RNASeqMetaDB: a database and web server for navigating metadata of publicly available mouse RNA-Seq datasets

Guo, Zhengyu; Tzvetkova, Boriana; Bassik, Jennifer M.; Bodziak, Tara; Wojnar, Brianna M.; Qiao, Weiyang; Obaida, A.; Nelson, Sacha B.; Hu, Bo; Yu, Peng

doi:10.1093/bioinformatics/btv503

Cited by 15 publications

(17 citation statements)

References 16 publications

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“…For example, around one million series studies are publicly available in GEO. Due to the unstructured nature of the metadata associated with public data, manual curation is required [3][4][5][6][7] , a step that is essential for collecting large-scale gene expression data.…”

Section: And Gene Expressionmentioning

confidence: 99%

A data mining paradigm for identifying key factors in biological processes using gene expression data

Zheng

Uchiyama

et al. 2018

Preprint

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A large volume of biological data is being generated for studying mechanisms of various biological processes. These precious data enable large-scale computational analyses to gain biological insights. However, it remains a challenge to mine the data efficiently for knowledge discovery. The heterogeneity of these data makes it difficult to consistently integrate them, slowing down the process of biological discovery. We introduce a data processing paradigm to identify key factors in biological processes via systematic collection of gene expression datasets, primary analysis of data, and evaluation of consistent signals. To demonstrate its effectiveness, our paradigm was applied to epidermal development and identified many genes that play a potential role in this process. Besides the known epidermal development genes, a substantial proportion of the identified genes are still not supported by gain-or loss-of-function studies, yielding many novel genes for future studies. Among them, we selected a top gene for loss-of-function experimental validation and confirmed its function in epidermal differentiation, proving the ability of this paradigm to identify new factors in biological processes. In addition, this paradigm revealed many key genes in cold-induced thermogenesis using data from cold-challenged tissues, demonstrating its generalizability. This paradigm can lead to fruitful results for studying molecular mechanisms in an era of explosive accumulation of publicly available biological data.

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Section: And Gene Expressionmentioning

confidence: 99%

A data mining paradigm for identifying key factors in biological processes using gene expression data

Zheng

Uchiyama

et al. 2018

Preprint

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“…As a popular method for transcriptome analysis, RNA-sequencing (RNA-Seq) 4 has enabled genome-wide analyses of RNA molecules at a high sequencing depth with high accuracy. It has been used successfully on many mouse models 5,6 , and thousands of RNA-Seq datasets have been generated and released to the public. This massive amount of biological data brings great opportunity for generating prominent biological hypotheses 7,8 .…”

Section: Introductionmentioning

confidence: 99%

Integrated analysis of a compendium of RNA-Seq datasets for splicing factors

Jin

Deng

et al. 2020

Preprint

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A vast amount of public RNA-sequencing datasets have been generated and used widely to study transcriptome mechanisms. These data offer precious opportunity for advancing biological research in transcriptome studies such as alternative splicing. We report the first large-scale integrated analysis of RNA-Seq data of splicing factors for systematically identifying key factors in diseases and biological processes. We analyzed 1,321 RNA-Seq libraries of various mouse tissues and cell lines, comprising more than 6.6 TB sequences from 75 independent studies that experimentally manipulated 56 splicing factors. Using these data, RNA splicing signatures and gene expression signatures were computed, and signature comparison analysis identified a list of key splicing factors in Rett syndrome and cold-induced thermogenesis. We show that coldinduced RNA-binding proteins rescue the neurite outgrowth defects in Rett syndrome using neuronal morphology analysis, and we also reveal that SRSF1 and PTBP1 are required for energy expenditure in adipocytes using metabolic flux analysis. Our study provides an integrated analysis for identifying key factors in diseases and biological processes and highlights the importance of public data resources for identifying hypotheses for experimental testing.

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“…For example, microarray and RNA-Seq datasets have been curated for the downstream analyses in Expression Atlas (4) and in epidermal development (5). We previously launched two databases, RNASeqMetaDB (6) and SFMetaDB (7), to facilitate access to the metadata of publicly available mouse RNA-Seq datasets with perturbed disease-related genes and splicing factors, respectively. Here, we present a new database, RBPMetaDB, for the metadata of RNA-Seq datasets with perturbed RNA-binding proteins (RBPs).…”

Section: Introductionmentioning

confidence: 99%

“…Such a deficiency makes it difficult to identify useful datasets with high precision and recall, which limits the wide use of the datasets. 6 To address this challenge, we worked to curate RNA-Seq datasets from GEO and ArrayExpress with one or more RBPs being perturbed, e.g., by knockout, knock-down, or overexpression. Important dataset annotations such as genotypes and PubMed references were manually curated to ensure high accuracy.…”

Section: Introductionmentioning

confidence: 99%

RBPMetaDB: A comprehensive annotation of mouse RNA-Seq datasets with perturbations of RNA-binding proteins

Deng

Vieira

et al. 2018

Preprint

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RNA-binding proteins may play a critical role in gene regulation in various diseases or biological processes by controlling post-transcriptional events such as polyadenylation, splicing, and mRNA stabilization via binding activities to RNA molecules. Due to the importance of RNA-binding proteins in gene regulation, a great number of studies have been conducted, resulting in a large amount of RNA-Seq datasets. However, these datasets usually do not have structured organization of metadata, which limits their potentially wide use. To bridge this gap, the metadata of a comprehensive set of publicly available mouse RNA-Seq datasets with perturbed RNA-binding proteins were collected and integrated into a database called RBPMetaDB. This database contains 278 mouse RNA-Seq datasets for a comprehensive list of 163 RNA-binding proteins. These RNAbinding proteins account for only ~10% of all known RNA-binding proteins annotated in Gene Ontology, indicating that most are still unexplored using highthroughput sequencing. This negative information provides a great pool of candidate RNA-binding proteins for biologists to conduct future experimental studies. In addition, we found that DNA-binding activities are significantly enriched among RNA-binding proteins in RBPMetaDB, suggesting that prior studies of these DNA-and RNA-binding factors focus more on DNA-binding activities instead of RNA-binding activities. This result reveals the opportunity to efficiently reuse these data for investigation of the roles of their RNA-binding activities. A web application has also been implemented to enable easy access and wide use of RBPMetaDB. It is expected that RBPMetaDB will be a great 3 resource for improving understanding of the biological roles of RNA-binding proteins.

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RNASeqMetaDB: a database and web server for navigating metadata of publicly available mouse RNA-Seq datasets

Abstract: Freely available on the web at http://rnaseqmetadb.ece.tamu.edu.

Cited by 15 publications

References 16 publications

A data mining paradigm for identifying key factors in biological processes using gene expression data

A data mining paradigm for identifying key factors in biological processes using gene expression data

Integrated analysis of a compendium of RNA-Seq datasets for splicing factors

RBPMetaDB: A comprehensive annotation of mouse RNA-Seq datasets with perturbations of RNA-binding proteins

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