1
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Yang M, Ali O, Bjørås M, Wang J. Identifying functional regulatory mutation blocks by integrating genome sequencing and transcriptome data. iScience 2023; 26:107266. [PMID: 37520692 PMCID: PMC10371843 DOI: 10.1016/j.isci.2023.107266] [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: 10/19/2022] [Revised: 04/05/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023] Open
Abstract
Millions of single nucleotide variants (SNVs) exist in the human genome; however, it remains challenging to identify functional SNVs associated with diseases. We propose a non-encoding SNVs analysis tool bpb3, BayesPI-BAR version 3, aiming to identify the functional mutation blocks (FMBs) by integrating genome sequencing and transcriptome data. The identified FMBs display high frequency SNVs, significant changes in transcription factors (TFs) binding affinity and are nearby the regulatory regions of differentially expressed genes. A two-level Bayesian approach with a biophysical model for protein-DNA interactions is implemented, to compute TF-DNA binding affinity changes based on clustered position weight matrices (PWMs) from over 1700 TF-motifs. The epigenetic data, such as the DNA methylome can also be integrated to scan FMBs. By testing the datasets from follicular lymphoma and melanoma, bpb3 automatically and robustly identifies FMBs, demonstrating that bpb3 can provide insight into patho-mechanisms, and therapeutic targets from transcriptomic and genomic data.
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Affiliation(s)
- Mingyi Yang
- Department of Microbiology, Oslo University Hospital and University of Oslo, Oslo, Norway
- Department of Medical Biochemistry, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Omer Ali
- Department of Pathology, Oslo University Hospital - Norwegian Radium Hospital, Oslo, Norway
- Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Magnar Bjørås
- Department of Microbiology, Oslo University Hospital and University of Oslo, Oslo, Norway
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Junbai Wang
- Department of Clinical Molecular Biology (EpiGen), Akershus University Hospital and University of Oslo, Lørenskog, Norway
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2
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Ali O, Farooq A, Yang M, Jin VX, Bjørås M, Wang J. abc4pwm: affinity based clustering for position weight matrices in applications of DNA sequence analysis. BMC Bioinformatics 2022; 23:83. [PMID: 35240993 PMCID: PMC8896320 DOI: 10.1186/s12859-022-04615-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 02/18/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Transcription factor (TF) binding motifs are identified by high throughput sequencing technologies as means to capture Protein-DNA interactions. These motifs are often represented by consensus sequences in form of position weight matrices (PWMs). With ever-increasing pool of TF binding motifs from multiple sources, redundancy issues are difficult to avoid, especially when every source maintains its own database for collection. One solution can be to cluster biologically relevant or similar PWMs, whether coming from experimental detection or in silico predictions. However, there is a lack of efficient tools to cluster PWMs. Assessing quality of PWM clusters is yet another challenge. Therefore, new methods and tools are required to efficiently cluster PWMs and assess quality of clusters. RESULTS A new Python package Affinity Based Clustering for Position Weight Matrices (abc4pwm) was developed. It efficiently clustered PWMs from multiple sources with or without using DNA-Binding Domain (DBD) information, generated a representative motif for each cluster, evaluated the clustering quality automatically, and filtered out incorrectly clustered PWMs. Additionally, it was able to update human DBD family database automatically, classified known human TF PWMs to the respective DBD family, and performed TF motif searching and motif discovery by a new ensemble learning approach. CONCLUSION This work demonstrates applications of abc4pwm in the DNA sequence analysis for various high throughput sequencing data using ~ 1770 human TF PWMs. It recovered known TF motifs at gene promoters based on gene expression profiles (RNA-seq) and identified true TF binding targets for motifs predicted from ChIP-seq experiments. Abc4pwm is a useful tool for TF motif searching, clustering, quality assessment and integration in multiple types of sequence data analysis including RNA-seq, ChIP-seq and ATAC-seq.
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Affiliation(s)
- Omer Ali
- Department of Pathology, Oslo University Hospital - Norwegian Radium Hospital, Oslo, Norway
| | - Amna Farooq
- Department of Pathology, Oslo University Hospital - Norwegian Radium Hospital, Oslo, Norway
| | - Mingyi Yang
- Department of Medical Biochemistry, Oslo University Hospital and University of Oslo, Oslo, Norway.,Department of Microbiology, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Victor X Jin
- Department of Molecular Medicine, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Magnar Bjørås
- Department of Microbiology, Oslo University Hospital and University of Oslo, Oslo, Norway.,Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
| | - Junbai Wang
- Department of Clinical Molecular Biology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway. .,Department of Clinical Molecular Biology (EpiGen), Akershus University Hospital, Lørenskog, Norway.
