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

Author:

Zhang Weihang1ORCID,Suo Jinli123,Yan Yan45,Yang Runzhao1,Lu Yiming1,Jin Yiqi1,Gao Shuochen1,Li Shao14ORCID,Gao Juntao45,Zhang Michael45,Dai Qionghai12

Affiliation:

1. Department of Automation, Tsinghua University , Beijing 100084, China

2. Institute of Brain and Cognitive Sciences, Tsinghua University , Beijing 100084, China

3. Shanghai Artificial Intelligence Laboratory , Shanghai 200232, China

4. MOE Key Laboratory of Bioinformatics; Bioinformatics Division, BNRist; Center for Synthetic & Systems Biology, Tsinghua University , Beijing 100084, China

5. School of Medicine, Tsinghua University , Beijing 100084, China

Abstract

Abstract 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.

Funder

Beijing Municipal Natural Science Foundation

National Natural Science Foundation of China

State Key Research Development Program of China

CAS Interdisciplinary Innovation Team

Publisher

Oxford University Press (OUP)

Subject

Genetics

Reference96 articles.

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