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Banerjee A, Zhang S, Bahar I. Genome structural dynamics: insights from Gaussian network analysis of Hi-C data. Brief Funct Genomics 2024:elae014. [PMID: 38654598 DOI: 10.1093/bfgp/elae014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/11/2024] [Accepted: 04/02/2024] [Indexed: 04/26/2024] Open
Abstract
Characterization of the spatiotemporal properties of the chromatin is essential to gaining insights into the physical bases of gene co-expression, transcriptional regulation and epigenetic modifications. The Gaussian network model (GNM) has proven in recent work to serve as a useful tool for modeling chromatin structural dynamics, using as input high-throughput chromosome conformation capture data. We focus here on the exploration of the collective dynamics of chromosomal structures at hierarchical levels of resolution, from single gene loci to topologically associating domains or entire chromosomes. The GNM permits us to identify long-range interactions between gene loci, shedding light on the role of cross-correlations between distal regions of the chromosomes in regulating gene expression. Notably, GNM analysis performed across diverse cell lines highlights the conservation of the global/cooperative movements of the chromatin across different types of cells. Variations driven by localized couplings between genomic loci, on the other hand, underlie cell differentiation, underscoring the significance of the four-dimensional properties of the genome in defining cellular identity. Finally, we demonstrate the close relation between the cell type-dependent mobility profiles of gene loci and their gene expression patterns, providing a clear demonstration of the role of chromosomal 4D features in defining cell-specific differential expression of genes.
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Affiliation(s)
- Anupam Banerjee
- Laufer Center for Physical & Quantitative Biology, Stony Brook University, NY 11794, USA
| | - She Zhang
- OpenEye, Cadence Molecular Sciences, Santa Fe, NM 87508, USA
| | - Ivet Bahar
- Laufer Center for Physical & Quantitative Biology, Stony Brook University, NY 11794, USA
- Department of Biochemistry and Cell Biology, Renaissance School of Medicine, Stony Brook University, NY 11794, USA
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2
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Yousefzadeh N, Tran C, Ramirez-Zamora A, Chen J, Fang R, Thai MT. Neuron-level explainable AI for Alzheimer's Disease assessment from fundus images. Sci Rep 2024; 14:7710. [PMID: 38565579 PMCID: PMC10987553 DOI: 10.1038/s41598-024-58121-8] [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: 05/06/2023] [Accepted: 03/26/2024] [Indexed: 04/04/2024] Open
Abstract
Alzheimer's Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia. Early diagnosis is critical for patients to benefit from potential intervention and treatment. The retina has emerged as a plausible diagnostic site for AD detection owing to its anatomical connection with the brain. However, existing AI models for this purpose have yet to provide a rational explanation behind their decisions and have not been able to infer the stage of the disease's progression. Along this direction, we propose a novel model-agnostic explainable-AI framework, called Granula ̲ r Neuron-le v ̲ el Expl a ̲ iner (LAVA), an interpretation prototype that probes into intermediate layers of the Convolutional Neural Network (CNN) models to directly assess the continuum of AD from the retinal imaging without the need for longitudinal or clinical evaluations. This innovative approach aims to validate retinal vasculature as a biomarker and diagnostic modality for evaluating Alzheimer's Disease. Leveraged UK Biobank cognitive tests and vascular morphological features demonstrate significant promise and effectiveness of LAVA in identifying AD stages across the progression continuum.
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Affiliation(s)
- Nooshin Yousefzadeh
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, USA
| | - Charlie Tran
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA
| | | | - Jinghua Chen
- Department of Ophthalmology, University of Florida, Gainesville, FL, USA
| | - Ruogu Fang
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA.
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA.
- Center for Cognitive Aging and Memory, University of Florida, Gainesville, FL, USA.
| | - My T Thai
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, USA.
