1
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Scalvini B, Mashaghi A. Circuit Topology Analysis of Single-Cell HiC Data. Methods Mol Biol 2024; 2819:27-38. [PMID: 39028500 DOI: 10.1007/978-1-0716-3930-6_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
The 3D fold structure of the genome is intricately linked to its function. As a result, descriptors of 3D genome conformation are becoming increasingly important as markers for disease and therapeutic responses. Circuit topology, a theory of folds, formalizes the arrangement of contacts in an entangled chain. It is uniquely suited for the topological description of the cellular genome and changes to genomic architecture during physiological processes like cellular differentiation or pathological and therapeutic alterations. In this discussion, we will explore circuit topology and its ability to extract topological information from single-cell HiC data.
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Affiliation(s)
- Barbara Scalvini
- Medical Systems Biophysics and Bioengineering, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Leiden, The Netherlands
- Centre for Interdisciplinary Genome Research, Faculty of Science, Leiden University, Leiden, The Netherlands
| | - Alireza Mashaghi
- Medical Systems Biophysics and Bioengineering, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Leiden, The Netherlands.
- Centre for Interdisciplinary Genome Research, Faculty of Science, Leiden University, Leiden, The Netherlands.
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2
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Wang F, Alinejad‐Rokny H, Lin J, Gao T, Chen X, Zheng Z, Meng L, Li X, Wong K. A Lightweight Framework For Chromatin Loop Detection at the Single-Cell Level. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2303502. [PMID: 37816141 PMCID: PMC10667817 DOI: 10.1002/advs.202303502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/10/2023] [Indexed: 10/12/2023]
Abstract
Single-cell Hi-C (scHi-C) has made it possible to analyze chromatin organization at the single-cell level. However, scHi-C experiments generate inherently sparse data, which poses a challenge for loop calling methods. The existing approach performs significance tests across the imputed dense contact maps, leading to substantial computational overhead and loss of information at the single-cell level. To overcome this limitation, a lightweight framework called scGSLoop is proposed, which sets a new paradigm for scHi-C loop calling by adapting the training and inferencing strategies of graph-based deep learning to leverage the sequence features and 1D positional information of genomic loci. With this framework, sparsity is no longer a challenge, but rather an advantage that the model leverages to achieve unprecedented computational efficiency. Compared to existing methods, scGSLoop makes more accurate predictions and is able to identify more loops that have the potential to play regulatory roles in genome functioning. Moreover, scGSLoop preserves single-cell information by identifying a distinct group of loops for each individual cell, which not only enables an understanding of the variability of chromatin looping states between cells, but also allows scGSLoop to be extended for the investigation of multi-connected hubs and their underlying mechanisms.
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Affiliation(s)
- Fuzhou Wang
- Department of Computer ScienceCity University of Hong KongKowloon TongHong Kong SAR
| | - Hamid Alinejad‐Rokny
- BioMedical Machine Learning Lab, Graduate School of Biomedical EngineeringUniversity of New South WalesSydney2052Australia
| | - Jiecong Lin
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General HospitalDepartment of PathologyHarvard Medical SchoolBostonMA02129USA
- Department of Computer ScienceThe University of Hong KongPok Fu LamHong Kong SAR
| | - Tingxiao Gao
- Department of Medical Biophysics, Faculty of MedicineUniversity of TorontoTorontoOntarioM5G1L7Canada
| | - Xingjian Chen
- Department of Computer ScienceCity University of Hong KongKowloon TongHong Kong SAR
| | - Zetian Zheng
- Department of Computer ScienceCity University of Hong KongKowloon TongHong Kong SAR
| | - Lingkuan Meng
- Department of Computer ScienceCity University of Hong KongKowloon TongHong Kong SAR
| | - Xiangtao Li
- School of Artificial IntelligenceJilin UniversityChangchun130012China
| | - Ka‐Chun Wong
- Department of Computer ScienceCity University of Hong KongKowloon TongHong Kong SAR
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3
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Lee L, Yu M, Li X, Zhu C, Zhang Y, Yu H, Chen Z, Mishra S, Ren B, Li Y, Hu M. SnapHiC-D: a computational pipeline to identify differential chromatin contacts from single-cell Hi-C data. Brief Bioinform 2023; 24:bbad315. [PMID: 37649383 PMCID: PMC10516352 DOI: 10.1093/bib/bbad315] [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: 05/25/2023] [Revised: 08/04/2023] [Accepted: 08/07/2023] [Indexed: 09/01/2023] Open
Abstract
Single-cell high-throughput chromatin conformation capture technologies (scHi-C) has been used to map chromatin spatial organization in complex tissues. However, computational tools to detect differential chromatin contacts (DCCs) from scHi-C datasets in development and through disease pathogenesis are still lacking. Here, we present SnapHiC-D, a computational pipeline to identify DCCs between two scHi-C datasets. Compared to methods designed for bulk Hi-C data, SnapHiC-D detects DCCs with high sensitivity and accuracy. We used SnapHiC-D to identify cell-type-specific chromatin contacts at 10 Kb resolution in mouse hippocampal and human prefrontal cortical tissues, demonstrating that DCCs detected in the hippocampal and cortical cell types are generally associated with cell-type-specific gene expression patterns and epigenomic features. SnapHiC-D is freely available at https://github.com/HuMingLab/SnapHiC-D.
