1
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Deyneko IV. BestCRM: An Exhaustive Search for Optimal Cis-Regulatory Modules in Promoters Accelerated by the Multidimensional Hash Function. Int J Mol Sci 2024; 25:1903. [PMID: 38339181 PMCID: PMC10856692 DOI: 10.3390/ijms25031903] [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] [Received: 12/01/2023] [Revised: 01/24/2024] [Accepted: 01/26/2024] [Indexed: 02/12/2024] Open
Abstract
The concept of cis-regulatory modules located in gene promoters represents today's vision of the organization of gene transcriptional regulation. Such modules are a combination of two or more single, short DNA motifs. The bioinformatic identification of such modules belongs to so-called NP-hard problems with extreme computational complexity, and therefore, simplifications, assumptions, and heuristics are usually deployed to tackle the problem. In practice, this requires, first, many parameters to be set before the search, and second, it leads to the identification of locally optimal results. Here, a novel method is presented, aimed at identifying the cis-regulatory elements in gene promoters based on an exhaustive search of all the feasible modules' configurations. All required parameters are automatically estimated using positive and negative datasets. To be computationally efficient, the search is accelerated using a multidimensional hash function, allowing the search to complete in a few hours on a regular laptop (for example, a CPU Intel i7, 3.2 GH, 32 Gb RAM). Tests on an established benchmark and real data show better performance of BestCRM compared to the available methods according to several metrics like specificity, sensitivity, AUC, etc. A great practical advantage of the method is its minimum number of input parameters-apart from positive and negative promoters, only a desired level of module presence in promoters is required.
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Affiliation(s)
- Igor V Deyneko
- K.A. Timiryazev Institute of Plant Physiology RAS, 35 Botanicheskaya Str., Moscow 127276, Russia
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2
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Deyneko IV. Guidelines on the performance evaluation of motif recognition methods in bioinformatics. Front Genet 2023; 14:1135320. [PMID: 36824436 PMCID: PMC9941176 DOI: 10.3389/fgene.2023.1135320] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 01/19/2023] [Indexed: 02/09/2023] Open
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3
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A systematic study of HIF1A cofactors in hypoxic cancer cells. Sci Rep 2022; 12:18962. [PMID: 36347941 PMCID: PMC9643333 DOI: 10.1038/s41598-022-23060-9] [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: 08/10/2022] [Accepted: 10/25/2022] [Indexed: 11/09/2022] Open
Abstract
Hypoxia inducible factor 1 alpha (HIF1A) is a transcription factor (TF) that forms highly structural and functional protein-protein interactions with other TFs to promote gene expression in hypoxic cancer cells. However, despite the importance of these TF-TF interactions, we still lack a comprehensive view of many of the TF cofactors involved and how they cooperate. In this study, we systematically studied HIF1A cofactors in eight cancer cell lines using the computational motif mining tool, SIOMICS, and discovered 201 potential HIF1A cofactors, which included 21 of the 29 known HIF1A cofactors in public databases. These 201 cofactors were statistically and biologically significant, with 19 of the top 37 cofactors in our study directly validated in the literature. The remaining 18 were novel cofactors. These discovered cofactors can be essential to HIF1A's regulatory functions and may lead to the discovery of new therapeutic targets in cancer treatment.
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4
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Dang DT, Nguyen NT, Hwang D. Hybrid genetic algorithms for the determination of DNA motifs to satisfy postulate 2-Optimality. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03491-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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5
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Castellana S, Biagini T, Parca L, Petrizzelli F, Bianco SD, Vescovi AL, Carella M, Mazza T. A comparative benchmark of classic DNA motif discovery tools on synthetic data. Brief Bioinform 2021; 22:6341664. [PMID: 34351399 DOI: 10.1093/bib/bbab303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/08/2021] [Accepted: 07/15/2021] [Indexed: 01/01/2023] Open
Abstract
Hundreds of human proteins were found to establish transient interactions with rather degenerated consensus DNA sequences or motifs. Identifying these motifs and the genomic sites where interactions occur represent one of the most challenging research goals in modern molecular biology and bioinformatics. The last twenty years witnessed an explosion of computational tools designed to perform this task, whose performance has been last compared fifteen years ago. Here, we survey sixteen of them, benchmark their ability to identify known motifs nested in twenty-nine simulated sequence datasets, and finally report their strengths, weaknesses, and complementarity.
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Affiliation(s)
- Stefano Castellana
- Bioinformatics Unit, IRCCS Casa Sollievo della Sofferenza, S. Giovanni Rotondo 71013, Italy
| | - Tommaso Biagini
- Bioinformatics Unit, IRCCS Casa Sollievo della Sofferenza, S. Giovanni Rotondo 71013, Italy
| | - Luca Parca
- Bioinformatics Unit, IRCCS Casa Sollievo della Sofferenza, S. Giovanni Rotondo 71013, Italy
| | - Francesco Petrizzelli
- Bioinformatics Unit, IRCCS Casa Sollievo della Sofferenza, S. Giovanni Rotondo 71013, Italy.,Department of Experimental Medicine, Sapienza University of Rome, Rome 00161, Italy
| | | | - Angelo Luigi Vescovi
- ISBReMIT Institute for Stem Cell Biology, Regenerative Medicine and Innovative Therapies, IRCSS Casa Sollievo della Sofferenza, San Giovanni Rotondo (FG), 71013, Italy
| | - Massimo Carella
- Medical Genetics Unit, IRCCS Casa Sollievo della Sofferenza, S. Giovanni Rotondo 71013, Italy
| | - Tommaso Mazza
- Bioinformatics Unit, IRCCS Casa Sollievo della Sofferenza, S. Giovanni Rotondo 71013, Italy
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6
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Li JY, Jin S, Tu XM, Ding Y, Gao G. Identifying complex motifs in massive omics data with a variable-convolutional layer in deep neural network. Brief Bioinform 2021; 22:6312656. [PMID: 34219140 DOI: 10.1093/bib/bbab233] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/25/2021] [Accepted: 05/28/2021] [Indexed: 01/10/2023] Open
Abstract
Motif identification is among the most common and essential computational tasks for bioinformatics and genomics. Here we proposed a novel convolutional layer for deep neural network, named variable convolutional (vConv) layer, for effective motif identification in high-throughput omics data by learning kernel length from data adaptively. Empirical evaluations on DNA-protein binding and DNase footprinting cases well demonstrated that vConv-based networks have superior performance to their convolutional counterparts regardless of model complexity. Meanwhile, vConv could be readily integrated into multi-layer neural networks as an 'in-place replacement' of canonical convolutional layer. All source codes are freely available on GitHub for academic usage.