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3
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Raffo A, Paulsen J. The shape of chromatin: insights from computational recognition of geometric patterns in Hi-C data. Brief Bioinform 2023; 24:bbad302. [PMID: 37646128 PMCID: PMC10516369 DOI: 10.1093/bib/bbad302] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/05/2023] [Accepted: 08/03/2023] [Indexed: 09/01/2023] Open
Abstract
The three-dimensional organization of chromatin plays a crucial role in gene regulation and cellular processes like deoxyribonucleic acid (DNA) transcription, replication and repair. Hi-C and related techniques provide detailed views of spatial proximities within the nucleus. However, data analysis is challenging partially due to a lack of well-defined, underpinning mathematical frameworks. Recently, recognizing and analyzing geometric patterns in Hi-C data has emerged as a powerful approach. This review provides a summary of algorithms for automatic recognition and analysis of geometric patterns in Hi-C data and their correspondence with chromatin structure. We classify existing algorithms on the basis of the data representation and pattern recognition paradigm they make use of. Finally, we outline some of the challenges ahead and promising future directions.
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Affiliation(s)
- Andrea Raffo
- Department of Biosciences, University of Oslo, 0316 Oslo, Norway
| | - Jonas Paulsen
- Department of Biosciences, University of Oslo, 0316 Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0316 Oslo, Norway
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4
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Weith M, Großbach J, Clement‐Ziza M, Gillet L, Rodríguez‐López M, Marguerat S, Workman CT, Picotti P, Bähler J, Aebersold R, Beyer A. Genetic effects on molecular network states explain complex traits. Mol Syst Biol 2023; 19:e11493. [PMID: 37485750 PMCID: PMC10407735 DOI: 10.15252/msb.202211493] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 06/29/2023] [Accepted: 07/04/2023] [Indexed: 07/25/2023] Open
Abstract
The complexity of many cellular and organismal traits results from the integration of genetic and environmental factors via molecular networks. Network structure and effect propagation are best understood at the level of functional modules, but so far, no concept has been established to include the global network state. Here, we show when and how genetic perturbations lead to molecular changes that are confined to small parts of a network versus when they lead to modulation of network states. Integrating multi-omics profiling of genetically heterogeneous budding and fission yeast strains with an array of cellular traits identified a central state transition of the yeast molecular network that is related to PKA and TOR (PT) signaling. Genetic variants affecting this PT state globally shifted the molecular network along a single-dimensional axis, thereby modulating processes including energy and amino acid metabolism, transcription, translation, cell cycle control, and cellular stress response. We propose that genetic effects can propagate through large parts of molecular networks because of the functional requirement to centrally coordinate the activity of fundamental cellular processes.
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Affiliation(s)
- Matthias Weith
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
| | - Jan Großbach
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
| | | | - Ludovic Gillet
- Department of BiologyInstitute of Molecular Systems Biology, ETH ZürichZürichSwitzerland
| | - María Rodríguez‐López
- Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentUniversity College LondonLondonUK
| | - Samuel Marguerat
- Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentUniversity College LondonLondonUK
| | - Christopher T Workman
- Department of Biotechnology and BiomedicineTechnical University of DenmarkLyngbyDenmark
| | - Paola Picotti
- Department of BiologyInstitute of Molecular Systems Biology, ETH ZürichZürichSwitzerland
| | - Jürg Bähler
- Institute of Healthy Ageing and Department of Genetics, Evolution & EnvironmentUniversity College LondonLondonUK
| | - Ruedi Aebersold
- Department of BiologyInstitute of Molecular Systems Biology, ETH ZürichZürichSwitzerland
| | - Andreas Beyer
- Excellence Cluster on Cellular Stress Responses in Aging Associated DiseasesUniversity of CologneCologneGermany
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5
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Nan L, Du C, Fan Y, Liu W, Luo X, Wang H, Ding L, Zhang Y, Chu C, Li C, Ren X, Yu H, Lu S, Zhang S. Association between Days Open and Parity, Calving Season or Milk Spectral Data. Animals (Basel) 2023; 13:ani13030509. [PMID: 36766398 PMCID: PMC9913365 DOI: 10.3390/ani13030509] [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: 12/14/2022] [Revised: 01/15/2023] [Accepted: 01/18/2023] [Indexed: 02/04/2023] Open
Abstract
Milk spectral data on 2118 cows from nine herds located in northern China were used to access the association of days open (DO). Meanwhile, the parity and calving season of dairy cows were also studied to characterize the difference in DO between groups of these two cow-level factors. The result of the linear mixed-effects model revealed that no significant differences were observed between the parity groups. However, a significant difference in DO exists between calving season groups. The interaction between parity and calving season presented that primiparous cows always exhibit lower DO among all calving season groups, and the variation in DO among parity groups was especially clearer in winter. Survival analysis revealed that the difference in DO between calving season groups might be caused by the different P/AI at the first TAI. In addition, the summer group had a higher chance of conception in the subsequent services than other groups, implying that the micro-environment featured by season played a critical role in P/AI. A weak linkage between DO and wavenumbers ranging in the mid-infrared region was detected. In summary, our study revealed that the calving season of dairy cows can be used to optimize the reproduction management. The potential application of mid-infrared spectroscopy in dairy cows needs to be further developed.