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Affiliation(s)
- Lindsay Lee
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Miao Yu
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Xiaoqi Li
- Carolina Health Informatics Program, University of North Carolina, Chapel Hill, NC, USA
| | - Chenxu Zhu
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
- New York Genome Center, New York, NY, USA
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Yanxiao Zhang
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
- Westlake University, Hangzhou, Zhejiang, China
| | - Hongyu Yu
- Department of Statistics, University of Wisconsin Madison, Madison, WI, USA
- Department of Biochemistry, University of Wisconsin Madison, Madison, WI, USA
| | - Ziyin Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Shreya Mishra
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Bing Ren
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
- Center for Epigenomics & Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
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4
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Lee L, Yu H, Jia BB, Jussila A, Zhu C, Chen J, Xie L, Hafner A, Mishra S, Wang DD, Strambio-De-Castillia C, Boettiger A, Ren B, Li Y, Hu M. SnapFISH: a computational pipeline to identify chromatin loops from multiplexed DNA FISH data. Nat Commun 2023; 14:4873. [PMID: 37573342 PMCID: PMC10423204 DOI: 10.1038/s41467-023-40658-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/01/2023] [Indexed: 08/14/2023] Open
Abstract
Multiplexed DNA fluorescence in situ hybridization (FISH) imaging technologies have been developed to map the folding of chromatin fibers at tens of nanometers and up to several kilobases in resolution in single cells. However, computational methods to reliably identify chromatin loops from such imaging datasets are still lacking. Here we present a Single-Nucleus Analysis Pipeline for multiplexed DNA FISH (SnapFISH), to process the multiplexed DNA FISH data and identify chromatin loops. SnapFISH can identify known chromatin loops from mouse embryonic stem cells with high sensitivity and accuracy. In addition, SnapFISH obtains comparable results of chromatin loops across datasets generated from diverse imaging technologies. SnapFISH is freely available at https://github.com/HuMingLab/SnapFISH .
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Affiliation(s)
- Lindsay Lee
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Hongyu Yu
- Department of Statistics, University of Wisconsin Madison, Madison, WI, USA
- Department of Biochemistry, University of Wisconsin Madison, Madison, WI, USA
| | - Bojing Blair Jia
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA, USA
- Medical Scientist Training Program, University of California San Diego, La Jolla, CA, USA
| | - Adam Jussila
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA, USA
| | - Chenxu Zhu
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
- New York Genome Center, New York, NY, USA
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Liangqi Xie
- Department of Infection Biology, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
- Department of Cancer Biology, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Antonina Hafner
- Department of Developmental Biology, Stanford University, Stanford, CA, USA
| | - Shreya Mishra
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | | | | | - Alistair Boettiger
- Department of Developmental Biology, Stanford University, Stanford, CA, USA
| | - Bing Ren
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
- Center for Epigenomics & Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA.
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA.
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.
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5
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Powell SK, Liao W, O’Shea C, Kammourh S, Ghorbani S, Rigat R, Elahi R, Deans PJM, Le DJ, Agarwal P, Seow WQ, Wang KC, Akbarian S, Brennand KJ. Schizophrenia Risk Mapping and Functional Engineering of the 3D Genome in Three Neuronal Subtypes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.17.549339. [PMID: 37502907 PMCID: PMC10370055 DOI: 10.1101/2023.07.17.549339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Common variants associated with schizophrenia are concentrated in non-coding regulatory sequences, but their precise target genes are context-dependent and impacted by cell-type-specific three-dimensional spatial chromatin organization. Here, we map long-range chromosomal conformations in isogenic human dopaminergic, GABAergic, and glutamatergic neurons to track developmentally programmed shifts in the regulatory activity of schizophrenia risk loci. Massive repressive compartmentalization, concomitant with the emergence of hundreds of neuron-specific multi-valent chromatin architectural stripes, occurs during neuronal differentiation, with genes interconnected to genetic risk loci through these long-range chromatin structures differing in their biological roles from genes more proximal to sequences conferring heritable risk. Chemically induced CRISPR-guided chromosomal loop-engineering for the proximal risk gene SNAP91 and distal risk gene BHLHE22 profoundly alters synaptic development and functional activity. Our findings highlight the large-scale cell-type-specific reorganization of chromosomal conformations at schizophrenia risk loci during neurodevelopment and establish a causal link between risk-associated gene-regulatory loop structures and neuronal function.
