LnCeCell: a comprehensive database of predicted lncRNA-associated ceRNA networks at single-cell resolution

Abstract 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.

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Cell-Type-Specific Gene Regulatory Networks Underlying Murine Neonatal Heart Regeneration at Single-Cell Resolution

Cell Reports ◽

10.1016/j.celrep.2020.108472 ◽

2020 ◽

Vol 33 (10) ◽

pp. 108472

Author(s):

Zhaoning Wang ◽

Miao Cui ◽

Akansha M. Shah ◽

Wei Tan ◽

Ning Liu ◽

...

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Specific Gene ◽

Heart Regeneration ◽

Cell Type ◽

Neonatal Heart ◽

Cell Type Specific ◽

Gene Regulatory

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Cell-Type-Specific Gene Regulatory Networks Underlying Murine Neonatal Heart Regeneration at Single-Cell Resolution

Cell Reports ◽

10.1016/j.celrep.2021.109211 ◽

2021 ◽

Vol 35 (8) ◽

pp. 109211

Author(s):

Zhaoning Wang ◽

Miao Cui ◽

Akansha M. Shah ◽

Wei Tan ◽

Ning Liu ◽

...

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Specific Gene ◽

Heart Regeneration ◽

Cell Type ◽

Neonatal Heart ◽

Cell Type Specific ◽

Gene Regulatory

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Inferring gene regulatory networks from single-cell RNA-seq temporal snapshot data requires higher-order moments

Patterns ◽

10.1016/j.patter.2021.100332 ◽

2021 ◽

Vol 2 (9) ◽

pp. 100332

Author(s):

N. Alexia Raharinirina ◽

Felix Peppert ◽

Max von Kleist ◽

Christof Schütte ◽

Vikram Sunkara

Keyword(s):

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Higher Order ◽

Rna Seq ◽

Higher Order Moments ◽

Gene Regulatory

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Adaptive NetworkProfiler for Identifying Cancer Characteristic-Specific Gene Regulatory Networks

Journal of Computational Biology ◽

10.1089/cmb.2017.0120 ◽

2018 ◽

Vol 25 (2) ◽

pp. 130-145 ◽

Cited By ~ 3

Author(s):

Heewon Park ◽

Teppei Shimamura ◽

Seiya Imoto ◽

Satoru Miyano

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Specific Gene ◽

Gene Regulatory

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Discriminating the Single-cell Gene Regulatory Networks of Human Pancreatic Islets: A Novel Deep Learning Application

10.1101/2020.08.30.273839 ◽

2020 ◽

Author(s):

Turki Turki ◽

Y-h. Taguchi

Keyword(s):

Deep Learning ◽

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Metabolic Diseases ◽

Large Data ◽

Data Repositories ◽

Cell Gene Expression ◽

Gene Regulatory ◽

Cell Gene

AbstractAnalyzing single-cell pancreatic data would play an important role in understanding various metabolic diseases and health conditions. Due to the sparsity and noise present in such single-cell gene expression data, analyzing various functions related to the inference of gene regulatory networks, derived from single-cell data, remains difficult, thereby posing a barrier to the deepening of understanding of cellular metabolism. Since recent studies have led to the reliable inference of single-cell gene regulatory networks (SCGRNs), the challenge of discriminating between SCGRNs has now arisen. By accurately discriminating between SCGRNs (e.g., distinguishing SCGRNs of healthy pancreas from those of T2D pancreas), biologists would be able to annotate, organize, visualize, and identify common patterns of SCGRNs for metabolic diseases. Such annotated SCGRNs could play an important role in speeding up the process of building large data repositories. In this study, we aimed to contribute to the development of a novel deep learning (DL) application. First, we generated a dataset consisting of 224 SCGRNs belonging to both T2D and healthy pancreas and made it freely available. Next, we chose seven DL architectures, including VGG16, VGG19, Xception, ResNet50, ResNet101, DenseNet121, and DenseNet169, trained each of them on the dataset, and checked prediction based on a test set. We evaluated the DL architectures on an HP workstation platform with a single NVIDIA GeForce RTX 2080Ti GPU. Experimental results on the whole dataset, using several performance measures, demonstrated the superiority of VGG19 DL model in the automatic classification of SCGRNs, derived from the single-cell pancreatic data.

