide quantitative enhancer activity measurements across thousands of constructs that can be mined to develop predictive models of gene expression.
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Affiliation(s)
- Pouya Kheradpour
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Broad Institute, Cambridge, Massachusetts 02142, USA
| | - Jason Ernst
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Broad Institute, Cambridge, Massachusetts 02142, USA
| | | | - Peter Rogov
- Broad Institute, Cambridge, Massachusetts 02142, USA
| | - Li Wang
- Broad Institute, Cambridge, Massachusetts 02142, USA
| | - Xiaolan Zhang
- Broad Institute, Cambridge, Massachusetts 02142, USA
| | - Jessica Alston
- Broad Institute, Cambridge, Massachusetts 02142, USA
- Program in Biological and Biomedical Sciences and Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Tarjei S. Mikkelsen
- Broad Institute, Cambridge, Massachusetts 02142, USA
- Harvard Stem Cell Institute and Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Broad Institute, Cambridge, Massachusetts 02142, USA
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Simonatto M, Barozzi I, Natoli G. Non-coding transcription at cis-regulatory elements: computational and experimental approaches. Methods 2013; 63:66-75. [PMID: 23542771 DOI: 10.1016/j.ymeth.2013.03.021] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2013] [Revised: 03/18/2013] [Accepted: 03/20/2013] [Indexed: 12/17/2022] Open
Abstract
Mammalian genomes are pervasively transcribed, generating mostly RNAs with no coding potential that display different size, structure and interspecies sequence conservation. A prominent contribution to the ncRNA pool comes from the transcription of cis-regulatory elements, namely promoters, enhancers and locus control regions. While this phenomenon has been extensively documented, possible roles of such ncRNAs in gene regulation are still unclear. Addressing this issue will require experimental strategies dealing with the low abundance of enhancer-templated ncRNAs and aimed at specifically dissecting the relative role of transcription per se vs. RNA products. In this review, we first focus on the identification and characterization of cis-regulatory elements, highlighting the differences between emerging classes of ncRNAs associated to specific chromatin signatures. We then discuss current experimental strategies to dissect the function of nc transcription and computational approaches to the analysis and classification of regulatory sequences identified in next-generation sequencing experiments.
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Affiliation(s)
- Marta Simonatto
- Department of Experimental Oncology, European Institute of Oncology (IEO), Via Adamello 16, 20139 Milan, Italy.
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36
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Shu JJ, Li Y. A statistical thin-tail test of predicting regulatory regions in the Drosophila genome. Theor Biol Med Model 2013; 10:11. [PMID: 23409927 PMCID: PMC3598831 DOI: 10.1186/1742-4682-10-11] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2012] [Accepted: 02/07/2013] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The identification of transcription factor binding sites (TFBSs) and cis-regulatory modules (CRMs) is a crucial step in studying gene expression, but the computational method attempting to distinguish CRMs from NCNRs still remains a challenging problem due to the limited knowledge of specific interactions involved. METHODS The statistical properties of cis-regulatory modules (CRMs) are explored by estimating the similar-word set distribution with overrepresentation (Z-score). It is observed that CRMs tend to have a thin-tail Z-score distribution. A new statistical thin-tail test with two thinness coefficients is proposed to distinguish CRMs from non-coding non-regulatory regions (NCNRs). RESULTS As compared with the existing fluffy-tail test, the first thinness coefficient is designed to reduce computational time, making the novel thin-tail test very suitable for long sequences and large database analysis in the post-genome time and the second one to improve the separation accuracy between CRMs and NCNRs. These two thinness coefficients may serve as valuable filtering indexes to predict CRMs experimentally. CONCLUSIONS The novel thin-tail test provides an efficient and effective means for distinguishing CRMs from NCNRs based on the specific statistical properties of CRMs and can guide future experiments aimed at finding new CRMs in the post-genome time.
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Affiliation(s)
- Jian-Jun Shu
- School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore.
