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Zhou J, Wang X, Wei Z, Meng J, Huang D. 4acCPred: Weakly supervised prediction of N4-acetyldeoxycytosine DNA modification from sequences. MOLECULAR THERAPY - NUCLEIC ACIDS 2022; 30:337-345. [DOI: 10.1016/j.omtn.2022.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 10/12/2022] [Indexed: 11/06/2022]
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Ahsan F, Yan Z, Precup D, Blanchette M. PhyloPGM: boosting regulatory function prediction accuracy using evolutionary information. Bioinformatics 2022; 38:i299-i306. [PMID: 35758792 PMCID: PMC9235490 DOI: 10.1093/bioinformatics/btac259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Motivation The computational prediction of regulatory function associated with a genomic sequence is of utter importance in -omics study, which facilitates our understanding of the underlying mechanisms underpinning the vast gene regulatory network. Prominent examples in this area include the binding prediction of transcription factors in DNA regulatory regions, and predicting RNA–protein interaction in the context of post-transcriptional gene expression. However, existing computational methods have suffered from high false-positive rates and have seldom used any evolutionary information, despite the vast amount of available orthologous data across multitudes of extant and ancestral genomes, which readily present an opportunity to improve the accuracy of existing computational methods. Results In this study, we present a novel probabilistic approach called PhyloPGM that leverages previously trained TFBS or RNA–RBP binding predictors by aggregating their predictions from various orthologous regions, in order to boost the overall prediction accuracy on human sequences. Throughout our experiments, PhyloPGM has shown significant improvement over baselines such as the sequence-based RNA–RBP binding predictor RNATracker and the sequence-based TFBS predictor that is known as FactorNet. PhyloPGM is simple in principle, easy to implement and yet, yields impressive results. Availability and implementation The PhyloPGM package is available at https://github.com/BlanchetteLab/PhyloPGM Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Faizy Ahsan
- School of Computer Science, McGill University, Montreal H3A 0G4, Canada
| | - Zichao Yan
- School of Computer Science, McGill University, Montreal H3A 0G4, Canada
| | - Doina Precup
- School of Computer Science, McGill University, Montreal H3A 0G4, Canada
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Huang D, Song B, Wei J, Su J, Coenen F, Meng J. Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data. Bioinformatics 2021; 37:i222-i230. [PMID: 34252943 PMCID: PMC8336446 DOI: 10.1093/bioinformatics/btab278] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Motivation Increasing evidence suggests that post-transcriptional ribonucleic acid (RNA) modifications regulate essential biomolecular functions and are related to the pathogenesis of various diseases. Precise identification of RNA modification sites is essential for understanding the regulatory mechanisms of RNAs. To date, many computational approaches for predicting RNA modifications have been developed, most of which were based on strong supervision enabled by base-resolution epitranscriptome data. However, high-resolution data may not be available. Results We propose WeakRM, the first weakly supervised learning framework for predicting RNA modifications from low-resolution epitranscriptome datasets, such as those generated from acRIP-seq and hMeRIP-seq. Evaluations on three independent datasets (corresponding to three different RNA modification types and their respective sequencing technologies) demonstrated the effectiveness of our approach in predicting RNA modifications from low-resolution data. WeakRM outperformed state-of-the-art multi-instance learning methods for genomic sequences, such as WSCNN, which was originally designed for transcription factor binding site prediction. Additionally, our approach captured motifs that are consistent with existing knowledge, and visualization of the predicted modification-containing regions unveiled the potentials of detecting RNA modifications with improved resolution. Availability implementation The source code for the WeakRM algorithm, along with the datasets used, are freely accessible at: https://github.com/daiyun02211/WeakRM Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Daiyun Huang
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.,Department of Computer Science, University of Liverpool, Liverpool L69 7ZB, UK
| | - Bowen Song
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.,Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK
| | - Jingjue Wei
- Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
| | - Jionglong Su
- School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.,AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
| | - Frans Coenen
- Department of Computer Science, University of Liverpool, Liverpool L69 7ZB, UK
