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Liu T, Zhu H, Wang Z. Learning Micro-C from Hi-C with diffusion models. PLoS Comput Biol 2024; 20:e1012136. [PMID: 38758956 PMCID: PMC11139321 DOI: 10.1371/journal.pcbi.1012136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 05/30/2024] [Accepted: 05/05/2024] [Indexed: 05/19/2024] Open
Abstract
In the last few years, Micro-C has shown itself as an improved alternative to Hi-C. It replaced the restriction enzymes in Hi-C assays with micrococcal nuclease (MNase), resulting in capturing nucleosome resolution chromatin interactions. The signal-to-noise improvement of Micro-C allows it to detect more chromatin loops than high-resolution Hi-C. However, compared with massive Hi-C datasets available in the literature, there are only a limited number of Micro-C datasets. To take full advantage of these Hi-C datasets, we present HiC2MicroC, a computational method learning and then predicting Micro-C from Hi-C based on the denoising diffusion probabilistic models (DDPM). We trained our DDPM and other regression models in human foreskin fibroblast (HFFc6) cell line and evaluated these methods in six different cell types at 5-kb and 1-kb resolution. Our evaluations demonstrate that both HiC2MicroC and regression methods can markedly improve Hi-C towards Micro-C, and our DDPM-based HiC2MicroC outperforms regression in various terms. First, HiC2MicroC successfully recovers most of the Micro-C loops even those not detected in Hi-C maps. Second, a majority of the HiC2MicroC-recovered loops anchor CTCF binding sites in a convergent orientation. Third, HiC2MicroC loops share genomic and epigenetic properties with Micro-C loops, including linking promoters and enhancers, and their anchors are enriched for structural proteins (CTCF and cohesin) and histone modifications. Lastly, we find our recovered loops are also consistent with the loops identified from promoter capture Micro-C (PCMicro-C) and Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET). Overall, HiC2MicroC is an effective tool for further studying Hi-C data with Micro-C as a template. HiC2MicroC is publicly available at https://github.com/zwang-bioinformatics/HiC2MicroC/.
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Affiliation(s)
- Tong Liu
- Department of Computer Science, University of Miami, Coral Gables, Florida, United States of America
| | - Hao Zhu
- Department of Computer Science, University of Miami, Coral Gables, Florida, United States of America
| | - Zheng Wang
- Department of Computer Science, University of Miami, Coral Gables, Florida, United States of America
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2
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Wall BPG, Nguyen M, Harrell JC, Dozmorov MG. Machine and deep learning methods for predicting 3D genome organization. ARXIV 2024:arXiv:2403.03231v1. [PMID: 38495565 PMCID: PMC10942493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Three-Dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, Topologically Associating Domains (TADs), and A/B compartments play critical roles in a wide range of cellular processes by regulating gene expression. Recent development of chromatin conformation capture technologies has enabled genome-wide profiling of various 3D structures, even with single cells. However, current catalogs of 3D structures remain incomplete and unreliable due to differences in technology, tools, and low data resolution. Machine learning methods have emerged as an alternative to obtain missing 3D interactions and/or improve resolution. Such methods frequently use genome annotation data (ChIP-seq, DNAse-seq, etc.), DNA sequencing information (k-mers, Transcription Factor Binding Site (TFBS) motifs), and other genomic properties to learn the associations between genomic features and chromatin interactions. In this review, we discuss computational tools for predicting three types of 3D interactions (EPIs, chromatin interactions, TAD boundaries) and analyze their pros and cons. We also point out obstacles of computational prediction of 3D interactions and suggest future research directions.