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Affiliation(s)
- Jing-Yi Li
- Biomedical Pioneering Innovation Center & Beijing Advanced Innovation Center for Genomics, Center for Bioinformatics, and State Key Laboratory of Protein and Plant Gene Research at School of Life Sciences, Peking University, Beijing 100871, China
| | - Shen Jin
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Xin-Ming Tu
- Biomedical Pioneering Innovation Center & Beijing Advanced Innovation Center for Genomics, Center for Bioinformatics, and State Key Laboratory of Protein and Plant Gene Research at School of Life Sciences, Peking University, Beijing 100871, China
| | - Yang Ding
- Biomedical Pioneering Innovation Center & Beijing Advanced Innovation Center for Genomics, Center for Bioinformatics, and State Key Laboratory of Protein and Plant Gene Research at School of Life Sciences, Peking University, Beijing 100871, China
| | - Ge Gao
- Biomedical Pioneering Innovation Center & Beijing Advanced Innovation Center for Genomics, Center for Bioinformatics, and State Key Laboratory of Protein and Plant Gene Research at School of Life Sciences, Peking University, Beijing 100871, China
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7
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Zhang Y, Wang C, Feng X, Chen X, Zhang W. Redondoviridae and periodontitis: a case–control study and identification of five novel redondoviruses from periodontal tissues. Virus Evol 2021; 7:veab033. [PMID: 35186324 PMCID: PMC8088815 DOI: 10.1093/ve/veab033] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Abstract
Redondoviridae is a family of DNA viruses recently identified in the human oro-respiratory tract. However, the characteristics of this new virus family are not yet fully understood. The aim of the present study was to investigate the relationship between redondoviruses and chronic periodontitis. In addition, the complete circular genome, phylogenetic relationship, and biological characteristics of novel redondoviruses were analyzed. The gingival tissues of healthy individuals (n = 120) and periodontitis patients (n = 120) were analyzed using nested polymerase chain reaction assays. The prevalence of redondovirus infection in the periodontitis group was 71.67%. Logistic regression analysis revealed an association between redondoviruses and chronic periodontitis after controlling the confounding factors (odds ratio = 2.53). Five novel redondoviruses, named ‘human periodontal circular-like virus (HPeCV)’, were identified in patients with periodontitis and detailed genetic analysis of the viruses was performed. The 3,035–3,056 bp genome contained a capsid protein, a replication-associated protein, an open reading frame 3 protein, and a stem-loop structure. Phylogenetic analysis demonstrated that HPeCV-1, HPeCV-10, and HPeCV-25 formed a cluster. Recombination may be common in the genomes of HPeCVs. Potential antigenic epitopes in the capsid protein, which may be involved in the host immune response, were predicted. In conclusion, periodontitis patients had a significantly higher prevalence of redondoviruses than healthy controls. Genetic characterization enhanced the current understanding of the genetic diversity and pathogenicity of redondoviruses as well as their association with periodontitis in humans. The data presented in this article will expand the current understanding of the epidemiology, genetic diversity, and pathogenicity of redondoviruses.
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Affiliation(s)
- Yu Zhang
- Department of Preventive Dentistry, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China
| | - Chunmei Wang
- Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China
| | - Xiping Feng
- Department of Preventive Dentistry, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China
| | - Xi Chen
- Department of Preventive Dentistry, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- National Clinical Research Center for Oral Diseases, Shanghai, China
- Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China
| | - Wen Zhang
- School of Medicine, Jiangsu University, Zhenjiang, China
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8
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Lu K, Yang K, Niyongabo E, Shu Z, Wang J, Chang K, Zou Q, Jiang J, Jia C, Liu B, Zhou X. Integrated network analysis of symptom clusters across disease conditions. J Biomed Inform 2020; 107:103482. [PMID: 32535270 DOI: 10.1016/j.jbi.2020.103482] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Revised: 05/18/2020] [Accepted: 06/08/2020] [Indexed: 10/24/2022]
Abstract
Identifying the symptom clusters (two or more related symptoms) with shared underlying molecular mechanisms has been a vital analysis task to promote the symptom science and precision health. Related studies have applied the clustering algorithms (e.g. k-means, latent class model) to detect the symptom clusters mostly from various kinds of clinical data. In addition, they focused on identifying the symptom clusters (SCs) for a specific disease, which also mainly concerned with the clinical regularities for symptom management. Here, we utilized a network-based clustering algorithm (i.e., BigCLAM) to obtain 208 typical SCs across disease conditions on a large-scale symptom network derived from integrated high-quality disease-symptom associations. Furthermore, we evaluated the underlying shared molecular mechanisms for SCs, i.e., shared genes, protein-protein interaction (PPI) and gene functional annotations using integrated networks and similarity measures. We found that the symptoms in the same SCs tend to share a higher degree of genes, PPIs and have higher functional homogeneities. In addition, we found that most SCs have related symptoms with shared underlying molecular mechanisms (e.g. enriched pathways) across different disease conditions. Our work demonstrated that the integrated network analysis method could be used for identifying robust SCs and investigate the molecular mechanisms of these SCs, which would be valuable for symptom science and precision health.
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Affiliation(s)
- Kezhi Lu
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Kuo Yang
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Edouard Niyongabo
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Zixin Shu
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Jingjing Wang
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Kai Chang
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Qunsheng Zou
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Jiyue Jiang
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Caiyan Jia
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
| | - Baoyan Liu
- Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
| | - Xuezhong Zhou
- Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China; Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
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9
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Carazo F, Romero JP, Rubio A. Upstream analysis of alternative splicing: a review of computational approaches to predict context-dependent splicing factors. Brief Bioinform 2020; 20:1358-1375. [PMID: 29390045 DOI: 10.1093/bib/bby005] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Revised: 12/14/2017] [Indexed: 12/13/2022] Open
Abstract
Alternative splicing (AS) has shown to play a pivotal role in the development of diseases, including cancer. Specifically, all the hallmarks of cancer (angiogenesis, cell immortality, avoiding immune system response, etc.) are found to have a counterpart in aberrant splicing of key genes. Identifying the context-specific regulators of splicing provides valuable information to find new biomarkers, as well as to define alternative therapeutic strategies. The computational models to identify these regulators are not trivial and require three conceptual steps: the detection of AS events, the identification of splicing factors that potentially regulate these events and the contextualization of these pieces of information for a specific experiment. In this work, we review the different algorithmic methodologies developed for each of these tasks. Main weaknesses and strengths of the different steps of the pipeline are discussed. Finally, a case study is detailed to help the reader be aware of the potential and limitations of this computational approach.