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Affiliation(s)
- Samuel K. Powell
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Graduate School of Biomedical Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Division of Molecular Psychiatry, Department of Psychiatry, Yale University, New Haven CT, 06511
| | - Will Liao
- New York Genome Center, New York, NY, 10029
| | - Callan O’Shea
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Division of Molecular Psychiatry, Department of Psychiatry, Yale University, New Haven CT, 06511
| | - Sarah Kammourh
- Division of Molecular Psychiatry, Department of Psychiatry, Yale University, New Haven CT, 06511
| | - Sadaf Ghorbani
- Division of Molecular Psychiatry, Department of Psychiatry, Yale University, New Haven CT, 06511
| | - Raymond Rigat
- Division of Molecular Psychiatry, Department of Psychiatry, Yale University, New Haven CT, 06511
| | - Rahat Elahi
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - PJ Michael Deans
- Division of Molecular Psychiatry, Department of Psychiatry, Yale University, New Haven CT, 06511
| | - Derek J. Le
- Department of Dermatology, Program in Epithelial Biology, Stanford University School of Medicine, Stanford, 94305, California, USA
- Cancer Biology Program, Stanford University School of Medicine, Stanford, California, 94305, USA
| | - Poonam Agarwal
- Department of Dermatology, Program in Epithelial Biology, Stanford University School of Medicine, Stanford, 94305, California, USA
| | - Wei Qiang Seow
- Department of Dermatology, Program in Epithelial Biology, Stanford University School of Medicine, Stanford, 94305, California, USA
| | - Kevin C. Wang
- Department of Dermatology, Program in Epithelial Biology, Stanford University School of Medicine, Stanford, 94305, California, USA
- Cancer Biology Program, Stanford University School of Medicine, Stanford, California, 94305, USA
- Veterans Affairs Palo Alto Healthcare System, Palo Alto, California, 94304, USA
| | - Schahram Akbarian
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Kristen J. Brennand
- Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomics, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Division of Molecular Psychiatry, Department of Psychiatry, Yale University, New Haven CT, 06511
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Zhong W, Liu W, Chen J, Sun Q, Hu M, Li Y. Understanding the function of regulatory DNA interactions in the interpretation of non-coding GWAS variants. Front Cell Dev Biol 2022; 10:957292. [PMID: 36060805 PMCID: PMC9437546 DOI: 10.3389/fcell.2022.957292] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 07/21/2022] [Indexed: 01/11/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified a vast number of variants associated with various complex human diseases and traits. However, most of these GWAS variants reside in non-coding regions producing no proteins, making the interpretation of these variants a daunting challenge. Prior evidence indicates that a subset of non-coding variants detected within or near cis-regulatory elements (e.g., promoters, enhancers, silencers, and insulators) might play a key role in disease etiology by regulating gene expression. Advanced sequencing- and imaging-based technologies, together with powerful computational methods, enabling comprehensive characterization of regulatory DNA interactions, have substantially improved our understanding of the three-dimensional (3D) genome architecture. Recent literature witnesses plenty of examples where using chromosome conformation capture (3C)-based technologies successfully links non-coding variants to their target genes and prioritizes relevant tissues or cell types. These examples illustrate the critical capability of 3D genome organization in annotating non-coding GWAS variants. This review discusses how 3D genome organization information contributes to elucidating the potential roles of non-coding GWAS variants in disease etiology.
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Affiliation(s)
- Wujuan Zhong
- Biostatistics and Research Decision Sciences, Merck & Co, Inc, Rahway, NJ, United States
| | - Weifang Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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7
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Li X, Lee L, Abnousi A, Yu M, Liu W, Huang L, Li Y, Hu M. SnapHiC2: A computationally efficient loop caller for single cell Hi-C data. Comput Struct Biotechnol J 2022; 20:2778-2783. [PMID: 35685374 PMCID: PMC9168059 DOI: 10.1016/j.csbj.2022.05.046] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/23/2022] [Accepted: 05/23/2022] [Indexed: 01/11/2023] Open
Abstract
Single cell Hi-C (scHi-C) technologies enable the study of chromatin spatial organization directly from complex tissues at single cell resolution. However, the identification of chromatin loops from single cells is challenging, largely due to the extremely sparse data. Our recently developed SnapHiC pipeline provides the first tool to map chromatin loops from scHi-C data, but it is computationally intensive. Here we introduce SnapHiC2, which adapts a sliding window approximation when imputing missing contacts in each single cell and reduces both memory usage and computational time by 70%. SnapHiC2 can identify 5 Kb resolution chromatin loops with high sensitivity and accuracy and help to suggest target genes for GWAS variants in a cell-type-specific manner. SnapHiC2 is freely available at: https://github.com/HuMingLab/SnapHiC/releases/tag/v0.2.2.
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Affiliation(s)
- Xiaoqi Li
- Carolina Health Informatics Program, University of North Carolina, Chapel Hill, NC, USA
| | - Lindsay Lee
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Armen Abnousi
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Miao Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Weifang Liu
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Le Huang
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
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