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37
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Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors. Genome Biol 2012; 13:R48. [PMID: 22950945 PMCID: PMC3491392 DOI: 10.1186/gb-2012-13-9-r48] [Citation(s) in RCA: 187] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2011] [Revised: 05/06/2012] [Accepted: 06/08/2012] [Indexed: 01/22/2023] Open
Abstract
Background Transcription factors function by binding different classes of regulatory elements. The Encyclopedia of DNA Elements (ENCODE) project has recently produced binding data for more than 100 transcription factors from about 500 ChIP-seq experiments in multiple cell types. While this large amount of data creates a valuable resource, it is nonetheless overwhelmingly complex and simultaneously incomplete since it covers only a small fraction of all human transcription factors. Results As part of the consortium effort in providing a concise abstraction of the data for facilitating various types of downstream analyses, we constructed statistical models that capture the genomic features of three paired types of regions by machine-learning methods: firstly, regions with active or inactive binding; secondly, those with extremely high or low degrees of co-binding, termed HOT and LOT regions; and finally, regulatory modules proximal or distal to genes. From the distal regulatory modules, we developed computational pipelines to identify potential enhancers, many of which were validated experimentally. We further associated the predicted enhancers with potential target transcripts and the transcription factors involved. For HOT regions, we found a significant fraction of transcription factor binding without clear sequence motifs and showed that this observation could be related to strong DNA accessibility of these regions. Conclusions Overall, the three pairs of regions exhibit intricate differences in chromosomal locations, chromatin features, factors that bind them, and cell-type specificity. Our machine learning approach enables us to identify features potentially general to all transcription factors, including those not included in the data.
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Shu JJ, Li Y. A statistical fat-tail test of predicting regulatory regions in the Drosophila genome. Comput Biol Med 2012; 42:935-41. [PMID: 22884312 DOI: 10.1016/j.compbiomed.2012.07.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2010] [Revised: 05/29/2012] [Accepted: 07/18/2012] [Indexed: 11/19/2022]
Affiliation(s)
- Jian-Jun Shu
- School of Mechanical & Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore.
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39
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Deyneko IV, Weiss S, Leschner S. An integrative computational approach to effectively guide experimental identification of regulatory elements in promoters. BMC Bioinformatics 2012; 13:202. [PMID: 22897887 PMCID: PMC3465240 DOI: 10.1186/1471-2105-13-202] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2012] [Accepted: 08/01/2012] [Indexed: 01/22/2023] Open
Abstract
Background Transcriptional activity of genes depends on many factors like DNA motifs, conformational characteristics of DNA, melting etc. and there are computational approaches for their identification. However, in real applications, the number of predicted, for example, DNA motifs may be considerably large. In cases when various computational programs are applied, systematic experimental knock out of each of the potential elements obviously becomes nonproductive. Hence, one needs an approach that is able to integrate many heterogeneous computational methods and upon that suggest selected regulatory elements for experimental verification. Results Here, we present an integrative bioinformatic approach aimed at the discovery of regulatory modules that can be effectively verified experimentally. It is based on combinatorial analysis of known and novel binding motifs, as well as of any other known features of promoters. The goal of this method is the identification of a collection of modules that are specific for an established dataset and at the same time are optimal for experimental verification. The method is particularly effective on small datasets, where most statistical approaches fail. We apply it to promoters that drive tumor-specific gene expression in tumor-colonizing Gram-negative bacteria. The method successfully identified a number of potential modules, which required only a few experiments to be verified. The resulting minimal functional bacterial promoter exhibited high specificity of expression in cancerous tissue. Conclusions Experimental analysis of promoter structures guided by bioinformatics has proved to be efficient. The developed computational method is able to include heterogeneous features of promoters and suggest combinatorial modules for experimental testing. Expansibility and robustness of the methodology implemented in the approach ensures good results for a wide range of problems.
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Affiliation(s)
- Igor V Deyneko
- Molecular Immunology, Helmholtz Centre for Infection Research, Inhoffenstr, 7, 38124 Braunschweig, Germany.
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Abstract
Differential gene expression is the fundamental mechanism underlying animal development and cell differentiation. However, it is a challenge to identify comprehensively and accurately the DNA sequences that are required to regulate gene expression: namely, cis-regulatory modules (CRMs). Three major features, either singly or in combination, are used to predict CRMs: clusters of transcription factor binding site motifs, non-coding DNA that is under evolutionary constraint and biochemical marks associated with CRMs, such as histone modifications and protein occupancy. The validation rates for predictions indicate that identifying diagnostic biochemical marks is the most reliable method, and understanding is enhanced by the analysis of motifs and conservation patterns within those predicted CRMs.