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Affiliation(s)
- Brydon P. G. Wall
- Center for Biological Data Science, Virginia Commonwealth University, Richmond, VA, 23284, USA
| | - My Nguyen
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, 23298, USA
| | - J. Chuck Harrell
- Department of Pathology, Virginia Commonwealth University, Richmond, VA, 23284, USA
- Massey Comprehensive Cancer Center, Virginia Commonwealth University, Richmond, VA 23298, USA
- Center for Pharmaceutical Engineering, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Mikhail G. Dozmorov
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, 23298, USA
- Department of Pathology, Virginia Commonwealth University, Richmond, VA, 23284, USA
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3
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Matsuo K, Aihara H, Hara Y, Morishita A, Sakagami Y, Miyake S, Tatsumi S, Ishihara S, Tohma Y, Yamashita H, Sasayama T. Machine Learning to Predict Three Types of Outcomes After Traumatic Brain Injury Using Data at Admission: A Multi-Center Study for Development and Validation. J Neurotrauma 2023; 40:1694-1706. [PMID: 37029810 DOI: 10.1089/neu.2022.0515] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2023] Open
Abstract
The difficulty of accurately identifying patients who would benefit from promising treatments makes it challenging to prove the efficacy of novel treatments for traumatic brain injury (TBI). Although machine learning is being increasingly applied to this task, existing binary outcome prediction models are insufficient for the effective stratification of TBI patients. The aim of this study was to develop an accurate 3-class outcome prediction model to enable appropriate patient stratification. To this end, retrospective balanced data of 1200 blunt TBI patients admitted to six Japanese hospitals from January 2018 onwards (200 consecutive cases at each institution) were used for model training and validation. We incorporated 21 predictors obtained in the emergency department, including age, sex, six clinical findings, four laboratory parameters, eight computed tomography findings, and an emergency craniotomy. We developed two machine learning models (XGBoost and dense neural network) and logistic regression models to predict 3-class outcomes based on the Glasgow Outcome Scale-Extended (GOSE) at discharge. The prediction models were developed using a training dataset with n = 1000, and their prediction performances were evaluated over two validation rounds on a validation dataset (n = 80) and a test dataset (n = 120) using the bootstrap method. Of the 1200 patients in aggregate, the median patient age was 71 years, 199 (16.7%) exhibited severe TBI, and emergency craniotomy was performed on 104 patients (8.7%). The median length of stay was 13.0 days. The 3-class outcomes were good recovery/moderate disability for 709 patients (59.1%), severe disability/vegetative state in 416 patients (34.7%), and death in 75 patients (6.2%). XGBoost model performed well with 69.5% sensitivity, 82.5% accuracy, and an area under the receiver operating characteristic curve of 0.901 in the final validation. In terms of the receiver operating characteristic curve analysis, the XGBoost outperformed the neural network-based and logistic regression models slightly. In particular, XGBoost outperformed the logistic regression model significantly in predicting severe disability/vegetative state. Although each model predicted favorable outcomes accurately, they tended to miss the mortality prediction. The proposed machine learning model was demonstrated to be capable of accurate prediction of in-hospital outcomes following TBI, even with the three GOSE-based categories. As a result, it is expected to be more impactful in the development of appropriate patient stratification methods in future TBI studies than conventional binary prognostic models. Further, outcomes were predicted based on only clinical data obtained from the emergency department. However, developing a robust model with consistent performance in diverse scenarios remains challenging, and further efforts are needed to improve generalization performance.