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10
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Baumgarten N, Schmidt F, Schulz MH. Improved linking of motifs to their TFs using domain information. Bioinformatics 2020; 36:1655-1662. [PMID: 31742324 PMCID: PMC7703792 DOI: 10.1093/bioinformatics/btz855] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 11/08/2019] [Accepted: 11/16/2019] [Indexed: 11/23/2022] Open
Abstract
Motivation A central aim of molecular biology is to identify mechanisms of transcriptional regulation. Transcription factors (TFs), which are DNA-binding proteins, are highly involved in these processes, thus a crucial information is to know where TFs interact with DNA and to be aware of the TFs’ DNA-binding motifs. For that reason, computational tools exist that link DNA-binding motifs to TFs either without sequence information or based on TF-associated sequences, e.g. identified via a chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiment. In this paper, we present MASSIF, a novel method to improve the performance of existing tools that link motifs to TFs relying on TF-associated sequences. MASSIF is based on the idea that a DNA-binding motif, which is correctly linked to a TF, should be assigned to a DNA-binding domain (DBD) similar to that of the mapped TF. Because DNA-binding motifs are in general not linked to DBDs, it is not possible to compare the DBD of a TF and the motif directly. Instead we created a DBD collection, which consist of TFs with a known DBD and an associated motif. This collection enables us to evaluate how likely it is that a linked motif and a TF of interest are associated to the same DBD. We named this similarity measure domain score, and represent it as a P-value. We developed two different ways to improve the performance of existing tools that link motifs to TFs based on TF-associated sequences: (i) using meta-analysis to combine P-values from one or several of these tools with the P-value of the domain score and (ii) filter unlikely motifs based on the domain score. Results We demonstrate the functionality of MASSIF on several human ChIP-seq datasets, using either motifs from the HOCOMOCO database or de novo identified ones as input motifs. In addition, we show that both variants of our method improve the performance of tools that link motifs to TFs based on TF-associated sequences significantly independent of the considered DBD type. Availability and implementation MASSIF is freely available online at https://github.com/SchulzLab/MASSIF. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Nina Baumgarten
- Institute for Cardiovascular Regeneration, Goethe University, Frankfurt am Main 60590, Germany.,German Center for Cardiovascular Regeneration, Partner Site Rhein-Main, Frankfurt am Main 60590, Germany
| | - Florian Schmidt
- High-throughput Genomics & Systems Biology, Cluster of Excellence MMCI, Saarland University.,Research Group Computational Biology, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken 66123, Germany
| | - Marcel H Schulz
- Institute for Cardiovascular Regeneration, Goethe University, Frankfurt am Main 60590, Germany.,German Center for Cardiovascular Regeneration, Partner Site Rhein-Main, Frankfurt am Main 60590, Germany.,High-throughput Genomics & Systems Biology, Cluster of Excellence MMCI, Saarland University.,Research Group Computational Biology, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken 66123, Germany
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11
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Li T, Zhang X, Luo F, Wu FX, Wang J. MultiMotifMaker: A Multi-Thread Tool for Identifying DNA Methylation Motifs from Pacbio Reads. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:220-225. [PMID: 30059318 DOI: 10.1109/tcbb.2018.2861399] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The methylation of DNA is an important mechanism to control biological processes. Recently, the Pacbio SMRT technology provides a new way to identify base methylation in the genome. MotifMaker is a tool developed by Pacbio for discovering DNA methylation motifs from methylated DNA sequences. However, MotifMaker is single-threaded and computational expensive for identifying methylation motifs from large genomes. Here, we present an efficient motif finding algorithm (MultiMotifMaker) by implementing multi threads of the MotifMaker. The MultiMotifMaker speeds up the motif search about 8-9 times on a 32 core computer comparing to MotifMaker. MultiMotifMaker makes it possible to identify methylation motifs from Pacbio reads for large genomes.
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12
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Ngo V, Wang M, Wang W. Finding de novo methylated DNA motifs. Bioinformatics 2019; 35:3287-3293. [PMID: 30726880 PMCID: PMC6748772 DOI: 10.1093/bioinformatics/btz079] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 01/14/2019] [Accepted: 02/04/2019] [Indexed: 01/24/2023] Open
Abstract
MOTIVATION Increasing evidence has shown that nucleotide modifications such as methylation and hydroxymethylation on cytosine would greatly impact the binding of transcription factors (TFs). However, there is a lack of motif finding algorithms with the function to search for motifs with modified bases. In this study, we expand on our previous motif finding pipeline Epigram to provide systematic de novo motif discovery and performance evaluation on methylated DNA motifs. RESULTS mEpigram outperforms both MEME and DREME on finding modified motifs in simulated data that mimics various motif enrichment scenarios. Furthermore we were able to identify methylated motifs in Arabidopsis DNA affinity purification sequencing (DAP-seq) data that were previously demonstrated to contain such motifs. When applied to TF ChIP-seq and DNA methylome data in H1 and GM12878, our method successfully identified novel methylated motifs that can be recognized by the TFs or their co-factors. We also observed spacing constraint between the canonical motif of the TF and the newly discovered methylated motifs, which suggests operative recognition of these cis-elements by collaborative proteins. AVAILABILITY AND IMPLEMENTATION The mEpigram program is available at http://wanglab.ucsd.edu/star/mEpigram. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Vu Ngo
- Graduate Program of Bioinformatics and Systems Biology, University of California at San Diego, La Jolla, CA, USA
| | - Mengchi Wang
- Graduate Program of Bioinformatics and Systems Biology, University of California at San Diego, La Jolla, CA, USA
| | - Wei Wang
- Graduate Program of Bioinformatics and Systems Biology, University of California at San Diego, La Jolla, CA, USA
- Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California at San Diego, La Jolla, CA, USA
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13
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Zhang S, Liang Y, Wang X, Su Z, Chen Y. FisherMP: fully parallel algorithm for detecting combinatorial motifs from large ChIP-seq datasets. DNA Res 2019; 26:231-242. [PMID: 30957858 PMCID: PMC6589551 DOI: 10.1093/dnares/dsz004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Accepted: 03/05/2019] [Indexed: 11/14/2022] Open
Abstract
Detecting binding motifs of combinatorial transcription factors (TFs) from chromatin immunoprecipitation sequencing (ChIP-seq) experiments is an important and challenging computational problem for understanding gene regulations. Although a number of motif-finding algorithms have been presented, most are either time consuming or have sub-optimal accuracy for processing large-scale datasets. In this article, we present a fully parallelized algorithm for detecting combinatorial motifs from ChIP-seq datasets by using Fisher combined method and OpenMP parallel design. Large scale validations on both synthetic data and 350 ChIP-seq datasets from the ENCODE database showed that FisherMP has not only super speeds on large datasets, but also has high accuracy when compared with multiple popular methods. By using FisherMP, we successfully detected combinatorial motifs of CTCF, YY1, MAZ, STAT3 and USF2 in chromosome X, suggesting that they are functional co-players in gene regulation and chromosomal organization. Integrative and statistical analysis of these TF-binding peaks clearly demonstrate that they are not only highly coordinated with each other, but that they are also correlated with histone modifications. FisherMP can be applied for integrative analysis of binding motifs and for predicting cis-regulatory modules from a large number of ChIP-seq datasets.
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Affiliation(s)
- Shaoqiang Zhang
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin, China
| | - Ying Liang
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin, China
| | - Xiangyun Wang
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin, China
| | - Zhengchang Su
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin, China
- Department of Bioinformatics and Genomics, the University of North Carolina at Charlotte, NC, USA
| | - Yong Chen
- Department of Biological Sciences, Center for Systems Biology, the University of Texas at Dallas, Richardson, TX, USA
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14
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Emamjomeh A, Choobineh D, Hajieghrari B, MahdiNezhad N, Khodavirdipour A. DNA-protein interaction: identification, prediction and data analysis. Mol Biol Rep 2019; 46:3571-3596. [PMID: 30915687 DOI: 10.1007/s11033-019-04763-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 03/14/2019] [Indexed: 12/30/2022]
Abstract
Life in living organisms is dependent on specific and purposeful interaction between other molecules. Such purposeful interactions make the various processes inside the cells and the bodies of living organisms possible. DNA-protein interactions, among all the types of interactions between different molecules, are of considerable importance. Currently, with the development of numerous experimental techniques, diverse methods are convenient for recognition and investigating such interactions. While the traditional experimental techniques to identify DNA-protein complexes are time-consuming and are unsuitable for genome-scale studies, the current high throughput approaches are more efficient in determining such interaction at a large-scale, but they are clearly too costly to be practice for daily applications. Hence, according to the availability of much information related to different biological sequences and clearing different dimensions of conditions in which such interactions are formed, with the developments related to the computer, mathematics, and statistics motivate scientists to develop bioinformatics tools for prediction the interaction site(s). Until now, there has been much progress in this field. In this review, the factors and conditions governing the interaction and the laboratory techniques for examining such interactions are addressed. In addition, developed bioinformatics tools are introduced and compared for this reason and, in the end, several suggestions are offered for the promotion of such tools in prediction with much more precision.