| | - Jia Meng
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China.,Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK.,AI University Research Centre, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
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Wang W, Langlois R, Langlois M, Genchev GZ, Wang X, Lu H. Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins. Front Genet 2019; 10:729. [PMID: 31543893 PMCID: PMC6729729 DOI: 10.3389/fgene.2019.00729] [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: 12/21/2018] [Accepted: 07/11/2019] [Indexed: 12/27/2022] Open
Abstract
Function annotation efforts provide a foundation to our understanding of cellular processes and the functioning of the living cell. This motivates high-throughput computational methods to characterize new protein members of a particular function. Research work has focused on discriminative machine-learning methods, which promise to make efficient, de novo predictions of protein function. Furthermore, available function annotation exists predominantly for individual proteins rather than residues of which only a subset is necessary for the conveyance of a particular function. This limits discriminative approaches to predicting functions for which there is sufficient residue-level annotation, e.g., identification of DNA-binding proteins or where an excellent global representation can be divined. Complete understanding of the various functions of proteins requires discovery and functional annotation at the residue level. Herein, we cast this problem into the setting of multiple-instance learning, which only requires knowledge of the protein’s function yet identifies functionally relevant residues and need not rely on homology. We developed a new multiple-instance leaning algorithm derived from AdaBoost and benchmarked this algorithm against two well-studied protein function prediction tasks: annotating proteins that bind DNA and RNA. This algorithm outperforms certain previous approaches in annotating protein function while identifying functionally relevant residues involved in binding both DNA and RNA, and on one protein-DNA benchmark, it achieves near perfect classification.
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Affiliation(s)
- Wenchuan Wang
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, Chinas
| | - Robert Langlois
- Department of Bioengineering and Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States
| | - Marina Langlois
- Department of Bioengineering and Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States
| | - Georgi Z Genchev
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, Chinas.,Department of Bioengineering and Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States.,Bulgarian Institute for Genomics and Precision Medicine, Sofia, Bulgaria
| | - Xiaolei Wang
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, Chinas.,Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Hui Lu
- SJTU-Yale Joint Center for Biostatistics and Data Science, Department of Bioinformatics and Biostatistics, College of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, Chinas.,Department of Bioengineering and Department of Computer Science, University of Illinois at Chicago, Chicago, IL, United States.,Center for Biomedical Informatics, Shanghai Children's Hospital, Shanghai, China
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Gao Z, Ruan J. Computational modeling of in vivo and in vitro protein-DNA interactions by multiple instance learning. Bioinformatics 2018; 33:2097-2105. [PMID: 28334224 DOI: 10.1093/bioinformatics/btx115] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2016] [Accepted: 02/27/2017] [Indexed: 12/25/2022] Open
Abstract
Motivation The study of transcriptional regulation is still difficult yet fundamental in molecular biology research. While the development of both in vivo and in vitro profiling techniques have significantly enhanced our knowledge of transcription factor (TF)-DNA interactions, computational models of TF-DNA interactions are relatively simple and may not reveal sufficient biological insight. In particular, supervised learning based models for TF-DNA interactions attempt to map sequence-level features ( k -mers) to binding event but usually ignore the location of k -mers, which can cause data fragmentation and consequently inferior model performance. Results Here, we propose a novel algorithm based on the so-called multiple-instance learning (MIL) paradigm. MIL breaks each DNA sequence into multiple overlapping subsequences and models each subsequence separately, therefore implicitly takes into consideration binding site locations, resulting in both higher accuracy and better interpretability of the models. The result from both in vivo and in vitro TF-DNA interaction data show that our approach significantly outperform conventional single-instance learning based algorithms. Importantly, the models learned from in vitro data using our approach can predict in vivo binding with very good accuracy. In addition, the location information obtained by our method provides additional insight for motif finding results from ChIP-Seq data. Finally, our approach can be easily combined with other state-of-the-art TF-DNA interaction modeling methods. Availability and Implementation http://www.cs.utsa.edu/∼jruan/MIL/. Contact jianhua.ruan@utsa.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhen Gao