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Affiliation(s)
- Liangkang Nan
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Chao Du
- Henan Institute of Science and Technology, College of Animal Science and Veterinary Medicine, Xinxiang 453003, China
| | - Yikai Fan
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Wenju Liu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Xuelu Luo
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Haitong Wang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Lei Ding
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yi Zhang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Chu Chu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Chunfang Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiaoli Ren
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Hao Yu
- Hebei Livestock Breeding Station, Shijiazhuang 050000, China
| | - Shiyu Lu
- Hebei Livestock Breeding Station, Shijiazhuang 050000, China
| | - Shujun Zhang
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
- Correspondence:
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6
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Liu K, Li HD, Li Y, Wang J, Wang J. A Comparison of Topologically Associating Domain Callers Based on Hi-C Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:15-29. [PMID: 35104223 DOI: 10.1109/tcbb.2022.3147805] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Topologically associating domains (TADs) are local chromatin interaction domains, which have been shown to play an important role in gene expression regulation. TADs were originally discovered in the investigation of 3D genome organization based on High-throughput Chromosome Conformation Capture (Hi-C) data. Continuous considerable efforts have been dedicated to developing methods for detecting TADs from Hi-C data. Different computational methods for TADs identification vary in their assumptions and criteria in calling TADs. As a consequence, the TADs called by these methods differ in their similarities and biological features they are enriched in. In this work, we performed a systematic comparison of twenty-six TAD callers. We first compared the TADs and gaps between adjacent TADs across different methods, resolutions, and sequencing depths. We then assessed the quality of TADs and TAD boundaries according to three criteria: the decay of contact frequencies over the genomic distance, enrichment and depletion of regulatory elements around TAD boundaries, and reproducibility of TADs and TAD boundaries in replicate samples. Last, due to the lack of a gold standard of TADs, we also evaluated the performance of the methods on synthetic datasets. We discussed the key principles of TAD callers, and pinpointed current situation in the detection of TADs. We provide a concise, comprehensive, and systematic framework for evaluating the performance of TAD callers, and expect our work will provide useful guidance in choosing suitable approaches for the detection and evaluation of TADs.
