SnapHiC2: A computationally efficient loop caller for single cell Hi-C data
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037022002021 |
| _version_ | 1797978245601165312 |
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| author | Xiaoqi Li Lindsay Lee Armen Abnousi Miao Yu Weifang Liu Le Huang Yun Li Ming Hu |
| author_facet | Xiaoqi Li Lindsay Lee Armen Abnousi Miao Yu Weifang Liu Le Huang Yun Li Ming Hu |
| author_sort | Xiaoqi Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T05:19:56Z |
| format | Article |
| id | doaj.art-d1ab2b29cce1416aa0a89af6b4d35bba |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-04-11T05:19:56Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-d1ab2b29cce1416aa0a89af6b4d35bba2022-12-24T04:52:42ZengElsevierComputational and Structural Biotechnology Journal2001-03702022-01-012027782783SnapHiC2: A computationally efficient loop caller for single cell Hi-C dataXiaoqi Li0Lindsay Lee1Armen Abnousi2Miao Yu3Weifang Liu4Le Huang5Yun Li6Ming Hu7Carolina Health Informatics Program, University of North Carolina, Chapel Hill, NC, USADepartment of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USADepartment of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USAState Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, ChinaDepartment of Biostatistics, University of North Carolina, Chapel Hill, NC, USACurriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USADepartment 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; Corresponding authors.Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA; Corresponding authors.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.http://www.sciencedirect.com/science/article/pii/S2001037022002021Chromatin spatial organizationsingle cell Hi-CChromatin loopsthe random walk with restart (RWR) algorithm |
| spellingShingle | Xiaoqi Li Lindsay Lee Armen Abnousi Miao Yu Weifang Liu Le Huang Yun Li Ming Hu SnapHiC2: A computationally efficient loop caller for single cell Hi-C data Computational and Structural Biotechnology Journal Chromatin spatial organization single cell Hi-C Chromatin loops the random walk with restart (RWR) algorithm |
| title | SnapHiC2: A computationally efficient loop caller for single cell Hi-C data |
| title_full | SnapHiC2: A computationally efficient loop caller for single cell Hi-C data |
| title_fullStr | SnapHiC2: A computationally efficient loop caller for single cell Hi-C data |
| title_full_unstemmed | SnapHiC2: A computationally efficient loop caller for single cell Hi-C data |
| title_short | SnapHiC2: A computationally efficient loop caller for single cell Hi-C data |
| title_sort | snaphic2 a computationally efficient loop caller for single cell hi c data |
| topic | Chromatin spatial organization single cell Hi-C Chromatin loops the random walk with restart (RWR) algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2001037022002021 |
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