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Affiliation(s)
- Abbasali Emamjomeh
- Laboratory of Computational Biotechnology and Bioinformatics (CBB), Department of Plant Breeding and Biotechnology (PBB), University of Zabol, Zabol, 98615-538, Iran.
| | - Darush Choobineh
- Agricultural Biotechnology, Department of Plant Breeding and Biotechnology (PBB), Faculty of Agriculture, University of Zabol, Zabol, Iran
| | - Behzad Hajieghrari
- Department of Agricultural Biotechnology, College of Agriculture, Jahrom University, Jahrom, 74135-111, Iran.
| | - Nafiseh MahdiNezhad
- Laboratory of Computational Biotechnology and Bioinformatics (CBB), Department of Plant Breeding and Biotechnology (PBB), University of Zabol, Zabol, 98615-538, Iran
| | - Amir Khodavirdipour
- Division of Human Genetics, Department of Anatomy, St. John's hospital, Bangalore, India
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15
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Tran NTL, Huang CH. Performance evaluation for MOTIFSIM. Biol Proced Online 2018; 20:23. [PMID: 30574025 PMCID: PMC6299673 DOI: 10.1186/s12575-018-0088-3] [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: 11/04/2018] [Accepted: 12/07/2018] [Indexed: 11/10/2022] Open
Abstract
Background Previous studies show various results obtained from different motif finders for an identical dataset. This is largely due to the fact that these tools use different strategies and possess unique features for discovering the motifs. Hence, using multiple tools and methods has been suggested because the motifs commonly reported by them are more likely to be biologically significant. Results The common significant motifs from multiple tools can be obtained by using MOTIFSIM tool. In this work, we evaluated the performance of MOTIFSIM in three aspects. First, we compared the pair-wise comparison technique of MOTIFSIM with the un-gapped Smith-Waterman algorithm and four common distance metrics: average Kullback-Leibler, average log-likelihood ratio, Chi-Square distance, and Pearson Correlation Coefficient. Second, we compared the performance of MOTIFSIM with RSAT Matrix-clustering tool for motif clustering. Lastly, we evaluated the performances of nineteen motif finders and the reliability of MOTIFSIM for identifying the common significant motifs from multiple tools. Conclusions The pair-wise comparison results reveal that MOTIFSIM attains better performance than the un-gapped Smith-Waterman algorithm and four distance metrics. The clustering results also demonstrate that MOTIFSIM achieves similar or even better performance than RSAT Matrix-clustering. Furthermore, the findings indicate if the motif detection does not require a special tool for detecting a specific type of motif then using multiple motif finders and combining with MOTIFSIM for obtaining the common significant motifs, it improved the results for DNA motif detection. Electronic supplementary material The online version of this article (10.1186/s12575-018-0088-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ngoc Tam L Tran
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269 USA
| | - Chun-Hsi Huang
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269 USA
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16
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Tran NTL, Huang CH. MODSIDE: a motif discovery pipeline and similarity detector. BMC Genomics 2018; 19:755. [PMID: 30340511 PMCID: PMC6194616 DOI: 10.1186/s12864-018-5148-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2018] [Accepted: 10/08/2018] [Indexed: 01/06/2023] Open
Abstract
Background Previous studies demonstrate the usefulness of using multiple tools and methods for improving the accuracy of motif detection. Over the past years, numerous motif discovery pipelines have been developed. However, they typically report only the top ranked results either from individual motif finders or from a combination of multiple tools and algorithms. Results Here we present MODSIDE, a motif discovery pipeline and similarity detector. The pipeline integrated four de novo motif finders: ChIPMunk, MEME, Weeder, and XXmotif. It also incorporated a motif similarity detection tool MOTIFSIM. MODSIDE was designed for delivering not only the predictive results from individual motif finders but also the comparison results for multiple tools. The results include the common significant motifs from multiple tools, the motifs detected by some tools but not by others, and the best matches for each motif in the motif collection of multiple tools. MODSIDE also possesses other useful features for merging similar motifs and clustering motifs into motif trees. Conclusions We evaluated MODSIDE and its adopted motif finders on 16 benchmark datasets. The statistical results demonstrate MODSIDE achieves better accuracy than individual motif finders. We also compared MODSIDE with two popular motif discovery pipelines: MEME-ChIP and RSAT peak-motifs. The comparison results reveal MODSIDE attains similar performance as RSAT peak-motifs but better accuracy than MEME-ChIP. In addition, MODSIDE is able to deliver various comparison results that are not offered by MEME-ChIP, RSAT peak-motifs, and other existing motif discovery pipelines. Electronic supplementary material The online version of this article (10.1186/s12864-018-5148-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ngoc Tam L Tran
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, 06269, USA.
| | - Chun-Hsi Huang
- Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, 06269, USA
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17
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Lee NK, Li X, Wang D. A comprehensive survey on genetic algorithms for DNA motif prediction. Inf Sci (N Y) 2018. [DOI: 10.1016/j.ins.2018.07.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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18
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Zhang J, Kai L, Zhang W, Yin Y, Wang W. Association between genetic variants in p53 binding sites and risks of osteosarcoma in a Chinese population: a two-stage case-control study. Cancer Biol Ther 2018; 19:994-997. [PMID: 29595404 DOI: 10.1080/15384047.2018.1456607] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Osteosarcoma (OS) is one of the most common bone malignancies in children and adolescents. To date, inaugural mechanism of OS was considered as a complex process and was still not clear. The p53 gene, most important tumor suppressors, was associated with risk of many tumors, including OS. In current study, we evaluated the relationship between genetic variation of the p53 binding site and the OS susceptibility through a two-stage case-control study in Chinese population. We found that rs1295925 (OR = 0.85; 95 CI = 0.76-0.94; P = 0.003) and rs3787547 (OR = 1.27; 95 CI = 1.11-1.45; P = 4.0 × 10-4) was significantly with OS susceptibility. Compared with those with rs1295925-TT genotype, and the risk of OS was significantly lower in individuals with CT genotype (OR = 0.77; 95 CI = 0.65-0.92) and CC genotype (OR = 0.75; 95 CI = 0.60-0.93). Compared with those with rs3787547-GG genotype, and the risk of OS was significantly higher in individuals with AG genotype (OR = 1.32; 95 CI = 1.10-1.58) and AA genotype (OR = 1.46; 95 CI = 1.11-1.92). To sum up, our results prove that SNP rs1295925 and rs3787547 play an important role in the etiology of OS, suggesting them as the potential genetic modifier for OS development.
