| URL: | http://bio-bigdata.hrbmu.edu.cn/LnCeCell |
| Full name: | lncRNA-associated ceRNA networks at single-cell resolution |
| Description: | LnCeCell 2.0 is an updated resource for lncRNA-associated ceRNA networks, integrating 257 stRNA-seq datasets across 86 diseases and 80 normal tissues. It provides 836,581 cell/spot-specific ceRNA interactions, 15,489 curated lncRNA biomarkers, clinical profiles of 20,326 cancer patients, and 24 analytical tools for exploring ceRNA mechanisms at single-cell/spatial resolution. |
| Year founded: | 2021 |
| Last update: | 2024-10-29 |
| Version: | v2.0 |
| Accessibility: |
Accessible
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| Country/Region: | China |
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| University/Institution: | Harbin Medical University |
| Address: | College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China |
| City: | Harbin |
| Province/State: | Heilongjiang |
| Country/Region: | China |
| Contact name (PI/Team): | Peng Wang |
| Contact email (PI/Helpdesk): | wpgqy@163.com |
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LnCeCell 2.0: an updated resource for lncRNA-associated ceRNA networks and web tools based on single-cell and spatial transcriptomics sequencing data. [PMID: 39470723]
We describe LnCeCell 2.0 (http://bio-bigdata.hrbmu.edu.cn/LnCeCell), an updated resource for lncRNA-associated competing endogenous RNA (ceRNA) networks and web tools based on single-cell and spatial transcriptomics sequencing (stRNA-seq) data. We have updated the LnCeCell 2.0 database with significantly expanded data and improved features, including (i) 257 single-cell RNA sequencing and stRNA-seq datasets across 86 diseases/phenotypes and 80 human normal tissues, (ii) 836 581 cell-specific and spatial spot-specific ceRNA interactions and functional networks for 1 002 988 cells and 367 971 spatial spots, (iii) 15 489 experimentally supported lncRNA biomarkers related to disease pathology, diagnosis and treatment, (iv) detailed annotation of cell type, cell state, subcellular and extracellular locations of ceRNAs through manual curation and (v) ceRNA expression profiles and follow-up clinical information of 20 326 cancer patients. Further, a panel of 24 flexible tools (including 8 comprehensive and 16 mini-analysis tools) was developed to investigate ceRNA-regulated mechanisms at single-cell/spot resolution. The CeCellTraject tool, for example, illustrates the detailed ceRNA distribution of different cell populations and explores the dynamic change of the ceRNA network along the developmental trajectory. LnCeCell 2.0 will facilitate the study of fine-tuned lncRNA-ceRNA networks with single-cell and spatial spot resolution, helping us to understand the regulatory mechanisms behind complex microbial ecosystems. |
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LnCeCell: a comprehensive database of predicted lncRNA-associated ceRNA networks at single-cell resolution. [PMID: 33219686]
Within the tumour microenvironment, cells exhibit different behaviours driven by fine-tuning of gene regulation. Identification of cellular-specific gene regulatory networks will deepen the understanding of disease pathology at single-cell resolution and contribute to the development of precision medicine. Here, we describe a database, LnCeCell (http://www.bio-bigdata.net/LnCeCell/ or http://bio-bigdata.hrbmu.edu.cn/LnCeCell/), which aims to document cellular-specific long non-coding RNA (lncRNA)-associated competing endogenous RNA (ceRNA) networks for personalised characterisation of diseases based on the 'One Cell, One World' theory. LnCeCell is curated with cellular-specific ceRNA regulations from >94 000 cells across 25 types of cancers and provides >9000 experimentally supported lncRNA biomarkers, associated with tumour metastasis, recurrence, prognosis, circulation, drug resistance, etc. For each cell, LnCeCell illustrates a global map of ceRNA sub-cellular locations, which have been manually curated from the literature and related data sources, and portrays a functional state atlas for a single cancer cell. LnCeCell also provides several flexible tools to infer ceRNA functions based on a specific cellular background. LnCeCell serves as an important resource for investigating the gene regulatory networks within a single cell and can help researchers understand the regulatory mechanisms underlying complex microbial ecosystems and individual phenotypes. |