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Affiliation(s)
- Kazuya Matsuo
- Department of Neurosurgery, Hyogo Emergency Medical Center and Kobe Red Cross Hospital, Kobe, Japan
- Department of Neurosurgery, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Hideo Aihara
- Department of Neurosurgery, Hyogo Prefectural Himeji Cardiovascular Center, Himeji, Japan
| | - Yoshie Hara
- Department of Neurosurgery, Hyogo Emergency Medical Center and Kobe Red Cross Hospital, Kobe, Japan
| | - Akitsugu Morishita
- Department of Neurosurgery, Hyogo Prefectural Kakogawa Medical Center, Kakogawa, Japan
| | - Yoshio Sakagami
- Department of Neurosurgery, Hyogo Prefectural Awaji Medical Center, Sumoto, Japan
| | - Shigeru Miyake
- Department of Neurosurgery, Kita-harima Medical Center, Ono, Japan
| | - Shotaro Tatsumi
- Department of Neurosurgery, Hirohata Steel Memorial Hospital, Himeji, Japan
| | - Satoshi Ishihara
- Department of Emergency and Critical Care Medicine, Hyogo Emergency Medical Center, Kobe, Japan
| | - Yoshiki Tohma
- Acute Care Medical Center, Hyogo Prefectural Kakogawa Medical Center, Kakogawa, Japan
| | - Haruo Yamashita
- Department of Neurosurgery, Hyogo Emergency Medical Center and Kobe Red Cross Hospital, Kobe, Japan
| | - Takashi Sasayama
- Department of Neurosurgery, Kobe University Graduate School of Medicine, Kobe, Japan
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Dsouza KB, Maslova A, Al-Jibury E, Merkenschlager M, Bhargava VK, Libbrecht MW. Learning representations of chromatin contacts using a recurrent neural network identifies genomic drivers of conformation. Nat Commun 2022; 13:3704. [PMID: 35764630 PMCID: PMC9240038 DOI: 10.1038/s41467-022-31337-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 06/15/2022] [Indexed: 11/28/2022] Open
Abstract
Despite the availability of chromatin conformation capture experiments, discerning the relationship between the 1D genome and 3D conformation remains a challenge, which limits our understanding of their affect on gene expression and disease. We propose Hi-C-LSTM, a method that produces low-dimensional latent representations that summarize intra-chromosomal Hi-C contacts via a recurrent long short-term memory neural network model. We find that these representations contain all the information needed to recreate the observed Hi-C matrix with high accuracy, outperforming existing methods. These representations enable the identification of a variety of conformation-defining genomic elements, including nuclear compartments and conformation-related transcription factors. They furthermore enable in-silico perturbation experiments that measure the influence of cis-regulatory elements on conformation.
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Affiliation(s)
- Kevin B Dsouza
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada.
| | - Alexandra Maslova
- School of Computing Science, Simon Fraser University, Burnaby, Canada
| | - Ediem Al-Jibury
- MRC, London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
- Department of Computing, Imperial College London, London, UK
| | - Matthias Merkenschlager
- MRC, London Institute of Medical Sciences, Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - Vijay K Bhargava
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada
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Dybowski R. Emergence of Deep Machine Learning in Medicine. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_26] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Talukder A, Barham C, Li X, Hu H. Interpretation of deep learning in genomics and epigenomics. Brief Bioinform 2021; 22:bbaa177. [PMID: 34020542 PMCID: PMC8138893 DOI: 10.1093/bib/bbaa177] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 06/26/2020] [Accepted: 07/10/2020] [Indexed: 12/17/2022] Open
Abstract
Machine learning methods have been widely applied to big data analysis in genomics and epigenomics research. Although accuracy and efficiency are common goals in many modeling tasks, model interpretability is especially important to these studies towards understanding the underlying molecular and cellular mechanisms. Deep neural networks (DNNs) have recently gained popularity in various types of genomic and epigenomic studies due to their capabilities in utilizing large-scale high-throughput bioinformatics data and achieving high accuracy in predictions and classifications. However, DNNs are often challenged by their potential to explain the predictions due to their black-box nature. In this review, we present current development in the model interpretation of DNNs, focusing on their applications in genomics and epigenomics. We first describe state-of-the-art DNN interpretation methods in representative machine learning fields. We then summarize the DNN interpretation methods in recent studies on genomics and epigenomics, focusing on current data- and computing-intensive topics such as sequence motif identification, genetic variations, gene expression, chromatin interactions and non-coding RNAs. We also present the biological discoveries that resulted from these interpretation methods. We finally discuss the advantages and limitations of current interpretation approaches in the context of genomic and epigenomic studies. Contact:xiaoman@mail.ucf.edu, haihu@cs.ucf.edu.