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7
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Bendl J, Hauberg ME, Girdhar K, Im E, Vicari JM, Rahman S, Fernando MB, Townsley KG, Dong P, Misir R, Kleopoulos SP, Reach SM, Apontes P, Zeng B, Zhang W, Voloudakis G, Brennand KJ, Nixon RA, Haroutunian V, Hoffman GE, Fullard JF, Roussos P. The three-dimensional landscape of cortical chromatin accessibility in Alzheimer's disease. Nat Neurosci 2022; 25:1366-1378. [PMID: 36171428 PMCID: PMC9581463 DOI: 10.1038/s41593-022-01166-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 08/16/2022] [Indexed: 02/06/2023]
Abstract
To characterize the dysregulation of chromatin accessibility in Alzheimer's disease (AD), we generated 636 ATAC-seq libraries from neuronal and nonneuronal nuclei isolated from the superior temporal gyrus and entorhinal cortex of 153 AD cases and 56 controls. By analyzing a total of ~20 billion read pairs, we expanded the repertoire of known open chromatin regions (OCRs) in the human brain and identified cell-type-specific enhancer-promoter interactions. We show that interindividual variability in OCRs can be leveraged to identify cis-regulatory domains (CRDs) that capture the three-dimensional structure of the genome (3D genome). We identified AD-associated effects on chromatin accessibility, the 3D genome and transcription factor (TF) regulatory networks. For one of the most AD-perturbed TFs, USF2, we validated its regulatory effect on lysosomal genes. Overall, we applied a systematic approach to understanding the role of the 3D genome in AD. We provide all data as an online resource for widespread community-based analysis.
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Affiliation(s)
- Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mads E Hauberg
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative of Integrative Psychiatric Research (iPSYCH), Aarhus University, Aarhus, Denmark
- Centre for Integrative Sequencing (iSEQ), Aarhus University, Aarhus, Denmark
| | - Kiran Girdhar
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eunju Im
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Department of Psychiatry, New York University Langone Health, New York, NY, USA
| | - James M Vicari
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Graduate School of Biomedical Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Samir Rahman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael B Fernando
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Graduate School of Biomedical Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kayla G Townsley
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Graduate School of Biomedical Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pengfei Dong
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ruth Misir
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steven P Kleopoulos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sarah M Reach
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pasha Apontes
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Biao Zeng
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Wen Zhang
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Georgios Voloudakis
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kristen J Brennand
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Ralph A Nixon
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
- Department of Psychiatry, New York University Langone Health, New York, NY, USA
- Department of Cell Biology, New York University Langone Health, New York, NY, USA
- New York University Neuroscience Institute, New York, NY, USA
| | - Vahram Haroutunian
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA.
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8
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Sefer E. A comparison of topologically associating domain callers over mammals at high resolution. BMC Bioinformatics 2022; 23:127. [PMID: 35413815 PMCID: PMC9006547 DOI: 10.1186/s12859-022-04674-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 04/07/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Topologically associating domains (TADs) are locally highly-interacting genome regions, which also play a critical role in regulating gene expression in the cell. TADs have been first identified while investigating the 3D genome structure over High-throughput Chromosome Conformation Capture (Hi-C) interaction dataset. Substantial degree of efforts have been devoted to develop techniques for inferring TADs from Hi-C interaction dataset. Many TAD-calling methods have been developed which differ in their criteria and assumptions in TAD inference. Correspondingly, TADs inferred via these callers vary in terms of both similarities and biological features they are enriched in. RESULT We have carried out a systematic comparison of 27 TAD-calling methods over mammals. We use Micro-C, a recent high-resolution variant of Hi-C, to compare TADs at a very high resolution, and classify the methods into 3 categories: feature-based methods, Clustering methods, Graph-partitioning methods. We have evaluated TAD boundaries, gaps between adjacent TADs, and quality of TADs across various criteria. We also found particularly CTCF and Cohesin proteins to be effective in formation of TADs with corner dots. We have also assessed the callers performance on simulated datasets since a gold standard for TADs is missing. TAD sizes and numbers change remarkably between TAD callers and dataset resolutions, indicating that TADs are hierarchically-organized domains, instead of disjoint regions. A core subset of feature-based TAD callers regularly perform the best while inferring reproducible domains, which are also enriched for TAD related biological properties. CONCLUSION We have analyzed the fundamental principles of TAD-calling methods, and identified the existing situation in TAD inference across high resolution Micro-C interaction datasets over mammals. We come up with a systematic, comprehensive, and concise framework to evaluate the TAD-calling methods performance across Micro-C datasets. Our research will be useful in selecting appropriate methods for TAD inference and evaluation based on available data, experimental design, and biological question of interest. We also introduce our analysis as a benchmarking tool with publicly available source code.