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Maston GA, Landt SG, Snyder M, Green MR. Characterization of enhancer function from genome-wide analyses. Annu Rev Genomics Hum Genet 2012; 13:29-57. [PMID: 22703170 DOI: 10.1146/annurev-genom-090711-163723] [Citation(s) in RCA: 80] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
There has been a recent surge in the use of genome-wide methodologies to identify and annotate the transcriptional regulatory elements in the human genome. Here we review some of these methodologies and the conceptual insights about transcription regulation that have been gained from the use of genome-wide studies. It has become clear that the binding of transcription factors is itself a highly regulated process, and binding does not always appear to have functional consequences. Numerous properties have now been associated with regulatory elements that may be useful in their identification. Several aspects of enhancer function have been shown to be more widespread than was previously appreciated, including the highly combinatorial nature of transcription factor binding, the postinitiation regulation of many target genes, and the binding of enhancers at early stages to maintain their competence during development. Going forward, the integration of multiple genome-wide data sets should become a standard approach to elucidate higher-order regulatory interactions.
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Affiliation(s)
- Glenn A Maston
- Howard Hughes Medical Institute and Programs in Gene Function and Expression and Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01605, USA.
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Aboukhalil A, Bulyk ML. LOESS correction for length variation in gene set-based genomic sequence analysis. ACTA ACUST UNITED AC 2012; 28:1446-54. [PMID: 22492312 DOI: 10.1093/bioinformatics/bts155] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
MOTIVATION Sequence analysis algorithms are often applied to sets of DNA, RNA or protein sequences to identify common or distinguishing features. Controlling for sequence length variation is critical to properly score sequence features and identify true biological signals rather than length-dependent artifacts. RESULTS Several cis-regulatory module discovery algorithms exhibit a substantial dependence between DNA sequence score and sequence length. Our newly developed LOESS method is flexible in capturing diverse score-length relationships and is more effective in correcting DNA sequence scores for length-dependent artifacts, compared with four other approaches. Application of this method to genes co-expressed during Drosophila melanogaster embryonic mesoderm development or neural development scored by the Lever motif analysis algorithm resulted in successful recovery of their biologically validated cis-regulatory codes. The LOESS length-correction method is broadly applicable, and may be useful not only for more accurate inference of cis-regulatory codes, but also for detection of other types of patterns in biological sequences. AVAILABILITY Source code and compiled code are available from http://thebrain.bwh.harvard.edu/LM_LOESS/
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Affiliation(s)
- Anton Aboukhalil
- Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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Sun H, Guns T, Fierro AC, Thorrez L, Nijssen S, Marchal K. Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection. Nucleic Acids Res 2012; 40:e90. [PMID: 22422841 PMCID: PMC3384348 DOI: 10.1093/nar/gks237] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Computationally retrieving biologically relevant cis-regulatory modules (CRMs) is not straightforward. Because of the large number of candidates and the imperfection of the screening methods, many spurious CRMs are detected that are as high scoring as the biologically true ones. Using ChIP-information allows not only to reduce the regions in which the binding sites of the assayed transcription factor (TF) should be located, but also allows restricting the valid CRMs to those that contain the assayed TF (here referred to as applying CRM detection in a query-based mode). In this study, we show that exploiting ChIP-information in a query-based way makes in silico CRM detection a much more feasible endeavor. To be able to handle the large datasets, the query-based setting and other specificities proper to CRM detection on ChIP-Seq based data, we developed a novel powerful CRM detection method 'CPModule'. By applying it on a well-studied ChIP-Seq data set involved in self-renewal of mouse embryonic stem cells, we demonstrate how our tool can recover combinatorial regulation of five known TFs that are key in the self-renewal of mouse embryonic stem cells. Additionally, we make a number of new predictions on combinatorial regulation of these five key TFs with other TFs documented in TRANSFAC.