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Affiliation(s)
- Amlan Talukder
- Computer Science, University of Central Florida, Orlando, FL 32816, USA
| | - Clayton Barham
- Computer Science, University of Central Florida, Orlando, FL 32816, USA
| | - Xiaoman Li
- Burnett School of Biomedical Science, University of Central Florida, Orlando, FL 32816, USA
| | - Haiyan Hu
- Computer Science, University of Central Florida, Orlando, FL 32816, USA
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Tao H, Li H, Xu K, Hong H, Jiang S, Du G, Wang J, Sun Y, Huang X, Ding Y, Li F, Zheng X, Chen H, Bo X. Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles. Brief Bioinform 2021; 22:6102668. [PMID: 33454752 PMCID: PMC8424394 DOI: 10.1093/bib/bbaa405] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/26/2020] [Accepted: 12/10/2020] [Indexed: 12/14/2022] Open
Abstract
The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differentiation and disease development. Advances in chromosome conformation capture technologies, such as high-throughput chromosome conformation capture (Hi-C) and chromatin interaction analysis by paired-end tag (ChIA-PET), have enabled the exploration of chromatin interaction and organization. However, high-resolution Hi-C and ChIA-PET data are only available for a limited number of cell lines, and their acquisition is costly, time consuming, laborious and affected by theoretical limitations. Increasing evidence shows that DNA sequence and epigenomic features are informative predictors of regulatory interaction and chromatin architecture. Based on these features, numerous computational methods have been developed for the prediction of chromatin interaction and organization, whereas they are not extensively applied in biomedical study. A systematical study to summarize and evaluate such methods is still needed to facilitate their application. Here, we summarize 48 computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles, categorize them and compare their performance. Besides, we provide a comprehensive guideline for the selection of suitable methods to predict chromatin interaction and organization based on available data and biological question of interest.
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Affiliation(s)
- Huan Tao
- Beijing Institute of Radiation Medicine
| | - Hao Li
- Beijing Institute of Radiation Medicine
| | - Kang Xu
- Beijing Institute of Radiation Medicine
| | - Hao Hong
- Beijing Institute of Radiation Medicine, Department of Biotechnology
| | - Shuai Jiang
- Beijing Institute of Radiation Medicine, Department of Biotechnology
| | - Guifang Du
- Beijing Institute of Radiation Medicine, Department of Biotechnology
| | | | - Yu Sun
- Beijing Institute of Radiation Medicine, Department of Biotechnology
| | - Xin Huang
- Beijing Institute of Radiation Medicine, Department of Biotechnology
| | - Yang Ding
- Beijing Institute of Radiation Medicine
| | - Fei Li
- Chinese Academy of Sciences, Department of Computer Network Information Center
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8
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Emergence of Deep Machine Learning in Medicine. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_26-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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9
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Rozenwald MB, Galitsyna AA, Sapunov GV, Khrameeva EE, Gelfand MS. A machine learning framework for the prediction of chromatin folding in Drosophila using epigenetic features. PeerJ Comput Sci 2020; 6:e307. [PMID: 33816958 PMCID: PMC7924456 DOI: 10.7717/peerj-cs.307] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 09/30/2020] [Indexed: 05/03/2023]
Abstract
Technological advances have lead to the creation of large epigenetic datasets, including information about DNA binding proteins and DNA spatial structure. Hi-C experiments have revealed that chromosomes are subdivided into sets of self-interacting domains called Topologically Associating Domains (TADs). TADs are involved in the regulation of gene expression activity, but the mechanisms of their formation are not yet fully understood. Here, we focus on machine learning methods to characterize DNA folding patterns in Drosophila based on chromatin marks across three cell lines. We present linear regression models with four types of regularization, gradient boosting, and recurrent neural networks (RNN) as tools to study chromatin folding characteristics associated with TADs given epigenetic chromatin immunoprecipitation data. The bidirectional long short-term memory RNN architecture produced the best prediction scores and identified biologically relevant features. Distribution of protein Chriz (Chromator) and histone modification H3K4me3 were selected as the most informative features for the prediction of TADs characteristics. This approach may be adapted to any similar biological dataset of chromatin features across various cell lines and species. The code for the implemented pipeline, Hi-ChiP-ML, is publicly available: https://github.com/MichalRozenwald/Hi-ChIP-ML.