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Inference of differential gene regulatory networks based on gene expression and genetic perturbation data

Bioinformatics ◽

10.1093/bioinformatics/btz529 ◽

2019 ◽

Vol 36 (1) ◽

pp. 197-204 ◽

Cited By ~ 2

Author(s):

Xin Zhou ◽

Xiaodong Cai

Keyword(s):

Gene Expression ◽

Gene Regulatory Networks ◽

Structural Equation ◽

Regulatory Networks ◽

Supplementary Information ◽

Specific Gene ◽

Joint Inference ◽

Perturbation Data ◽

Gene Regulatory ◽

The Difference

Abstract Motivation Gene regulatory networks (GRNs) of the same organism can be different under different conditions, although the overall network structure may be similar. Understanding the difference in GRNs under different conditions is important to understand condition-specific gene regulation. When gene expression and other relevant data under two different conditions are available, they can be used by an existing network inference algorithm to estimate two GRNs separately, and then to identify the difference between the two GRNs. However, such an approach does not exploit the similarity in two GRNs, and may sacrifice inference accuracy. Results In this paper, we model GRNs with the structural equation model (SEM) that can integrate gene expression and genetic perturbation data, and develop an algorithm named fused sparse SEM (FSSEM), to jointly infer GRNs under two conditions, and then to identify difference of the two GRNs. Computer simulations demonstrate that the FSSEM algorithm outperforms the approaches that estimate two GRNs separately. Analysis of a dataset of lung cancer and another dataset of gastric cancer with FSSEM inferred differential GRNs in cancer versus normal tissues, whose genes with largest network degrees have been reported to be implicated in tumorigenesis. The FSSEM algorithm provides a valuable tool for joint inference of two GRNs and identification of the differential GRN under two conditions. Availability and implementation The R package fssemR implementing the FSSEM algorithm is available at https://github.com/Ivis4ml/fssemR.git. It is also available on CRAN. Supplementary information Supplementary data are available at Bioinformatics online.

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Comparison of gene regulatory networks to identify pathogenic genes for lymphoma

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720020500298 ◽

2020 ◽

Vol 18 (05) ◽

pp. 2050029

Author(s):

Xiao Yu ◽

Tongfeng Weng ◽

Changgui Gu ◽

Huijie Yang

Keyword(s):

Retinoic Acid ◽

Environmental Factors ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Retinoic Acid Receptor ◽

Specific Gene ◽

Acid Receptor ◽

Cancer Pathways ◽

Pathogenic Genes ◽

Gene Regulatory

Lymphoma is the most complicated cancer that can be divided into several tens of subtypes. It may occur in any part of body that has lymphocytes, and is closely correlated with diverse environmental factors such as the ionizing radiation, chemocarcinogenesis, and virus infection. All the environmental factors affect the lymphoma through genes. Identifying pathogenic genes for lymphoma is consequently an essential task to understand its complexity in a unified framework. In this paper, we propose a new method to expose high-confident edges in gene regulatory networks (GRNs) for a total of 32 organs, called Filtered GRNs (f-GRNs), comparison of which gives us a proper reference for the Lymphoma, i.e. the B-lymphocytes cells, whose f-GRN is closest with that for the Lymphoma. By using the Gene Ontology and Biological Process analysis we display the differences of the two networks’ hubs in biological functions. Matching with the Genecards shows that most of the hubs take part in the genetic information transmission and expression, except a specific gene of Retinoic Acid Receptor Alpha (RARA) that encodes the retinoic acid receptor. In the lymphoma, the genes in the RARA ego-network are involved in two cancer pathways, and the RARA is present only in these cancer pathways. For the lymphoid B cells, however, the genes in the RARA ego-network do not participate in cancer-related pathways.

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Gynoecium size and ovule number are interconnected traits that impact seed yield

Journal of Experimental Botany ◽

10.1093/jxb/eraa050 ◽

2020 ◽

Vol 71 (9) ◽

pp. 2479-2489 ◽

Cited By ~ 7

Author(s):

Mara Cucinotta ◽

Maurizio Di Marzo ◽

Andrea Guazzotti ◽

Stefan de Folter ◽

Martin M Kater ◽

...

Keyword(s):

Seed Yield ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Current Knowledge ◽

Fine Tuning ◽

World Population ◽

Meristematic Tissue ◽

Reproductive Part ◽

Ovule Number ◽

Gene Regulatory

Abstract Angiosperms form the largest group of land plants and display an astonishing diversity of floral structures. The development of flowers greatly contributed to the evolutionary success of the angiosperms as they guarantee efficient reproduction with the help of either biotic or abiotic vectors. The female reproductive part of the flower is the gynoecium (also called pistil). Ovules arise from meristematic tissue within the gynoecium. Upon fertilization, these ovules develop into seeds while the gynoecium turns into a fruit. Gene regulatory networks involving transcription factors and hormonal communication regulate ovule primordium initiation, spacing on the placenta, and development. Ovule number and gynoecium size are usually correlated and several genetic factors that impact these traits have been identified. Understanding and fine-tuning the gene regulatory networks influencing ovule number and pistil length open up strategies for crop yield improvement, which is pivotal in light of a rapidly growing world population. In this review, we present an overview of the current knowledge of the genes and hormones involved in determining ovule number and gynoecium size. We propose a model for the gene regulatory network that guides the developmental processes that determine seed yield.

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