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Affiliation(s)
- Jingzhe Zhang
- a Department of Orthopedics , China-Japan Union Hospital Of Jilin University , Changchun130033, Jilin Province , China
| | - Li Kai
- b Department of Anesthesiology , China-Japan Union Hospital Of Jilin University , Changchun130033, Jilin Province , China
| | - Wenlong Zhang
- c Department of Hematology , China-Japan Union Hospital Of Jilin University , Changchun130033, Jilin Province , China
| | - Yu Yin
- d Department of Neurology , China-Japan Union Hospital Of Jilin University , Changchun130033, Jilin Province , China
| | - Wenjun Wang
- a Department of Orthopedics , China-Japan Union Hospital Of Jilin University , Changchun130033, Jilin Province , China
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19
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Montrose L, Faulk C, Francis J, Dolinoy D. Perinatal lead (Pb) exposure results in sex and tissue-dependent adult DNA methylation alterations in murine IAP transposons. ENVIRONMENTAL AND MOLECULAR MUTAGENESIS 2017; 58:540-550. [PMID: 28833526 PMCID: PMC5784428 DOI: 10.1002/em.22119] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Revised: 05/25/2017] [Accepted: 05/25/2017] [Indexed: 05/17/2023]
Abstract
Epidemiological and animal data suggest that adult chronic disease is influenced by early-life exposure-induced changes to the epigenome. Previously, we observed that perinatal lead (Pb) exposure results in persistent murine metabolic- and activity-related effects. Using phylogenetic and DNA methylation analysis, we have also identified novel intracisternal A particle (IAP) retrotransposons exhibiting regions of variable methylation as candidate loci for environmental effects on the epigenome. Here, we now evaluate brain and kidney DNA methylation profiles of four representative IAPs in adult mice exposed to human physiologically relevant levels of Pb two weeks prior to mating through lactation. When IAPs across the genome were evaluated globally, average (sd) methylation levels were 92.84% (3.74) differing by tissue (P < 0.001), but not sex or dose. By contrast, the four individual IAPs displayed tissue-specific Pb and sex effects. Medium Pb-exposed mice had 3.86% less brain methylation at IAP 110 (P < 0.01), while high Pb-exposed mice had 2.83% less brain methylation at IAP 236 (P = 0.01) and 1.77% less at IAP 506 (P = 0.05). Individual IAP DNA methylation differed by sex for IAP 110 in the brain and kidney, IAP 236 in the kidney, and IAP 1259 in the kidney. Using Tomtom, we identified three binding motifs that matched to each of our novel IAPs impacted by Pb, one of which (HMGA2) has been linked to metabolic-related conditions in both mice and humans. Thus, these recently identified IAPs display tissue-specific environmental lability as well as sex-specific differences supporting an epigenetic link between early exposure to Pb and later-in-life health outcomes. Environ. Mol. Mutagen. 58:540-550, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- L. Montrose
- Environmental Health Sciences, University of Michigan
| | - C. Faulk
- Animal Science, University of Minnesota
| | - J. Francis
- Environmental Health Sciences, University of Michigan
| | - D.C. Dolinoy
- Environmental Health Sciences, University of Michigan
- Nutritional Sciences, University of Michigan
- Corresponding author: Dana C. Dolinoy, 1415 Washington Heights, Ann Arbor, Michigan 48109-2029, Tel: 734 647-3155,
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20
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Tran NTL, Huang CH. MOTIFSIM 2.1: An Enhanced Software Platform for Detecting Similarity in Multiple DNA Motif Data Sets. J Comput Biol 2017. [PMID: 28632401 PMCID: PMC5610392 DOI: 10.1089/cmb.2017.0005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Finding binding site motifs plays an important role in bioinformatics as it reveals the transcription factors that control the gene expression. The development for motif finders has flourished in the past years with many tools have been introduced to the research community. Although these tools possess exceptional features for detecting motifs, they report different results for an identical data set. Hence, using multiple tools is recommended because motifs reported by several tools are likely biologically significant. However, the results from multiple tools need to be compared for obtaining common significant motifs. MOTIFSIM web tool and command-line tool were developed for this purpose. In this work, we present several technical improvements as well as additional features to further support the motif analysis in our new release MOTIFSIM 2.1.
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Affiliation(s)
- Ngoc Tam L Tran
- Department of Computer Science and Engineering, University of Connecticut , Storrs, Connecticut
| | - Chun-Hsi Huang
- Department of Computer Science and Engineering, University of Connecticut , Storrs, Connecticut
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21
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Spanier KI, Jansen M, Decaestecker E, Hulselmans G, Becker D, Colbourne JK, Orsini L, De Meester L, Aerts S. Conserved Transcription Factors Steer Growth-Related Genomic Programs in Daphnia. Genome Biol Evol 2017; 9:1821-1842. [PMID: 28854641 PMCID: PMC5569996 DOI: 10.1093/gbe/evx127] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2017] [Indexed: 02/06/2023] Open
Abstract
Ecological genomics aims to understand the functional association between environmental gradients and the genes underlying adaptive traits. Many genes that are identified by genome-wide screening in ecologically relevant species lack functional annotations. Although gene functions can be inferred from sequence homology, such approaches have limited power. Here, we introduce ecological regulatory genomics by presenting an ontology-free gene prioritization method. Specifically, our method combines transcriptome profiling with high-throughput cis-regulatory sequence analysis in the water fleas Daphnia pulex and Daphnia magna. It screens coexpressed genes for overrepresented DNA motifs that serve as transcription factor binding sites, thereby providing insight into conserved transcription factors and gene regulatory networks shaping the expression profile. We first validated our method, called Daphnia-cisTarget, on a D. pulex heat shock data set, which revealed a network driven by the heat shock factor. Next, we performed RNA-Seq in D. magna exposed to the cyanobacterium Microcystis aeruginosa. Daphnia-cisTarget identified coregulated gene networks that associate with the moulting cycle and potentially regulate life history changes in growth rate and age at maturity. These networks are predicted to be regulated by evolutionary conserved transcription factors such as the homologues of Drosophila Shavenbaby and Grainyhead, nuclear receptors, and a GATA family member. In conclusion, our approach allows prioritising candidate genes in Daphnia without bias towards prior knowledge about functional gene annotation and represents an important step towards exploring the molecular mechanisms of ecological responses in organisms with poorly annotated genomes.