- Department of Computer Science, University of Texas at San Antonio, San Antonio, TX, USA
| | - Jianhua Ruan
- Department of Computer Science, University of Texas at San Antonio, San Antonio, TX, USA
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Deplancke B, Alpern D, Gardeux V. The Genetics of Transcription Factor DNA Binding Variation. Cell 2016; 166:538-554. [PMID: 27471964 DOI: 10.1016/j.cell.2016.07.012] [Citation(s) in RCA: 244] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Indexed: 12/23/2022]
Abstract
Most complex trait-associated variants are located in non-coding regulatory regions of the genome, where they have been shown to disrupt transcription factor (TF)-DNA binding motifs. Variable TF-DNA interactions are therefore increasingly considered as key drivers of phenotypic variation. However, recent genome-wide studies revealed that the majority of variable TF-DNA binding events are not driven by sequence alterations in the motif of the studied TF. This observation implies that the molecular mechanisms underlying TF-DNA binding variation and, by extrapolation, inter-individual phenotypic variation are more complex than originally anticipated. Here, we summarize the findings that led to this important paradigm shift and review proposed mechanisms for local, proximal, or distal genetic variation-driven variable TF-DNA binding. In addition, we discuss the biomedical implications of these findings for our ability to dissect the molecular role(s) of non-coding genetic variants in complex traits, including disease susceptibility.
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Affiliation(s)
- Bart Deplancke
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
| | - Daniel Alpern
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Vincent Gardeux
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne and Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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Barr CL, Misener VL. Decoding the non-coding genome: elucidating genetic risk outside the coding genome. GENES, BRAIN, AND BEHAVIOR 2016; 15:187-204. [PMID: 26515765 PMCID: PMC4833497 DOI: 10.1111/gbb.12269] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2015] [Revised: 10/19/2015] [Accepted: 10/28/2015] [Indexed: 12/11/2022]
Abstract
Current evidence emerging from genome-wide association studies indicates that the genetic underpinnings of complex traits are likely attributable to genetic variation that changes gene expression, rather than (or in combination with) variation that changes protein-coding sequences. This is particularly compelling with respect to psychiatric disorders, as genetic changes in regulatory regions may result in differential transcriptional responses to developmental cues and environmental/psychosocial stressors. Until recently, however, the link between transcriptional regulation and psychiatric genetic risk has been understudied. Multiple obstacles have contributed to the paucity of research in this area, including challenges in identifying the positions of remote (distal from the promoter) regulatory elements (e.g. enhancers) and their target genes and the underrepresentation of neural cell types and brain tissues in epigenome projects - the availability of high-quality brain tissues for epigenetic and transcriptome profiling, particularly for the adolescent and developing brain, has been limited. Further challenges have arisen in the prediction and testing of the functional impact of DNA variation with respect to multiple aspects of transcriptional control, including regulatory-element interaction (e.g. between enhancers and promoters), transcription factor binding and DNA methylation. Further, the brain has uncommon DNA-methylation marks with unique genomic distributions not found in other tissues - current evidence suggests the involvement of non-CG methylation and 5-hydroxymethylation in neurodevelopmental processes but much remains unknown. We review here knowledge gaps as well as both technological and resource obstacles that will need to be overcome in order to elucidate the involvement of brain-relevant gene-regulatory variants in genetic risk for psychiatric disorders.
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Affiliation(s)
- C. L. Barr
- Toronto Western Research Institute, University Health Network, Toronto, ON, Canada
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
| | - V. L. Misener
- Toronto Western Research Institute, University Health Network, Toronto, ON, Canada
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