iSMOD: an integrative browser for image-based single-cell multi-omics data

被引:4
|
作者
Zhang, Weihang [1 ]
Suo, Jinli [1 ,2 ,3 ]
Yan, Yan [4 ,5 ,6 ]
Yang, Runzhao [1 ]
Lu, Yiming [1 ]
Jin, Yiqi [1 ]
Gao, Shuochen [1 ]
Li, Shao [1 ,4 ,5 ,6 ]
Gao, Juntao [4 ,5 ,6 ,7 ]
Zhang, Michael [4 ,5 ,6 ,7 ]
Dai, Qionghai [1 ,2 ]
机构
[1] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
[2] Tsinghua Univ, Inst Brain & Cognit Sci, Beijing 100084, Peoples R China
[3] Shanghai Artificial Intelligence Lab, Shanghai 200232, Peoples R China
[4] Tsinghua Univ, MOE Key Lab Bioinformat, Beijing 100084, Peoples R China
[5] Tsinghua Univ, Bioinformat Div, BNRist, Beijing 100084, Peoples R China
[6] Tsinghua Univ, Ctr Synthet & Syst Biol, Beijing 100084, Peoples R China
[7] Tsinghua Univ, Sch Med, Beijing 100084, Peoples R China
基金
中国国家自然科学基金;
关键词
SILVER-RUSSELL-SYNDROME; VISUALIZATION SYSTEM; SOCIAL NETWORK; NONCODING RNA; PROVIDES; ORGANIZATION; ARCHITECTURE; ASSOCIATION; DYNAMICS; BODIES;
D O I
10.1093/nar/gkad580
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Genomic and transcriptomic image data, represented by DNA and RNA fluorescence in situ hybridization (FISH), respectively, together with proteomic data, particularly that related to nuclear proteins, can help elucidate gene regulation in relation to the spatial positions of chromatins, messenger RNAs, and key proteins. However, methods for image-based multi-omics data collection and analysis are lacking. To this end, we aimed to develop the first integrative browser called iSMOD (image-based Single-cell Multi-omics Database) to collect and browse comprehensive FISH and nucleus proteomics data based on the title, abstract, and related experimental figures, which integrates multi-omics studies focusing on the key players in the cell nucleus from 20 000+ (still growing) published papers. We have also provided several exemplar demonstrations to show iSMOD's wide applications-profiling multi-omics research to reveal the molecular target for diseases; exploring the working mechanism behind biological phenomena using multi-omics interactions, and integrating the 3D multi-omics data in a virtual cell nucleus. iSMOD is a cornerstone for delineating a global view of relevant research to enable the integration of scattered data and thus provides new insights regarding the missing components of molecular pathway mechanisms and facilitates improved and efficient scientific research.
引用
收藏
页码:8348 / 8366
页数:19
相关论文
共 50 条
  • [41] EpiDamID, a new single-cell multi-omics tool
    不详
    NATURE REVIEWS GENETICS, 2022, 23 (6) : 323 - 323
  • [42] Single-cell multi-omics in the medicinal plant Catharanthusroseus
    Li, Chenxin
    Wood, Joshua C.
    Vu, Anh Hai
    Hamilton, John P.
    Lopez, Carlos Eduardo Rodriguez
    Payne, Richard M. E.
    Guerrero, Delia Ayled Serna
    Gase, Klaus
    Yamamoto, Kotaro
    Vaillancourt, Brieanne
    Caputi, Lorenzo
    O'Connor, Sarah E.
    Buell, C. Robin
    NATURE CHEMICAL BIOLOGY, 2023, 19 (08) : 1031 - +
  • [43] The technological landscape and applications of single-cell multi-omics
    Baysoy, Alev
    Bai, Zhiliang
    Satija, Rahul
    Fan, Rong
    NATURE REVIEWS MOLECULAR CELL BIOLOGY, 2023, 24 (10) : 695 - 713
  • [44] The technological landscape and applications of single-cell multi-omics
    Alev Baysoy
    Zhiliang Bai
    Rahul Satija
    Rong Fan
    Nature Reviews Molecular Cell Biology, 2023, 24 : 695 - 713
  • [45] How single-cell multi-omics builds relationships
    Vivien Marx
    Nature Methods, 2022, 19 : 142 - 146
  • [46] Applications of single-cell multi-omics in liver cancer
    Peeters, Frederik
    Cappuyns, Sarah
    Pique-Gili, Marta
    Phillips, Gino
    Verslype, Chris
    Lambrechts, Diether
    Dekervel, Jeroen
    JHEP REPORTS, 2024, 6 (07)
  • [47] Scbean: a python']python library for single-cell multi-omics data analysis
    Zhang, Haohui
    Wang, Yuwei
    Lian, Bin
    Wang, Yiran
    Li, Xingyi
    Wang, Tao
    Shang, Xuequn
    Yang, Hui
    Aziz, Ahmad
    Hu, Jialu
    BIOINFORMATICS, 2024, 40 (02)
  • [48] Molecular mechanisms reconstruction from single-cell multi-omics data with HuMMuS
    Trimbour, Remi
    Deutschmann, Ina Maria
    Cantini, Laura
    BIOINFORMATICS, 2024, 40 (05)
  • [49] Multi-omics at single-cell resolution: comparison of experimental and data fusion approaches
    Leonavicius, Karolis
    Nainys, Juozas
    Kuciauskas, Dalius
    Mazutis, Linas
    CURRENT OPINION IN BIOTECHNOLOGY, 2019, 55 : 159 - 166
  • [50] Clustering of single-cell multi-omics data with a multimodal deep learning method
    Xiang Lin
    Tian Tian
    Zhi Wei
    Hakon Hakonarson
    Nature Communications, 13