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Affiliation(s)
- Hong Sun
- Department of Microbial and Molecular Systems, Katholieke Universiteit Leuven, Leuven, Belgium
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44
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Beaster-Jones L. Cis-regulation and conserved non-coding elements in amphioxus. Brief Funct Genomics 2012; 11:118-30. [DOI: 10.1093/bfgp/els006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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45
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Nikulova AA, Polishchuk MS, Tumanian VG, Makeev VY, Mironov AA, Favorov AV. Correlations between clusters of protein-DNA binding sites and the binding experimental data allow predicting a structure of regulatory modules. Biophysics (Nagoya-shi) 2012. [DOI: 10.1134/s0006350912020157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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Erb I, González-Vallinas JR, Bussotti G, Blanco E, Eyras E, Notredame C. Use of ChIP-Seq data for the design of a multiple promoter-alignment method. Nucleic Acids Res 2012; 40:e52. [PMID: 22230796 PMCID: PMC3326335 DOI: 10.1093/nar/gkr1292] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
We address the challenge of regulatory sequence alignment with a new method, Pro-Coffee, a multiple aligner specifically designed for homologous promoter regions. Pro-Coffee uses a dinucleotide substitution matrix estimated on alignments of functional binding sites from TRANSFAC. We designed a validation framework using several thousand families of orthologous promoters. This dataset was used to evaluate the accuracy for predicting true human orthologs among their paralogs. We found that whereas other methods achieve on average 73.5% accuracy, and 77.6% when trained on that same dataset, the figure goes up to 80.4% for Pro-Coffee. We then applied a novel validation procedure based on multi-species ChIP-seq data. Trained and untrained methods were tested for their capacity to correctly align experimentally detected binding sites. Whereas the average number of correctly aligned sites for two transcription factors is 284 for default methods and 316 for trained methods, Pro-Coffee achieves 331, 16.5% above the default average. We find a high correlation between a method's performance when classifying orthologs and its ability to correctly align proven binding sites. Not only has this interesting biological consequences, it also allows us to conclude that any method that is trained on the ortholog data set will result in functionally more informative alignments.
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Affiliation(s)
- Ionas Erb
- Bioinformatics and Genomics program, Centre for Genomic Regulation and UPF, 08003 Barcelona, Spain
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Aerts S. Computational strategies for the genome-wide identification of cis-regulatory elements and transcriptional targets. Curr Top Dev Biol 2012; 98:121-45. [PMID: 22305161 DOI: 10.1016/b978-0-12-386499-4.00005-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Transcription factors (TFs) are key proteins that decode the information in our genome to express a precise and unique set of proteins and RNA molecules in each cell type in our body. These factors play a pivotal role in all biological processes, including the determination of a cell's fate during development and the maintenance of a cell's physiological function. To achieve this, a TF binds to specific DNA sequences in the noncoding part of the genome, recruits chromatin modifiers and cofactors, and directs the transcription initiation rate of its "target genes." Therefore, a key challenge in deciphering a transcriptional switch is to identify the direct target genes of the master regulators that control the switch, the cis-regulatory elements implementing (auto-)regulatory loops, and the target genes of all the TFs in the downstream regulatory network. A better knowledge of a TF's targetome during specification and differentiation of a particular cell type will generate mechanistic insight into its developmental program. Here, I review computational strategies and methods to predict transcriptional targets by genome-wide searches for TF binding sites using position weight matrices, motif clusters, phylogenetic footprinting, chromatin binding and accessibility data, enhancer classification, motif enrichment, and gene expression signatures.
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Affiliation(s)
- Stein Aerts
- Laboratory of Computational Biology, Center for Human Genetics, Katholieke Universiteit Leuven, Leuven, Belgium
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Taher L, Narlikar L, Ovcharenko I. CLARE: Cracking the LAnguage of Regulatory Elements. ACTA ACUST UNITED AC 2011; 28:581-3. [PMID: 22199387 DOI: 10.1093/bioinformatics/btr704] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
UNLABELLED CLARE is a computational method designed to reveal sequence encryption of tissue-specific regulatory elements. Starting with a set of regulatory elements known to be active in a particular tissue/process, it learns the sequence code of the input set and builds a predictive model from features specific to those elements. The resulting model can then be applied to user-supplied genomic regions to identify novel candidate regulatory elements. CLARE's model also provides a detailed analysis of transcription factors that most likely bind to the elements, making it an invaluable tool for understanding mechanisms of tissue-specific gene regulation. AVAILABILITY CLARE is freely accessible at http://clare.dcode.org/.
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Affiliation(s)
- Leila Taher
- Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MA 20894, USA.
