The Genome Sequence Archive Family: Toward Explosive Data Growth and Diverse Data Types

Chen, Tingting; Xu, Chen; Zhang, Sisi; Zhu, Junwei; Tang, Bixia; Wang, Anke; Dong, Lili; Zhang, Zhewen; Yu, Chao; Sun, Yanling; Chi, Lianjiang; Chen, Huanxin; Zhai, Shuang; Sun, Yuan; Li, Lan; Zhang, Xin; Xiao, Jingfa; Bào, Yīmíng; Wang, Yanqing; Zhang, Zhang; Zhao, Wenming

doi:10.1016/j.gpb.2021.08.001

Cited by 839 publications

(469 citation statements)

References 18 publications

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“…The datasets presented in the study are deposited in the Genome Sequence Archive (Chen et al, 2021) in National Genomics Data Center (Members and Partners, 2021), China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA005251) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa.…”

Section: Discussionmentioning

confidence: 99%

Characteristics of Fecal Microbiota and Machine Learning Strategy for Fecal Invasive Biomarkers in Pediatric Inflammatory Bowel Disease

Wang

Yuan

et al. 2021

Front. Cell. Infect. Microbiol.

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BackgroundEarly diagnosis and treatment of pediatric Inflammatory bowel disease (PIBD) is challenging due to the complexity of the disease and lack of disease specific biomarkers. The novel machine learning (ML) technique may be a useful tool to provide a new route for the identification of early biomarkers for the diagnosis of PIBD.MethodsIn total, 66 treatment naive PIBD patients and 27 healthy controls were enrolled as an exploration cohort. Fecal microbiome profiling using 16S rRNA gene sequencing was performed. The correlation between microbiota and inflammatory and nutritional markers was evaluated using Spearman’s correlation. A random forest model was used to set up an ML approach for the diagnosis of PIBD using 1902 markers. A validation cohort including 14 PIBD and 48 irritable bowel syndrome (IBS) was enrolled to further evaluate the sensitivity and accuracy of the model.ResultCompared with healthy subjects, PIBD patients showed a significantly lower diversity of the gut microbiome. The increased Escherichia-Shigella and Enterococcus were positively correlated with inflammatory markers and negatively correlated with nutrition markers, which indicated a more severe disease. A diagnostic ML model was successfully set up for differential diagnosis of PIBD integrating the top 11 OTUs. This diagnostic model showed outstanding performance at differentiating IBD from IBS in an independent validation cohort.ConclusionThe diagnosis penal based on the ML of the gut microbiome may be a favorable tool for the precise diagnosis and treatment of PIBD. A study of the relationship between disease status and the microbiome was an effective way to clarify the pathogenesis of PIBD.

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Section: Discussionmentioning

confidence: 99%

Characteristics of Fecal Microbiota and Machine Learning Strategy for Fecal Invasive Biomarkers in Pediatric Inflammatory Bowel Disease

Wang

Yuan

et al. 2021

Front. Cell. Infect. Microbiol.

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“…CircleBase comprises 601 036 eccDNAs (candidates larger than 50M were removed) gleaned from 13 published papers on PubMed ( https://pubmed.ncbi.nlm.nih.gov/ ) and includes the following information: (i) chromosomal localizations of eccDNAs based on reference genomes hg19 and hg38; (ii) conditions or treatments; (iii) sample types; (iv) sequencing library types and (v) validation strategies. The eccDNA localizations were collected from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/ ) ( 16 , 17 ) and Genome Sequence Archive (GSA, https://ngdc.cncb.ac.cn/gsa/ ) ( 18 ).…”