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Affiliation(s)
- Katina I. Spanier
- Department of Biology, Laboratory of Aquatic Ecology, Evolution and Conservation, KU Leuven, Belgium
- Department of Human Genetics, Laboratory of Computational Biology, KU Leuven, Belgium
- VIB Center for Brain and Disease Research, KU Leuven, Belgium
| | - Mieke Jansen
- Department of Biology, Laboratory of Aquatic Ecology, Evolution and Conservation, KU Leuven, Belgium
| | - Ellen Decaestecker
- Department of Biology, Laboratory of Aquatic Biology, Science and Technology, KU Leuven Campus Kulak, Kortrjik, Belgium
| | - Gert Hulselmans
- Department of Human Genetics, Laboratory of Computational Biology, KU Leuven, Belgium
- VIB Center for Brain and Disease Research, KU Leuven, Belgium
| | - Dörthe Becker
- Environmental Genomics Group, School of Biosciences, College of Life and Environmental Sciences, University of Birmingham, United Kingdom
- Department of Animal and Plant Sciences, University of Sheffield, Western Bank, United Kingdom
| | - John K. Colbourne
- Environmental Genomics Group, School of Biosciences, College of Life and Environmental Sciences, University of Birmingham, United Kingdom
| | - Luisa Orsini
- Environmental Genomics Group, School of Biosciences, College of Life and Environmental Sciences, University of Birmingham, United Kingdom
| | - Luc De Meester
- Department of Biology, Laboratory of Aquatic Ecology, Evolution and Conservation, KU Leuven, Belgium
| | - Stein Aerts
- Department of Human Genetics, Laboratory of Computational Biology, KU Leuven, Belgium
- VIB Center for Brain and Disease Research, KU Leuven, Belgium
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22
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Tran NTL, Huang CH. Cloud-based MOTIFSIM: Detecting Similarity in Large DNA Motif Data Sets. J Comput Biol 2017; 24:450-459. [DOI: 10.1089/cmb.2016.0080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Ngoc Tam L. Tran
- Department of Computer Science and Engineering, University of Connecticut, Storrs, Connecticut
| | - Chun-Hsi Huang
- Department of Computer Science and Engineering, University of Connecticut, Storrs, Connecticut
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23
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Liu B, Yang J, Li Y, McDermaid A, Ma Q. An algorithmic perspective of de novo cis-regulatory motif finding based on ChIP-seq data. Brief Bioinform 2017; 19:1069-1081. [DOI: 10.1093/bib/bbx026] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Indexed: 01/06/2023] Open
Affiliation(s)
- Bingqiang Liu
- School of Mathematics, Shandong University, Jinan Shandong, P. R. China
| | - Jinyu Yang
- Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA
| | - Yang Li
- School of Mathematics, Shandong University, Jinan Shandong, P. R. China
| | - Adam McDermaid
- Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA
| | - Qin Ma
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA
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24
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Jayaram N, Usvyat D, R Martin AC. Evaluating tools for transcription factor binding site prediction. BMC Bioinformatics 2016; 17:547. [PMID: 27806697 PMCID: PMC6889335 DOI: 10.1186/s12859-016-1298-9] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2016] [Accepted: 10/20/2016] [Indexed: 12/21/2022] Open
Abstract
Background Binding of transcription factors to transcription factor binding sites (TFBSs) is key to the mediation of transcriptional regulation. Information on experimentally validated functional TFBSs is limited and consequently there is a need for accurate prediction of TFBSs for gene annotation and in applications such as evaluating the effects of single nucleotide variations in causing disease. TFBSs are generally recognized by scanning a position weight matrix (PWM) against DNA using one of a number of available computer programs. Thus we set out to evaluate the best tools that can be used locally (and are therefore suitable for large-scale analyses) for creating PWMs from high-throughput ChIP-Seq data and for scanning them against DNA. Results We evaluated a set of de novo motif discovery tools that could be downloaded and installed locally using ENCODE-ChIP-Seq data and showed that rGADEM was the best-performing tool. TFBS prediction tools used to scan PWMs against DNA fall into two classes — those that predict individual TFBSs and those that identify clusters. Our evaluation showed that FIMO and MCAST performed best respectively. Conclusions Selection of the best-performing tools for generating PWMs from ChIP-Seq data and for scanning PWMs against DNA has the potential to improve prediction of precise transcription factor binding sites within regions identified by ChIP-Seq experiments for gene finding, understanding regulation and in evaluating the effects of single nucleotide variations in causing disease. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1298-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Narayan Jayaram
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, Darwin Building, Gower Street, London, WC1E 6BT, UK
| | - Daniel Usvyat
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, Darwin Building, Gower Street, London, WC1E 6BT, UK
| | - Andrew C R Martin
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, Darwin Building, Gower Street, London, WC1E 6BT, UK.
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25
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Diamanti K, Umer HM, Kruczyk M, Dąbrowski MJ, Cavalli M, Wadelius C, Komorowski J. Maps of context-dependent putative regulatory regions and genomic signal interactions. Nucleic Acids Res 2016; 44:9110-9120. [PMID: 27625394 PMCID: PMC5100580 DOI: 10.1093/nar/gkw800] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Accepted: 08/31/2016] [Indexed: 12/24/2022] Open
Abstract
Gene transcription is regulated mainly by transcription factors (TFs). ENCODE and Roadmap Epigenomics provide global binding profiles of TFs, which can be used to identify regulatory regions. To this end we implemented a method to systematically construct cell-type and species-specific maps of regulatory regions and TF-TF interactions. We illustrated the approach by developing maps for five human cell-lines and two other species. We detected ∼144k putative regulatory regions among the human cell-lines, with the majority of them being ∼300 bp. We found ∼20k putative regulatory elements in the ENCODE heterochromatic domains suggesting a large regulatory potential in the regions presumed transcriptionally silent. Among the most significant TF interactions identified in the heterochromatic regions were CTCF and the cohesin complex, which is in agreement with previous reports. Finally, we investigated the enrichment of the obtained putative regulatory regions in the 3D chromatin domains. More than 90% of the regions were discovered in the 3D contacting domains. We found a significant enrichment of GWAS SNPs in the putative regulatory regions. These significant enrichments provide evidence that the regulatory regions play a crucial role in the genomic structural stability. Additionally, we generated maps of putative regulatory regions for prostate and colorectal cancer human cell-lines.
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Affiliation(s)
- Klev Diamanti
- Department of Cell and Molecular Biology, Uppsala University, Uppsala SE-751-24, Sweden
| | - Husen M Umer
- Department of Cell and Molecular Biology, Uppsala University, Uppsala SE-751-24, Sweden
| | - Marcin Kruczyk
- Department of Cell and Molecular Biology, Uppsala University, Uppsala SE-751-24, Sweden
| | - Michał J Dąbrowski
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala SE-751-08, Sweden
| | - Marco Cavalli
- Institute of Computer Science, Polish Academy of Sciences, Warsaw 012-48, Poland
| | - Claes Wadelius
- Institute of Computer Science, Polish Academy of Sciences, Warsaw 012-48, Poland
| | - Jan Komorowski
- Department of Cell and Molecular Biology, Uppsala University, Uppsala SE-751-24, Sweden .,Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala SE-751-08, Sweden
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26
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Pantazes RJ, Reifert J, Bozekowski J, Ibsen KN, Murray JA, Daugherty PS. Identification of disease-specific motifs in the antibody specificity repertoire via next-generation sequencing. Sci Rep 2016; 6:30312. [PMID: 27481573 PMCID: PMC4969583 DOI: 10.1038/srep30312] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2016] [Accepted: 07/04/2016] [Indexed: 12/12/2022] Open
Abstract
Disease-specific antibodies can serve as highly effective biomarkers but have been identified for only a relatively small number of autoimmune diseases. A method was developed to identify disease-specific binding motifs through integration of bacterial display peptide library screening, next-generation sequencing (NGS) and computational analysis. Antibody specificity repertoires were determined by identifying bound peptide library members for each specimen using cell sorting and performing NGS. A computational algorithm, termed Identifying Motifs Using Next- generation sequencing Experiments (IMUNE), was developed and applied to discover disease- and healthy control-specific motifs. IMUNE performs comprehensive pattern searches, identifies patterns statistically enriched in the disease or control groups and clusters the patterns to generate motifs. Using celiac disease sera as a discovery set, IMUNE identified a consensus motif (QPEQPF[PS]E) with high diagnostic sensitivity and specificity in a validation sera set, in addition to novel motifs. Peptide display and sequencing (Display-Seq) coupled with IMUNE analysis may thus be useful to characterize antibody repertoires and identify disease-specific antibody epitopes and biomarkers.