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Affiliation(s)
- Emre Sefer
- Department of Computer Science, Ozyegin University, Istanbul, Turkey.
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9
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Girdhar K, Hoffman GE, Bendl J, Rahman S, Dong P, Liao W, Hauberg ME, Sloofman L, Brown L, Devillers O, Kassim BS, Wiseman JR, Park R, Zharovsky E, Jacobov R, Flatow E, Kozlenkov A, Gilgenast T, Johnson JS, Couto L, Peters MA, Phillips-Cremins JE, Hahn CG, Gur RE, Tamminga CA, Lewis DA, Haroutunian V, Dracheva S, Lipska BK, Marenco S, Kundakovic M, Fullard JF, Jiang Y, Roussos P, Akbarian S. Chromatin domain alterations linked to 3D genome organization in a large cohort of schizophrenia and bipolar disorder brains. Nat Neurosci 2022; 25:474-483. [PMID: 35332326 PMCID: PMC8989650 DOI: 10.1038/s41593-022-01032-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 02/09/2022] [Indexed: 12/19/2022]
Abstract
Chromosomal organization, scaling from the 147-base pair (bp) nucleosome to megabase-ranging domains encompassing multiple transcriptional units, including heritability loci for psychiatric traits, remains largely unexplored in the human brain. In this study, we constructed promoter- and enhancer-enriched nucleosomal histone modification landscapes for adult prefrontal cortex from H3-lysine 27 acetylation and H3-lysine 4 trimethylation profiles, generated from 388 controls and 351 individuals diagnosed with schizophrenia (SCZ) or bipolar disorder (BD) (n = 739). We mapped thousands of cis-regulatory domains (CRDs), revealing fine-grained, 104-106-bp chromosomal organization, firmly integrated into Hi-C topologically associating domain stratification by open/repressive chromosomal environments and nuclear topography. Large clusters of hyper-acetylated CRDs were enriched for SCZ heritability, with prominent representation of regulatory sequences governing fetal development and glutamatergic neuron signaling. Therefore, SCZ and BD brains show coordinated dysregulation of risk-associated regulatory sequences assembled into kilobase- to megabase-scaling chromosomal domains.
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Affiliation(s)
- Kiran Girdhar
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Gabriel E Hoffman
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jaroslav Bendl
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Samir Rahman
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pengfei Dong
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Will Liao
- New York Genome Center, New York, NY, USA
| | - Mads E Hauberg
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Laura Sloofman
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Leanne Brown
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Olivia Devillers
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bibi S Kassim
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jennifer R Wiseman
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Royce Park
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Elizabeth Zharovsky
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rivky Jacobov
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Elie Flatow
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexey Kozlenkov
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thomas Gilgenast
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Jessica S Johnson
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lizette Couto
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Jennifer E Phillips-Cremins
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Chang-Gyu Hahn
- Department of Psychiatry, Vickie and Jack Farber Institute for Neuroscience, Jefferson University, Philadelphia, PA, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Carol A Tamminga
- Department of Psychiatry, The University of Texas Southwestern Medical School, Dallas, TX, USA
| | - David A Lewis
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Vahram Haroutunian
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Stella Dracheva
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Barbara K Lipska
- Human Brain Collection Core, National Institute of Mental Health-Intramural Research Program, Bethesda, MD, USA
| | - Stefano Marenco
- Human Brain Collection Core, National Institute of Mental Health-Intramural Research Program, Bethesda, MD, USA
| | - Marija Kundakovic
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Biological Sciences, Fordham University, Bronx, NY, USA
| | - John F Fullard
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yan Jiang
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Panos Roussos
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA.