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Halfon MS, Zhu Q, Brennan ER, Zhou Y. Erroneous attribution of relevant transcription factor binding sites despite successful prediction of cis-regulatory modules. BMC Genomics 2011; 12:578. [PMID: 22115527 PMCID: PMC3235160 DOI: 10.1186/1471-2164-12-578] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2011] [Accepted: 11/25/2011] [Indexed: 12/22/2022] Open
Abstract
Background Cis-regulatory modules are bound by transcription factors to regulate gene expression. Characterizing these DNA sequences is central to understanding gene regulatory networks and gaining insight into mechanisms of transcriptional regulation, but genome-scale regulatory module discovery remains a challenge. One popular approach is to scan the genome for clusters of transcription factor binding sites, especially those conserved in related species. When such approaches are successful, it is typically assumed that the activity of the modules is mediated by the identified binding sites and their cognate transcription factors. However, the validity of this assumption is often not assessed. Results We successfully predicted five new cis-regulatory modules by combining binding site identification with sequence conservation and compared these to unsuccessful predictions from a related approach not utilizing sequence conservation. Despite greatly improved predictive success, the positive set had similar degrees of sequence and binding site conservation as the negative set. We explored the reasons for this by mutagenizing putative binding sites in three cis-regulatory modules. A large proportion of the tested sites had little or no demonstrable role in mediating regulatory element activity. Examination of loss-of-function mutants also showed that some transcription factors supposedly binding to the modules are not required for their function. Conclusions Our results raise important questions about interpreting regulatory module predictions obtained by finding clusters of conserved binding sites. Attribution of function to these sites and their cognate transcription factors may be incorrect even when modules are successfully identified. Our study underscores the importance of empirical validation of computational results even when these results are in line with expectation.
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Affiliation(s)
- Marc S Halfon
- Department of Biochemistry, State University of New York at Buffalo, Buffalo, NY 14214, USA.
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Ludwig MZ, Manu, Kittler R, White KP, Kreitman M. Consequences of eukaryotic enhancer architecture for gene expression dynamics, development, and fitness. PLoS Genet 2011; 7:e1002364. [PMID: 22102826 PMCID: PMC3213169 DOI: 10.1371/journal.pgen.1002364] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2011] [Accepted: 09/14/2011] [Indexed: 12/13/2022] Open
Abstract
The regulatory logic of time- and tissue-specific gene expression has mostly been dissected in the context of the smallest DNA fragments that, when isolated, recapitulate native expression in reporter assays. It is not known if the genomic sequences surrounding such fragments, often evolutionarily conserved, have any biological function or not. Using an enhancer of the even-skipped gene of Drosophila as a model, we investigate the functional significance of the genomic sequences surrounding empirically identified enhancers. A 480 bp long "minimal stripe element" is able to drive even-skipped expression in the second of seven stripes but is embedded in a larger region of 800 bp containing evolutionarily conserved binding sites for required transcription factors. To assess the overall fitness contribution made by these binding sites in the native genomic context, we employed a gene-replacement strategy in which whole-locus transgenes, capable of rescuing even-skipped(-) lethality to adulthood, were substituted for the native gene. The molecular phenotypes were characterized by tagging Even-skipped with a fluorescent protein and monitoring gene expression dynamics in living embryos. We used recombineering to excise the sequences surrounding the minimal enhancer and site-specific transgenesis to create co-isogenic strains differing only in their stripe 2 sequences. Remarkably, the flanking sequences were dispensable for viability, proving the sufficiency of the minimal element for biological function under normal conditions. These sequences are required for robustness to genetic and environmental perturbation instead. The mutant enhancers had measurable sex- and dose-dependent effects on viability. At the molecular level, the mutants showed a destabilization of stripe placement and improper activation of downstream genes. Finally, we demonstrate through live measurements that the peripheral sequences are required for temperature compensation. These results imply that seemingly redundant regulatory sequences beyond the minimal enhancer are necessary for robust gene expression and that "robustness" itself must be an evolved characteristic of the wild-type enhancer.
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Affiliation(s)
- Michael Z. Ludwig
- Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, United States of America
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois, United States of America
| | - Manu
- Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, United States of America
| | - Ralf Kittler
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois, United States of America
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| | - Kevin P. White
- Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, United States of America
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois, United States of America
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| | - Martin Kreitman
- Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, United States of America
- Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois, United States of America
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