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Zhang Q, Wang D, Han K, Huang DS. Predicting TF-DNA Binding Motifs from ChIP-seq Datasets Using the Bag-Based Classifier Combined With a Multi-Fold Learning Scheme. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:1743-1751. [PMID: 32946398 DOI: 10.1109/tcbb.2020.3025007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The rapid development of high-throughput sequencing technology provides unique opportunities for studying of transcription factor binding sites, but also brings new computational challenges. Recently, a series of discriminative motif discovery (DMD) methods have been proposed and offer promising solutions for addressing these challenges. However, because of the huge computation cost, most of them have to choose approximate schemes that either sacrifice the accuracy of motif representation or tune motif parameter indirectly. In this paper, we propose a bag-based classifier combined with a multi-fold learning scheme (BCMF) to discover motifs from ChIP-seq datasets. First, BCMF formulates input sequences as a labeled bag naturally. Then, a bag-based classifier, combining with a bag feature extracting strategy, is applied to construct the objective function, and a multi-fold learning scheme is used to solve it. Compared with the existing DMD tools, BCMF features three improvements: 1) Learning position weight matrix (PWM) directly in a continuous space; 2) Proposing to represent a positive bag with a feature fused by its k "most positive" patterns. 3) Applying a more advanced learning scheme. The experimental results on 134 ChIP-seq datasets show that BCMF substantially outperforms existing DMD methods (including DREME, HOMER, XXmotif, motifRG, EDCOD and our previous work).
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IGAP-integrative genome analysis pipeline reveals new gene regulatory model associated with nonspecific TF-DNA binding affinity. Comput Struct Biotechnol J 2020; 18:1270-1286. [PMID: 32612751 PMCID: PMC7303559 DOI: 10.1016/j.csbj.2020.05.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 05/17/2020] [Accepted: 05/19/2020] [Indexed: 11/23/2022] Open
Abstract
The human genome is regulated in a multi-dimensional way. While biophysical factors like Non-specific Transcription factor Binding Affinity (nTBA) act at DNA sequence level, other factors act above sequence levels such as histone modifications and 3-D chromosomal interactions. This multidimensionality of regulation requires many of these factors for a proper understanding of the regulatory landscape of the human genome. Here, we propose a new biophysical model for estimating nTBA. Integration of nTBA with chromatin modifications and chromosomal interactions, using a new Integrative Genome Analysis Pipeline (IGAP), reveals additive effects of nTBA to regulatory DNA sequences and identifies three types of genomic zones in the human genome (Inactive Genomic Zones, Poised Genomic Zones, and Active Genomic Zones). It also unveils a novel long distance gene regulatory model: chromosomal interactions reduce the physical distance between the high occupancy target (HOT) regions that results in high nTBA to DNA in the area, which in turn attract TFs to such regions with higher binding potential. These findings will help to elucidate the three-dimensional diffusion process that TFs use during their search for the right targets.
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Santana-Garcia W, Rocha-Acevedo M, Ramirez-Navarro L, Mbouamboua Y, Thieffry D, Thomas-Chollier M, Contreras-Moreira B, van Helden J, Medina-Rivera A. RSAT variation-tools: An accessible and flexible framework to predict the impact of regulatory variants on transcription factor binding. Comput Struct Biotechnol J 2019; 17:1415-1428. [PMID: 31871587 PMCID: PMC6906655 DOI: 10.1016/j.csbj.2019.09.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Revised: 09/22/2019] [Accepted: 09/25/2019] [Indexed: 02/06/2023] Open
Abstract
Gene regulatory regions contain short and degenerated DNA binding sites recognized by transcription factors (TFBS). When TFBS harbor SNPs, the DNA binding site may be affected, thereby altering the transcriptional regulation of the target genes. Such regulatory SNPs have been implicated as causal variants in Genome-Wide Association Study (GWAS) studies. In this study, we describe improved versions of the programs Variation-tools designed to predict regulatory variants, and present four case studies to illustrate their usage and applications. In brief, Variation-tools facilitate i) obtaining variation information, ii) interconversion of variation file formats, iii) retrieval of sequences surrounding variants, and iv) calculating the change on predicted transcription factor affinity scores between alleles, using motif scanning approaches. Notably, the tools support the analysis of haplotypes. The tools are included within the well-maintained suite Regulatory Sequence Analysis Tools (RSAT, http://rsat.eu), and accessible through a web interface that currently enables analysis of five metazoa and ten plant genomes. Variation-tools can also be used in command-line with any locally-installed Ensembl genome. Users can input personal collections of variants and motifs, providing flexibility in the analysis.