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Affiliation(s)
- Michal B. Rozenwald
- Faculty of Computer Science, National Research University Higher School of Economics, Moscow, Russia
| | | | - Grigory V. Sapunov
- Faculty of Computer Science, National Research University Higher School of Economics, Moscow, Russia
- Intento, Inc., Berkeley, CA, USA
| | | | - Mikhail S. Gelfand
- Skolkovo Institute of Science and Technology, Moscow, Russia
- A.A. Kharkevich Institute for Information Transmission Problems, RAS, Moscow, Russia
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10
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Jaroszewicz A, Ernst J. An integrative approach for fine-mapping chromatin interactions. Bioinformatics 2020; 36:1704-1711. [PMID: 31742318 DOI: 10.1093/bioinformatics/btz843] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Revised: 09/30/2019] [Accepted: 11/16/2019] [Indexed: 01/17/2023] Open
Abstract
MOTIVATION Chromatin interactions play an important role in genome architecture and gene regulation. The Hi-C assay generates such interactions maps genome-wide, but at relatively low resolutions (e.g. 5-25 kb), which is substantially coarser than the resolution of transcription factor binding sites or open chromatin sites that are potential sources of such interactions. RESULTS To predict the sources of Hi-C-identified interactions at a high resolution (e.g. 100 bp), we developed a computational method that integrates data from DNase-seq and ChIP-seq of TFs and histone marks. Our method, χ-CNN, uses this data to first train a convolutional neural network (CNN) to discriminate between called Hi-C interactions and non-interactions. χ-CNN then predicts the high-resolution source of each Hi-C interaction using a feature attribution method. We show these predictions recover original Hi-C peaks after extending them to be coarser. We also show χ-CNN predictions enrich for evolutionarily conserved bases, eQTLs and CTCF motifs, supporting their biological significance. χ-CNN provides an approach for analyzing important aspects of genome architecture and gene regulation at a higher resolution than previously possible. AVAILABILITY AND IMPLEMENTATION χ-CNN software is available on GitHub (https://github.com/ernstlab/X-CNN). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Artur Jaroszewicz
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA.,Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Jason Ernst
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA.,Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA.,Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA.,Computer Science Department, University of California, Los Angeles, Los Angeles, CA 90095, USA.,Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA 90095, USA.,Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
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11
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The Road Not Taken with Pyrrole-Imidazole Polyamides: Off-Target Effects and Genomic Binding. Biomolecules 2020; 10:biom10040544. [PMID: 32260120 PMCID: PMC7226143 DOI: 10.3390/biom10040544] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Revised: 03/16/2020] [Accepted: 03/19/2020] [Indexed: 12/20/2022] Open
Abstract
The high sequence specificity of minor groove-binding N-methylpyrrole-N-methylimidazole polyamides have made significant advances in cancer and disease biology, yet there have been few comprehensive reports on their off-target effects, most likely as a consequence of the lack of available tools in evaluating genomic binding, an essential aspect that has gone seriously underexplored. Compared to other N-heterocycles, the off-target effects of these polyamides and their specificity for the DNA minor groove and primary base pair recognition require the development of new analytical methods, which are missing in the field today. This review aims to highlight the current progress in deciphering the off-target effects of these N-heterocyclic molecules and suggests new ways that next-generating sequencing can be used in addressing off-target effects.
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