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Affiliation(s)
- Robert J Pantazes
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA.,Serimmune, Inc, Santa Barbara, CA 93105, USA
| | - Jack Reifert
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA.,Serimmune, Inc, Santa Barbara, CA 93105, USA
| | - Joel Bozekowski
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA
| | - Kelly N Ibsen
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA
| | - Joseph A Murray
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - Patrick S Daugherty
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA.,Serimmune, Inc, Santa Barbara, CA 93105, USA
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27
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Chaitankar V, Karakülah G, Ratnapriya R, Giuste FO, Brooks MJ, Swaroop A. Next generation sequencing technology and genomewide data analysis: Perspectives for retinal research. Prog Retin Eye Res 2016; 55:1-31. [PMID: 27297499 DOI: 10.1016/j.preteyeres.2016.06.001] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Revised: 06/06/2016] [Accepted: 06/08/2016] [Indexed: 02/08/2023]
Abstract
The advent of high throughput next generation sequencing (NGS) has accelerated the pace of discovery of disease-associated genetic variants and genomewide profiling of expressed sequences and epigenetic marks, thereby permitting systems-based analyses of ocular development and disease. Rapid evolution of NGS and associated methodologies presents significant challenges in acquisition, management, and analysis of large data sets and for extracting biologically or clinically relevant information. Here we illustrate the basic design of commonly used NGS-based methods, specifically whole exome sequencing, transcriptome, and epigenome profiling, and provide recommendations for data analyses. We briefly discuss systems biology approaches for integrating multiple data sets to elucidate gene regulatory or disease networks. While we provide examples from the retina, the NGS guidelines reviewed here are applicable to other tissues/cell types as well.
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Affiliation(s)
- Vijender Chaitankar
- Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, 6 Center Drive, Bethesda, MD, 20892-0610, USA
| | - Gökhan Karakülah
- Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, 6 Center Drive, Bethesda, MD, 20892-0610, USA
| | - Rinki Ratnapriya
- Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, 6 Center Drive, Bethesda, MD, 20892-0610, USA
| | - Felipe O Giuste
- Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, 6 Center Drive, Bethesda, MD, 20892-0610, USA
| | - Matthew J Brooks
- Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, 6 Center Drive, Bethesda, MD, 20892-0610, USA
| | - Anand Swaroop
- Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, 6 Center Drive, Bethesda, MD, 20892-0610, USA.
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28
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Al-Okaily A, Huang CH. ET-Motif: Solving the Exact (l, d)-Planted Motif Problem Using Error Tree Structure. J Comput Biol 2016; 23:615-23. [PMID: 27152692 DOI: 10.1089/cmb.2015.0238] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Motif finding is an important and a challenging problem in many biological applications such as discovering promoters, enhancers, locus control regions, transcription factors, and more. The (l, d)-planted motif search, PMS, is one of several variations of the problem. In this problem, there are n given sequences over alphabets of size [Formula: see text], each of length m, and two given integers l and d. The problem is to find a motif m of length l, where in each sequence there is at least an l-mer at a Hamming distance of [Formula: see text] of m. In this article, we propose ET-Motif, an algorithm that can solve the PMS problem in [Formula: see text] time and [Formula: see text] space. The time bound can be further reduced by a factor of m with [Formula: see text] space. In case the suffix tree that is built for the input sequences is balanced, the problem can be solved in [Formula: see text] time and [Formula: see text] space. Similarly, the time bound can be reduced by a factor of m using [Formula: see text] space. Moreover, the variations of the problem, namely the edit distance PMS and edited PMS (Quorum), can be solved using ET-Motif with simple modifications but upper bands of space and time. For edit distance PMS, the time and space bounds will be increased by [Formula: see text], while for edited PMS the increase will be of [Formula: see text] in the time bound.
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Affiliation(s)
- Anas Al-Okaily
- Computer Science & Engineering Department, University of Connecticut , Storrs, Connecticut
| | - Chun-Hsi Huang
- Computer Science & Engineering Department, University of Connecticut , Storrs, Connecticut
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29
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Boeva V. Analysis of Genomic Sequence Motifs for Deciphering Transcription Factor Binding and Transcriptional Regulation in Eukaryotic Cells. Front Genet 2016; 7:24. [PMID: 26941778 PMCID: PMC4763482 DOI: 10.3389/fgene.2016.00024] [Citation(s) in RCA: 87] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Accepted: 02/05/2016] [Indexed: 12/27/2022] Open
Abstract
Eukaryotic genomes contain a variety of structured patterns: repetitive elements, binding sites of DNA and RNA associated proteins, splice sites, and so on. Often, these structured patterns can be formalized as motifs and described using a proper mathematical model such as position weight matrix and IUPAC consensus. Two key tasks are typically carried out for motifs in the context of the analysis of genomic sequences. These are: identification in a set of DNA regions of over-represented motifs from a particular motif database, and de novo discovery of over-represented motifs. Here we describe existing methodology to perform these two tasks for motifs characterizing transcription factor binding. When applied to the output of ChIP-seq and ChIP-exo experiments, or to promoter regions of co-modulated genes, motif analysis techniques allow for the prediction of transcription factor binding events and enable identification of transcriptional regulators and co-regulators. The usefulness of motif analysis is further exemplified in this review by how motif discovery improves peak calling in ChIP-seq and ChIP-exo experiments and, when coupled with information on gene expression, allows insights into physical mechanisms of transcriptional modulation.
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Affiliation(s)
- Valentina Boeva
- Centre de Recherche, Institut CurieParis, France; INSERM, U900Paris, France; Mines ParisTechFontainebleau, France; PSL Research UniversityParis, France; Department of Development, Reproduction and Cancer, Institut CochinParis, France; INSERM, U1016Paris, France; Centre National de la Recherche Scientifique UMR 8104Paris, France; Université Paris Descartes UMR-S1016Paris, France
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30
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Eggeling R, Roos T, Myllymäki P, Grosse I. Inferring intra-motif dependencies of DNA binding sites from ChIP-seq data. BMC Bioinformatics 2015; 16:375. [PMID: 26552868 PMCID: PMC4640111 DOI: 10.1186/s12859-015-0797-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Accepted: 10/23/2015] [Indexed: 11/29/2022] Open
Abstract
Background Statistical modeling of transcription factor binding sites is one of the classical fields in bioinformatics. The position weight matrix (PWM) model, which assumes statistical independence among all nucleotides in a binding site, used to be the standard model for this task for more than three decades but its simple assumptions are increasingly put into question. Recent high-throughput sequencing methods have provided data sets of sufficient size and quality for studying the benefits of more complex models. However, learning more complex models typically entails the danger of overfitting, and while model classes that dynamically adapt the model complexity to data have been developed, effective model selection is to date only possible for fully observable data, but not, e.g., within de novo motif discovery. Results To address this issue, we propose a stochastic algorithm for performing robust model selection in a latent variable setting. This algorithm yields a solution without relying on hyperparameter-tuning via massive cross-validation or other computationally expensive resampling techniques. Using this algorithm for learning inhomogeneous parsimonious Markov models, we study the degree of putative higher-order intra-motif dependencies for transcription factor binding sites inferred via de novo motif discovery from ChIP-seq data. We find that intra-motif dependencies are prevalent and not limited to first-order dependencies among directly adjacent nucleotides, but that second-order models appear to be the significantly better choice. Conclusions The traditional PWM model appears to be indeed insufficient to infer realistic sequence motifs, as it is on average outperformed by more complex models that take into account intra-motif dependencies. Moreover, using such models together with an appropriate model selection procedure does not lead to a significant performance loss in comparison with the PWM model for any of the studied transcription factors. Hence, we find it worthwhile to recommend that any modern motif discovery algorithm should attempt to take into account intra-motif dependencies. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0797-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ralf Eggeling
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany. .,Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland.
| | - Teemu Roos
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland.
| | - Petri Myllymäki
- Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland.
| | - Ivo Grosse
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany. .,German Center for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.