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Key Words
- Binding motifs
- CEU, Northern Europeans from Utah
- CRM, Cis-Regulatory Module
- GWAS, Genome Wide Association Studies
- LD, Linkage Disequilibrium
- MPRA, Massively Parallel Reporter Assays: MPRA
- PSSM, Position Specific Scoring Matrix
- Position specific scoring matrix
- ROC, Receiver Operating Characteristic
- RSAT, Regulatory Sequence Analysis Tools
- Regulatory variants
- SNP, Single Nucleotide Polymorphism
- SNPs
- SOIs, SNPs of Interest
- TF, Transcription Factor
- TFBS, Transcription Factor Binding Site
- Transcription factors
- eQTL, Expression Quantitative Trait Loci
- rsID, Reference SNP Identifier
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Affiliation(s)
- Walter Santana-Garcia
- Institut de Biologie de l’ENS (IBENS), Département de biologie, École normale supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France
- Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Campus Juriquilla, Blvd Juriquilla 3001, Santiago de Querétaro 76230, Mexico
| | - Maria Rocha-Acevedo
- Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Campus Juriquilla, Blvd Juriquilla 3001, Santiago de Querétaro 76230, Mexico
| | - Lucia Ramirez-Navarro
- Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Campus Juriquilla, Blvd Juriquilla 3001, Santiago de Querétaro 76230, Mexico
| | - Yvon Mbouamboua
- Fondation Congolaise pour la Recherche Médicale, Brazzaville, People’s Republic of Congo
- Aix-Marseille Univ, INSERM UMR S 1090, Theory and Approaches of Genome Complexity (TAGC), F-13288 Marseille, France
| | - Denis Thieffry
- Institut de Biologie de l’ENS (IBENS), Département de biologie, École normale supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France
| | - Morgane Thomas-Chollier
- Institut de Biologie de l’ENS (IBENS), Département de biologie, École normale supérieure, CNRS, INSERM, Université PSL, 75005 Paris, France
| | | | - Jacques van Helden
- Aix-Marseille Univ, INSERM UMR S 1090, Theory and Approaches of Genome Complexity (TAGC), F-13288 Marseille, France
- CNRS, Institut Français de Bioinformatique, IFB-core, UMS 3601, Evry, France
- Corresponding authors at: Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Campus Juriquilla, Blvd Juriquilla 3001, Santiago de Querétaro 76230, México (Medina-Rivera). Aix-Marseille Univ, INSERM UMR S 1090, Theory and Approaches of Genome Complexity (TAGC), F-13288 Marseille, France (J. van Heldenf).
| | - Alejandra Medina-Rivera
- Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Campus Juriquilla, Blvd Juriquilla 3001, Santiago de Querétaro 76230, Mexico
- Corresponding authors at: Laboratorio Internacional de Investigación sobre el Genoma Humano, Universidad Nacional Autónoma de México, Campus Juriquilla, Blvd Juriquilla 3001, Santiago de Querétaro 76230, México (Medina-Rivera). Aix-Marseille Univ, INSERM UMR S 1090, Theory and Approaches of Genome Complexity (TAGC), F-13288 Marseille, France (J. van Heldenf).
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Batmanov K, Delabie J, Wang J. BayesPI-BAR2: A New Python Package for Predicting Functional Non-coding Mutations in Cancer Patient Cohorts. Front Genet 2019; 10:282. [PMID: 31001324 PMCID: PMC6454009 DOI: 10.3389/fgene.2019.00282] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 03/15/2019] [Indexed: 12/21/2022] Open
Abstract
Most of somatic mutations in cancer occur outside of gene coding regions. These mutations may disrupt the gene regulation by affecting protein-DNA interaction. A study of these disruptions is important in understanding tumorigenesis. However, current computational tools process DNA sequence variants individually, when predicting the effect on protein-DNA binding. Thus, it is a daunting task to identify functional regulatory disturbances among thousands of mutations in a patient. Previously, we have reported and validated a pipeline for identifying functional non-coding somatic mutations in cancer patient cohorts, by integrating diverse information such as gene expression, spatial distribution of the mutations, and a biophysical model for estimating protein binding affinity. Here, we present a new user-friendly Python package BayesPI-BAR2 based on the proposed pipeline for integrative whole-genome sequence analysis. This may be the first prediction package that considers information from both multiple mutations and multiple patients. It is evaluated in follicular lymphoma and skin cancer patients, by focusing on sequence variants in gene promoter regions. BayesPI-BAR2 is a useful tool for predicting functional non-coding mutations in whole genome sequencing data: it allows identification of novel transcription factors (TFs) whose binding is altered by non-coding mutations in cancer. BayesPI-BAR2 program can analyze multiple datasets of genome-wide mutations at once and generate concise, easily interpretable reports for potentially affected gene regulatory sites. The package is freely available at http://folk.uio.no/junbaiw/BayesPI-BAR2/.