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Schahram Akbarian
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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10
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Khanal P, Tempelman RJ. The use of milk Fourier-transform mid-infrared spectroscopy to diagnose pregnancy and determine spectral regional associations with pregnancy in US dairy cows. J Dairy Sci 2022; 105:3209-3221. [DOI: 10.3168/jds.2021-21079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 12/21/2021] [Indexed: 11/19/2022]
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11
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Hierarchical correction of p-values via an ultrametric tree running Ornstein-Uhlenbeck process. Comput Stat 2021. [DOI: 10.1007/s00180-021-01148-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
AbstractStatistical testing is classically used as an exploratory tool to search for association between a phenotype and many possible explanatory variables. This approach often leads to multiple testing under dependence. We assume a hierarchical structure between tests via an Ornstein-Uhlenbeck process on a tree. The process correlation structure is used for smoothing the p-values. We design a penalized estimation of the mean of the Ornstein-Uhlenbeck process for p-value computation. The performances of the algorithm are assessed via simulations. Its ability to discover new associations is demonstrated on a metagenomic dataset. The corresponding R package is available from https://github.com/abichat/zazou.
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12
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Rovere G, de Los Campos G, Lock AL, Worden L, Vazquez AI, Lee K, Tempelman RJ. Prediction of fatty acid composition using milk spectral data and its associations with various mid-infrared spectral regions in Michigan Holsteins. J Dairy Sci 2021; 104:11242-11258. [PMID: 34275636 DOI: 10.3168/jds.2021-20267] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 05/28/2021] [Indexed: 11/19/2022]
Abstract
Fatty acid composition in milk is not only reflective of nutritional quality but also potentially predictive of other attributes (e. g. including the cow's energy balance and its relative output of methane emissions). Furthermore, a higher ratio of long-chain to short-chain fatty acids or mean carbon number has been associated with negative energy balance in dairy cows, whereas enhanced nutritional properties have been generally associated with higher levels of unsaturation. We set out to directly compare Bayesian regression strategies with partial least squares for the prediction of various milk fatty acids using Fourier-transform infrared spectrum data on 777 milk samples taken from 579 cows on 4 Michigan dairy herds between 5 and 90 d in milk. We also set out to identify those spectral regions that might be associated with fatty acids and whether carbon number or level of unsaturation might contribute to the strength of these associations. These associations were based on adaptively clustered windows of wavenumbers to mitigate the distorting effects of severe multicollinearity on marginal associations involving individual wavenumbers. In general, Bayesian regression methods, particularly the variable selection method BayesB, outperformed partial least squares regression for cross-validation prediction accuracy for both individual fatty acids and fatty acid groups. Strong signals for wavenumber associations using BayesB were well distributed throughout the mid-infrared spectrum, particularly between 910 and 3,998 cm-1. Carbon number appeared to be linearly related to strength of wavenumber associations for 38 moderately to highly predicted fatty acids within the spectral regions of 2,286 to 2,376 and 2,984 to 3,100 cm-1, whereas nonlinear associations were determined within 1,141 to 1,205; 1,570 to 1,630; and 1,727 to 1,768 cm-1. However, no such associations were detected with level of unsaturation. Spectral regions where there were significant relationships between strength of association and carbon number may be useful targets for inferring the relative proportion of long-chain to short-chain fatty acids, and hence energy balance.
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Affiliation(s)
- G Rovere
- Department of Animal Science, Michigan State University, East Lansing 48824-1225; Department of Epidemiology and Biostatistics, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing 48824-1225
| | - G de Los Campos
- Department of Epidemiology and Biostatistics, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing 48824-1225; Department of Statistics and Probability, Michigan State University, East Lansing 48824-1225
| | - A L Lock
- Department of Animal Science, Michigan State University, East Lansing 48824-1225
| | - L Worden
- Department of Animal Science, Michigan State University, East Lansing 48824-1225
| | - A I Vazquez
- Department of Epidemiology and Biostatistics, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing 48824-1225
| | - K Lee
- Michigan State University Extension, Lake City, MI 49651
| | - R J Tempelman
- Department of Animal Science, Michigan State University, East Lansing 48824-1225.
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