scHiMe: predicting single-cell DNA methylation levels based on single-cell Hi-C data.
scHiMe: predicting single-cell DNA methylation levels based on single-cell Hi-C data.
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scHiMe：根据单细胞 Hi-C 数据预测单细胞 DNA 甲基化水平。
DOI：
10.1093/bib/bbad223
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发表时间：
2023-07-20
影响因子：
9.5
通讯作者：
中科院分区：
文献类型：
--
作者：
最近，一项名为甲基-3C的生物化学实验被开发出来，以同时捕捉单个单个细胞的染色体构象和DNA甲基化水平。然而，与由单独的单细胞产生的大量单细胞Hi-C数据相比，这次实验产生的数据集在科学界仍然很少。因此，需要一种基于同一单个细胞上的单细胞Hi-C数据来预测单细胞甲基化水平的计算工具。我们开发了一种名为scHiMe的图形转换器，用于根据单细胞Hi-C数据和DNA核苷酸序列准确预测碱基对特定(BP特定)的甲基化水平。我们对scHiMe进行了基准测试，以预测人类基因组的所有启动子、所有启动子区域以及相应的第一外显子和内含子区域以及整个基因组上的随机区域上BP特异性甲基化水平。我们的评估显示，预测的甲基化水平与甲基-3C检测到的甲基化水平之间具有高度的一致性。此外，预测的DNA甲基化水平导致准确地将细胞分类为不同的细胞类型，这表明我们的算法成功地捕捉到了单细胞Hi-C数据中细胞间的变异性。可以在http://dna.cs.miami.edu/scHiMe/.上免费获得scHiMe
Recently a biochemistry experiment named methyl-3C was developed to simultaneously capture the chromosomal conformations and DNA methylation levels on individual single cells. However, the number of data sets generated from this experiment is still small in the scientific community compared with the greater amount of single-cell Hi-C data generated from separate single cells. Therefore, a computational tool to predict single-cell methylation levels based on single-cell Hi-C data on the same individual cells is needed. We developed a graph transformer named scHiMe to accurately predict the base-pair-specific (bp-specific) methylation levels based on both single-cell Hi-C data and DNA nucleotide sequences. We benchmarked scHiMe for predicting the bp-specific methylation levels on all of the promoters of the human genome, all of the promoter regions together with the corresponding first exon and intron regions, and random regions on the whole genome. Our evaluation showed a high consistency between the predicted and methyl-3C-detected methylation levels. Moreover, the predicted DNA methylation levels resulted in accurate classifications of cells into different cell types, which indicated that our algorithm successfully captured the cell-to-cell variability in the single-cell Hi-C data. scHiMe is freely available at http://dna.cs.miami.edu/scHiMe/.
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影响因子：
5.9
作者：
Hui T;Cao Q;Wegrzyn-Woltosz J;O'Neill K;Hammond CA;Knapp DJHF;Laks E;Moksa M;Aparicio S;Eaves CJ;Karsan A;Hirst M
通讯作者：
Hirst M
影响因子：
64.5
作者：
van Dijk D;Sharma R;Nainys J;Yim K;Kathail P;Carr AJ;Burdziak C;Moon KR;Chaffer CL;Pattabiraman D;Bierie B;Mazutis L;Wolf G;Krishnaswamy S;Pe'er D
通讯作者：
Pe'er D
影响因子：
48
作者：
Ramani V;Deng X;Qiu R;Gunderson KL;Steemers FJ;Disteche CM;Noble WS;Duan Z;Shendure J
通讯作者：
Shendure J
影响因子：
14.9
作者：
Frankish A;Diekhans M;Ferreira AM;Johnson R;Jungreis I;Loveland J;Mudge JM;Sisu C;Wright J;Armstrong J;Barnes I;Berry A;Bignell A;Carbonell Sala S;Chrast J;Cunningham F;Di Domenico T;Donaldson S;Fiddes IT;García Girón C;Gonzalez JM;Grego T;Hardy M;Hourlier T;Hunt T;Izuogu OG;Lagarde J;Martin FJ;Martínez L;Mohanan S;Muir P;Navarro FCP;Parker A;Pei B;Pozo F;Ruffier M;Schmitt BM;Stapleton E;Suner MM;Sycheva I;Uszczynska-Ratajczak B;Xu J;Yates A;Zerbino D;Zhang Y;Aken B;Choudhary JS;Gerstein M;Guigó R;Hubbard TJP;Kellis M;Paten B;Reymond A;Tress ML;Flicek P
通讯作者：
Flicek P
影响因子：
7
作者：
Uzun Y;Wu H;Tan K
通讯作者：
Tan K