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Affiliation(s)
- Kirill Batmanov
- Department of Pathology, Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
| | - Jan Delabie
- Department of Pathology, University Health Network, Toronto, ON, Canada
| | - Junbai Wang
- Department of Pathology, Norwegian Radium Hospital, Oslo University Hospital, Oslo, Norway
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7
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Batmanov K, Wang J. Predicting Variation of DNA Shape Preferences in Protein-DNA Interaction in Cancer Cells with a New Biophysical Model. Genes (Basel) 2017; 8:E233. [PMID: 28927002 PMCID: PMC5615366 DOI: 10.3390/genes8090233] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 09/13/2017] [Accepted: 09/13/2017] [Indexed: 11/30/2022] Open
Abstract
DNA shape readout is an important mechanism of transcription factor target site recognition, in addition to the sequence readout. Several machine learning-based models of transcription factor-DNA interactions, considering DNA shape features, have been developed in recent years. Here, we present a new biophysical model of protein-DNA interactions by integrating the DNA shape properties. It is based on the neighbor dinucleotide dependency model BayesPI2, where new parameters are restricted to a subspace spanned by the dinucleotide form of DNA shape features. This allows a biophysical interpretation of the new parameters as a position-dependent preference towards specific DNA shape features. Using the new model, we explore the variation of DNA shape preferences in several transcription factors across various cancer cell lines and cellular conditions. The results reveal that there are DNA shape variations at FOXA1 (Forkhead Box Protein A1) binding sites in steroid-treated MCF7 cells. The new biophysical model is useful for elucidating the finer details of transcription factor-DNA interaction, as well as for predicting cancer mutation effects in the future.
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Affiliation(s)
- Kirill Batmanov
- Department of Pathology, Oslo University Hospital-Norwegian Radium Hospital, Montebello, 0310 Oslo,Norway.
| | - Junbai Wang
- Department of Pathology, Oslo University Hospital-Norwegian Radium Hospital, Montebello, 0310 Oslo,Norway.
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8
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Integrative whole-genome sequence analysis reveals roles of regulatory mutations in BCL6 and BCL2 in follicular lymphoma. Sci Rep 2017; 7:7040. [PMID: 28765546 PMCID: PMC5539289 DOI: 10.1038/s41598-017-07226-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Accepted: 06/27/2017] [Indexed: 02/02/2023] Open
Abstract
The contribution of mutations in regulatory regions to tumorigenesis has been the subject of many recent studies. We propose a new framework for integrative analysis of genome-wide sequencing data by considering diverse genetic information. This approach is applied to study follicular lymphoma (FL), a disease for which little is known about the contribution of regulatory gene mutations. Results from a test FL cohort revealed three novel highly recurrent regulatory mutation blocks near important genes implicated in FL, BCL6 and BCL2. Similar findings were detected in a validation FL cohort. We also found transcription factors (TF) whose binding may be disturbed by these mutations in FL: disruption of FOX TF family near the BCL6 promoter may result in reduced BCL6 expression, which then increases BCL2 expression over that caused by BCL2 gene translocation. Knockdown experiments of two TF hits (FOXD2 or FOXD3) were performed in human B lymphocytes verifying that they modulate BCL6/BCL2 according to the computationally predicted effects of the SNVs on TF binding. Overall, our proposed integrative analysis facilitates non-coding driver identification and the new findings may enhance the understanding of FL.
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Zhang H, Zhu L, Huang DS. WSMD: weakly-supervised motif discovery in transcription factor ChIP-seq data. Sci Rep 2017; 7:3217. [PMID: 28607381 PMCID: PMC5468353 DOI: 10.1038/s41598-017-03554-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Accepted: 05/02/2017] [Indexed: 01/24/2023] Open
Abstract
Although discriminative motif discovery (DMD) methods are promising for eliciting motifs from high-throughput experimental data, due to consideration of computational expense, most of existing DMD methods have to choose approximate schemes that greatly restrict the search space, leading to significant loss of predictive accuracy. In this paper, we propose Weakly-Supervised Motif Discovery (WSMD) to discover motifs from ChIP-seq datasets. In contrast to the learning strategies adopted by previous DMD methods, WSMD allows a "global" optimization scheme of the motif parameters in continuous space, thereby reducing the information loss of model representation and improving the quality of resultant motifs. Meanwhile, by exploiting the connection between DMD framework and existing weakly supervised learning (WSL) technologies, we also present highly scalable learning strategies for the proposed method. The experimental results on both real ChIP-seq datasets and synthetic datasets show that WSMD substantially outperforms former DMD methods (including DREME, HOMER, XXmotif, motifRG and DECOD) in terms of predictive accuracy, while also achieving a competitive computational speed.