Section: Methodsmentioning

confidence: 99%

CircleBase: an integrated resource and analysis platform for human eccDNAs

Zhao

Shi

Ruan

et al. 2021

Nucleic Acids Research

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Rapid advances in high-throughput sequencing technologies have led to the discovery of thousands of extrachromosomal circular DNAs (eccDNAs) in the human genome. Loss-of-function experiments are difficult to conduct on circular and linear chromosomes, as they usually overlap. Hence, it is challenging to interpret the molecular functions of eccDNAs. Here, we present CircleBase (http://circlebase.maolab.org), an integrated resource and analysis platform used to curate and interpret eccDNAs in multiple cell types. CircleBase identifies putative functional eccDNAs by incorporating sequencing datasets, computational predictions, and manual annotations. It classifies them into six sections including targeting genes, epigenetic regulations, regulatory elements, chromatin accessibility, chromatin interactions, and genetic variants. The eccDNA targeting and regulatory networks are displayed by informative visualization tools and then prioritized. Functional enrichment analyses revealed that the top-ranked cancer cell eccDNAs were enriched in oncogenic pathways such as the Ras and PI3K-Akt signaling pathways. In contrast, eccDNAs from healthy individuals were not significantly enriched. CircleBase provides a user-friendly interface for searching, browsing, and analyzing eccDNAs in various cell/tissue types. Thus, it is useful to screen for potential functional eccDNAs and interpret their molecular mechanisms in human cancers and other diseases.

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“…A number of high-throughput RNA-seq projects and their associated datasets were collected from several public raw sequencing databases, including Genome Sequence Archive (GSA, https://ngdc.cncb.ac.cn/gsa/ ) ( 28 , 29 ), Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra/ ) ( 30 ), European Nucleotide Archive (ENA, https://www.ebi.ac.uk/ena ) ( 31 ) and DDBJ Sequence Read Archive (DRA, https://ddbj.nig.ac.jp/DRASearch/ ) ( 32 ). Only the datasets with median mapping rates ≥70% for bulk RNA-seq and ≥40% for scRNA-seq were kept for further processing.…”

Section: Methodsmentioning

confidence: 99%

Gene Expression Nebulas (GEN): a comprehensive data portal integrating transcriptomic profiles across multiple species at both bulk and single-cell levels

Zhang

Zou

Zhu

et al. 2021

Nucleic Acids Research

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Transcriptomic profiling is critical to uncovering functional elements from transcriptional and post-transcriptional aspects. Here, we present Gene Expression Nebulas (GEN, https://ngdc.cncb.ac.cn/gen/), an open-access data portal integrating transcriptomic profiles under various biological contexts. GEN features a curated collection of high-quality bulk and single-cell RNA sequencing datasets by using standardized data processing pipelines and a structured curation model. Currently, GEN houses a large number of gene expression profiles from 323 datasets (157 bulk and 166 single-cell), covering 50 500 samples and 15 540 169 cells across 30 species, which are further categorized into six biological contexts. Moreover, GEN integrates a full range of transcriptomic profiles on expression, RNA editing and alternative splicing for 10 bulk datasets, providing opportunities for users to conduct integrative analysis at both transcriptional and post-transcriptional levels. In addition, GEN provides abundant gene annotations based on value-added curation of transcriptomic profiles and delivers online services for data analysis and visualization. Collectively, GEN presents a comprehensive collection of transcriptomic profiles across multiple species, thus serving as a fundamental resource for better understanding genetic regulatory architecture and functional mechanisms from tissues to cells.

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The Genome Sequence Archive Family: Toward Explosive Data Growth and Diverse Data Types

Cited by 839 publications

References 18 publications

Characteristics of Fecal Microbiota and Machine Learning Strategy for Fecal Invasive Biomarkers in Pediatric Inflammatory Bowel Disease

Characteristics of Fecal Microbiota and Machine Learning Strategy for Fecal Invasive Biomarkers in Pediatric Inflammatory Bowel Disease

CircleBase: an integrated resource and analysis platform for human eccDNAs

Gene Expression Nebulas (GEN): a comprehensive data portal integrating transcriptomic profiles across multiple species at both bulk and single-cell levels

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