Guoji Guo
Zhejiang University
90 Papers
129 Citations
Guoji Guo is an academic researcher from Zhejiang University. The author has contributed to research in topics: Biology & Medicine. The author has an hindex of 33, co-authored 65 publications. Previous affiliations of Guoji Guo include Boston Children's Hospital & Genome Institute of Singapore.
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Papers
The complete and fully-phased diploid genome of a male Han Chinese.
Chentao Yang,Yang Zhou,Yanni Song,Dongya Wu,Yancai Zeng,Lei Nie,Panhong Liu,Shilong Zhang,Guangji Chen,Jinjin Xu,Hongling Zhou,Long Zhou,Xiaobo Qian,C. Liu,Shangjin Tan,Chengran Zhou,Mengyang Xu,Yanwei Qi,Xiaobo Wang,Lidong Guo,Guangyi Fan,Yuan Deng,Yong Zhang,Jiazheng Jin,Yu-Qing He,Chun Guo,Guoji Guo,Qing Zhou,Xun Xu,Huanming Yang,Jian Wang,Shuhua Xu,Yafei Mao,Xin Jin,Jue Ruan,Guojie Zhang +35 more
TL;DR: A fully phased and well-annotated diploid human genome from a Han Chinese male individual (CN1), in which the assemblies of both haploids achieve the telomere-to-telomere (T2T) level, and it is found that CN1 outperforms CHM13 as a reference genome in mapping and variant calling for the East Asian population.
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High-throughput single nucleus total RNA sequencing of formalin-fixed paraffin-embedded tissues by snRandom-seq
Hongyu Chen,Yuyi Zhu,Jiaye Chen,Haide Chen,Lili Yang,Shengyu Ni,Zhao-Lun Wang,Feng Chen,Hong-Jiang Zhang,Dandan Zhang,Longjiang Fan,Guoji Guo,Yongcheng Wang +12 more
TL;DR: Wang et al. as mentioned in this paper developed a droplet-based snRNA sequencing technology (snRandom-seq) for FFPE tissues by capturing full-length total RNAs with random primers.
Droplet-based high-throughput single microbe RNA sequencing by smRandom-seq
Ziye Xu,Yuting Wang,Kuanwei Sheng,Raoul G. Rosenthal,Nan Liu,Xiao Hu,Nian Zhang,Jiaye Chen,Mengdi Song,Yuexiao Lv,Shunji Zhang,Yingjuan Huang,Zhao-Lun Wang,Ting Cao,Yifei Shen,Yan Jiang,Yuan Yu,Yu Chen,Guoji Guo,Peng Yin,David A. Weitz,Yongcheng Wang +21 more
TL;DR: A droplet-based high-throughput single- microbe RNA-seq assay (smRandom-seq), using random primers for in situ cDNA generation, droplets for single-microbe barcoding, and CRISPR-based rRNA depletion for mRNA enrichment, which successfully captured transcriptome changes of thousands of individual E. coli.
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Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types
Jiaqi Li,Jingjing Wang,Peijing Zhang,Renying Wang,Yuqing Mei,Zhongyi Sun,Lijiang Fei,Mengmeng Jiang,Lifeng Ma,Weigao E,Haide Chen,Xinru Wang,Yuting Fu,Hanyu Wu,Daiyuan Liu,Xueyi Wang,Jingyu Li,Qile Guo,Yuan Liao,Cheng-Zhi Yu,Danmei Jia,Jian Wu,Shibo He,Hua-Yan Liu,Jun Ma,Kai-rong Lei,Jiming Chen,Xiao-Ming Han,Guoji Guo +28 more
TL;DR: This work generated whole-body single-cell transcriptomic landscapes of zebrafish, Drosophila and earthworm and developed a deep-learning-based strategy, Nvwa, to predict gene expression and identify regulatory sequences at the single- cell level.
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scMCA: A Tool to Define Mouse Cell Types Based on Single-Cell Digital Expression.
TL;DR: A "single-cell Mouse Cell Atlas (scMCA) analysis" pipeline based on scRNA-seq datasets covering all mouse cell types is built based on the scMCA reference and a tool is used to match single-cell digital expression to its closest cell type.
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