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Affiliation(s)
- Hongbo Zhang
- Institute of Machine Learning and Systems Biology, College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, P.R. China
| | - Lin Zhu
- Institute of Machine Learning and Systems Biology, College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, P.R. China
| | - De-Shuang Huang
- Institute of Machine Learning and Systems Biology, College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, P.R. China.
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Yi S, Lin S, Li Y, Zhao W, Mills GB, Sahni N. Functional variomics and network perturbation: connecting genotype to phenotype in cancer. Nat Rev Genet 2017; 18:395-410. [PMID: 28344341 DOI: 10.1038/nrg.2017.8] [Citation(s) in RCA: 70] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Proteins interact with other macromolecules in complex cellular networks for signal transduction and biological function. In cancer, genetic aberrations have been traditionally thought to disrupt the entire gene function. It has been increasingly appreciated that each mutation of a gene could have a subtle but unique effect on protein function or network rewiring, contributing to diverse phenotypic consequences across cancer patient populations. In this Review, we discuss the current understanding of cancer genetic variants, including the broad spectrum of mutation classes and the wide range of mechanistic effects on gene function in the context of signalling networks. We highlight recent advances in computational and experimental strategies to study the diverse functional and phenotypic consequences of mutations at the base-pair resolution. Such information is crucial to understanding the complex pleiotropic effect of cancer genes and provides a possible link between genotype and phenotype in cancer.
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Affiliation(s)
- Song Yi
- Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Shengda Lin
- Department of Medicine, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Yongsheng Li
- Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Wei Zhao
- Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Gordon B Mills
- Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA
| | - Nidhi Sahni
- Department of Systems Biology, University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.,Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas 77030, USA
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11
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Shi W, Fornes O, Mathelier A, Wasserman WW. Evaluating the impact of single nucleotide variants on transcription factor binding. Nucleic Acids Res 2016; 44:10106-10116. [PMID: 27492288 PMCID: PMC5137422 DOI: 10.1093/nar/gkw691] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Revised: 07/25/2016] [Accepted: 07/26/2016] [Indexed: 12/21/2022] Open
Abstract
Diseases and phenotypes caused by disrupted transcription factor (TF) binding are being identified, but progress is hampered by our limited capacity to predict such functional alterations. Improving predictions may be dependent on expanding the set of bona fide TF binding alterations. Allele-specific binding (ASB) events, where TFs preferentially bind to one of the two alleles at heterozygous sites, reveal the impact of sequence variations in altered TF binding. Here, we present the largest ASB compilation to our knowledge, 10 765 ASB events retrieved from 45 ENCODE ChIP-Seq data sets. Our analysis showed that ASB events were frequently associated with motif alterations of the ChIP'ed TF and potential partner TFs, allelic difference of DNase I hypersensitivity and allelic difference of histone modifications. For TF dimers bound symmetrically to DNA, ASB data revealed that central positions of the TF binding motifs were disproportionately important for binding. Lastly, the impact of variation on TF binding was predicted by a classification model incorporating all the investigated features of ASB events. Classification models using only DNase I hypersensitivity and sequence data exhibited predictive accuracy approaching the models with substantially more features. Taken together, the combination of ASB data and the classification model represents an important step toward elucidating regulatory variants across the human genome.
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Affiliation(s)
- Wenqiang Shi
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, Child & Family Research Institute, University of British Columbia, 950 28th Ave W, Vancouver, BC V5Z 4H4, Canada.,Bioinformatics Graduate Program, University of British Columbia, 2329 W Mall, Vancouver, BC V6T 1Z4, Canada
| | - Oriol Fornes
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, Child & Family Research Institute, University of British Columbia, 950 28th Ave W, Vancouver, BC V5Z 4H4, Canada
| | - Anthony Mathelier
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, Child & Family Research Institute, University of British Columbia, 950 28th Ave W, Vancouver, BC V5Z 4H4, Canada.,Centre for Molecular Medicine Norway (NCMM), Nordic EMBL partnership, University of Oslo and Oslo University Hospital, Norway
| | - Wyeth W Wasserman
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, Child & Family Research Institute, University of British Columbia, 950 28th Ave W, Vancouver, BC V5Z 4H4, Canada
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