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Abstract
To fully understand the regulation of gene expression, it is critical to quantitatively define whether and how RNA-binding proteins (RBPs) discriminate between alternative binding sites in RNAs. Here, we describe new methods that measure protein binding to large numbers of RNA variants, and ways to analyse and interpret data obtained by these approaches, including affinity distributions and free energy landscapes. We discuss how the new methodologies and the associated concepts enable the development of inclusive, quantitative models for RNA-protein interactions that transcend the traditional binary classification of RBPs as either specific or nonspecific.
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MOTIFSIM: A web tool for detecting similarity in multiple DNA motif datasets. Biotechniques 2015; 59:26-33. [PMID: 26156781 DOI: 10.2144/000114308] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Accepted: 05/04/2015] [Indexed: 11/23/2022] Open
Abstract
Currently, there are a number of motif detection tools available that possess unique functionality. These tools often report different motifs, and therefore use of multiple tools is generally advised since common motifs reported by multiple tools are more likely to be biologically significant. However, results produced by these different tools need to be compared and existing similarity detection tools only allow comparison between two data sets. Here, we describe a motif similarity detection tool (MOTIFSIM) possessing a web-based, user-friendly interface that is capable of detecting similarity from multiple DNA motif data sets concurrently. Results can either be viewed online or downloaded. Users may also download and run MOTIFSIM as a command-line tool in stand-alone mode. The web tool, along with its command-line version, user manuals, and source codes, are freely available at http://biogrid-head.engr.uconn.edu/motifsim/.
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Lihu A, Holban T. A review of ensemble methods for de novo motif discovery in ChIP-Seq data. Brief Bioinform 2015; 16:964-73. [DOI: 10.1093/bib/bbv022] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Indexed: 01/17/2023] Open
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Bhowmick P, Guharoy M, Tompa P. Bioinformatics Approaches for Predicting Disordered Protein Motifs. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2015; 870:291-318. [PMID: 26387106 DOI: 10.1007/978-3-319-20164-1_9] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Short, linear motifs (SLiMs) in proteins are functional microdomains consisting of contiguous residue segments along the protein sequence, typically not more than 10 consecutive amino acids in length with less than 5 defined positions. Many positions are 'degenerate' thus offering flexibility in terms of the amino acid types allowed at those positions. Their short length and degenerate nature confers evolutionary plasticity meaning that SLiMs often evolve convergently. Further, SLiMs have a propensity to occur within intrinsically unstructured protein segments and this confers versatile functionality to unstructured regions of the proteome. SLiMs mediate multiple types of protein interactions based on domain-peptide recognition and guide functions including posttranslational modifications, subcellular localization of proteins, and ligand binding. SLiMs thus behave as modular interaction units that confer versatility to protein function and SLiM-mediated interactions are increasingly being recognized as therapeutic targets. In this chapter we start with a brief description about the properties of SLiMs and their interactions and then move on to discuss algorithms and tools including several web-based methods that enable the discovery of novel SLiMs (de novo motif discovery) as well as the prediction of novel occurrences of known SLiMs. Both individual amino acid sequences as well as sets of protein sequences can be scanned using these methods to obtain statistically overrepresented sequence patterns. Lists of putatively functional SLiMs are then assembled based on parameters such as evolutionary sequence conservation, disorder scores, structural data, gene ontology terms and other contextual information that helps to assess the functional credibility or significance of these motifs. These bioinformatics methods should certainly guide experiments aimed at motif discovery.
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Affiliation(s)
- Pallab Bhowmick
- VIB Department of Structural Biology, Vrije Universiteit Brussel (VUB), Building E, Pleinlaan 2, 1050, Brussels, Belgium
| | - Mainak Guharoy
- VIB Department of Structural Biology, Vrije Universiteit Brussel (VUB), Building E, Pleinlaan 2, 1050, Brussels, Belgium.
| | - Peter Tompa
- VIB Department of Structural Biology, Vrije Universiteit Brussel (VUB), Building E, Pleinlaan 2, 1050, Brussels, Belgium. .,Institute of Enzymology, Research Center of Natural Sciences, Hungarian Academy of Sciences, Budapest, Hungary.
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Niu M, Tabari ES, Su Z. De novo prediction of cis-regulatory elements and modules through integrative analysis of a large number of ChIP datasets. BMC Genomics 2014; 15:1047. [PMID: 25442502 PMCID: PMC4265420 DOI: 10.1186/1471-2164-15-1047] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Accepted: 11/19/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In eukaryotes, transcriptional regulation is usually mediated by interactions of multiple transcription factors (TFs) with their respective specific cis-regulatory elements (CREs) in the so-called cis-regulatory modules (CRMs) in DNA. Although the knowledge of CREs and CRMs in a genome is crucial to elucidate gene regulatory networks and understand many important biological phenomena, little is known about the CREs and CRMs in most eukaryotic genomes due to the difficulty to characterize them by either computational or traditional experimental methods. However, the exponentially increasing number of TF binding location data produced by the recent wide adaptation of chromatin immunoprecipitation coupled with microarray hybridization (ChIP-chip) or high-throughput sequencing (ChIP-seq) technologies has provided an unprecedented opportunity to identify CRMs and CREs in genomes. Nonetheless, how to effectively mine these large volumes of ChIP data to identify CREs and CRMs at nucleotide resolution is a highly challenging task. RESULTS We have developed a novel graph-theoretic based algorithm DePCRM for genome-wide de novo predictions of CREs and CRMs using a large number of ChIP datasets. DePCRM predicts CREs and CRMs by identifying overrepresented combinatorial CRE motif patterns in multiple ChIP datasets in an effective way. When applied to 168 ChIP datasets of 56 TFs from D. melanogaster, DePCRM identified 184 and 746 overrepresented CRE motifs and their combinatorial patterns, respectively, and predicted a total of 115,932 CRMs in the genome. The predictions recover 77.9% of known CRMs in the datasets and 89.3% of known CRMs containing at least one predicted CRE. We found that the putative CRMs as well as CREs as a whole in a CRM are more conserved than randomly selected sequences. CONCLUSION Our results suggest that the CRMs predicted by DePCRM are highly likely to be functional. Our algorithm is the first of its kind for de novo genome-wide prediction of CREs and CRMs using larger number of transcription factor ChIP datasets. The algorithm and predictions will hopefully facilitate the elucidation of gene regulatory networks in eukaryotes. All the predicted CREs, CRMs, and their target genes are available at http://bioinfo.uncc.edu/mniu/pcrms/www/.
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Affiliation(s)
| | | | - Zhengchang Su
- Department of Bioinformatics and Genomics, College of Computing and Informatics, The University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA.
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