RNAcentral: an international database of ncRNA sequences

Petrov, Anton I.; Kay, Simon; Gibson, Richard L.; Kulesha, Eugene; Staines, Daniel M.; Bruford, Elspeth A.; Wright, Mathew W.; Burge, Sarah; Finn, Robert D.; Kersey, Paul J.; Cochrane, Guy; Bateman, Alex; Griffiths-Jones, Sam; Harrow, Jennifer; Chan, Patricia P.; Lowe, Todd M.; Zwieb, Christian W; Wower, Jacek; Williams, Kelly P.; Hudson, Corey; Gutell, Robin R.; Clark, Michael B.; Dinger, Marcel E.; Quek, Xiu Cheng; Bujnicki, Janusz M.; Chua, Nam Hai; Li, Jun; Wang, Huan; Skogerbø, Geir; Zhao, Yi; Chen, Runsheng; Zhu, Weimin; Cole, James R.; Chai, Benli; Huang, Hsien Da; Huang, His Yuan; Cherry, J. Michael; Hatzigeorgiou, Artemis G.; Pruitt, Kim D.

doi:10.1093/nar/gku991

Cited by 107 publications

(67 citation statements)

References 24 publications

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“…Additionally, reads shorter than 16 nt, probably degradation products from longer coding and non-coding RNA species, were excluded from the data set before proceeding to alignment [4]. To avoid false-positive hits during miRNA analysis, reads that mapped to sequences from human rRNA, tRNA, snRNA and snoRNA (obtained from RNAcentral) were initially removed from the data set [38]. Remaining reads were then aligned to human miRNA sequences in the most recent version (21) of miRBase [39].…”

Section: Methodsmentioning

confidence: 99%

Evaluation of serum extracellular vesicle isolation methods for profiling miRNAs by next‐generation sequencing

Buschmann

Kirchner

Hermann

et al. 2018

J of Extracellular Vesicle

207

165

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Extracellular vesicles (EVs) are intercellular communicators with key functions in physiological and pathological processes and have recently garnered interest because of their diagnostic and therapeutic potential. The past decade has brought about the development and commercialization of a wide array of methods to isolate EVs from serum. Which subpopulations of EVs are captured strongly depends on the isolation method, which in turn determines how suitable resulting samples are for various downstream applications. To help clinicians and scientists choose the most appropriate approach for their experiments, isolation methods need to be comparatively characterized. Few attempts have been made to comprehensively analyse vesicular microRNAs (miRNAs) in patient biofluids for biomarker studies. To address this discrepancy, we set out to benchmark the performance of several isolation principles for serum EVs in healthy individuals and critically ill patients. Here, we compared five different methods of EV isolation in combination with two RNA extraction methods regarding their suitability for biomarker discovery-focused miRNA sequencing as well as biological characteristics of captured vesicles. Our findings reveal striking method-specific differences in both the properties of isolated vesicles and the ability of associated miRNAs to serve in biomarker research. While isolation by precipitation and membrane affinity was highly suitable for miRNA-based biomarker discovery, methods based on size-exclusion chromatography failed to separate patients from healthy volunteers. Isolated vesicles differed in size, quantity, purity and composition, indicating that each method captured distinctive populations of EVs as well as additional contaminants. Even though the focus of this work was on transcriptomic profiling of EV-miRNAs, our insights also apply to additional areas of research. We provide guidance for navigating the multitude of EV isolation methods available today and help researchers and clinicians make an informed choice about which strategy to use for experiments involving critically ill patients.

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Section: Methodsmentioning

confidence: 99%

Evaluation of serum extracellular vesicle isolation methods for profiling miRNAs by next‐generation sequencing

Buschmann

Kirchner

Hermann

et al. 2018

J of Extracellular Vesicle

207

165

View full text Add to dashboard Cite

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“…Other repositories seek to build larger consolidated lncRNA catalogues, including LNCipedia 99 which focuses on human, and NONCODE 100 which contains information from 16 species. Meanwhile, the RNAcentral database 101 contains 8.1 million RNA sequences, representing all major functional classes of non-coding RNAs from a selection of species; a key goal is to resolve the redundancy between the lncRNA datasets produced from different annotation groups.…”

Section: Functional Annotationmentioning

confidence: 99%

The state of play in higher eukaryote gene annotation

Mudge

Harrow

2016

Nat Rev Genet

Self Cite

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A genome sequence is worthless if it cannot be deciphered, therefore efforts to describe — or ‘annotate’ — genes began as soon as DNA sequences became available. Whereas early work focused on individual protein-coding genes, the modern genomic ocean is a complex maelstrom of alternative splicing, non-coding transcription and pseudogenes. Scientists — from clinicians to evolutionary biologists — need to navigate these waters, and this has led to the design of high-throughput, computationally driven annotation projects. The catalogues produced are key resources for genome exploration, especially as they become integrated with expression, epigenomic and variation datasets. Their creation, however, remains challenging.

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“…As a result, all these identified lncRNAs as well as their associated small proteins are incorporated into LncRNAWiki. Since this July, LncRNAWiki has become a member of RNAcentral (47), further facilitating data exchange and sharing between LncRNAWiki and other related databases.…”

Section: Science Wikismentioning

confidence: 99%

The BIG Data Center: from deposition to integration to translation

2016

Nucleic Acids Res

464

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Biological data are generated at unprecedentedly exponential rates, posing considerable challenges in big data deposition, integration and translation. The BIG Data Center, established at Beijing Institute of Genomics (BIG), Chinese Academy of Sciences, provides a suite of database resources, including (i) Genome Sequence Archive, a data repository specialized for archiving raw sequence reads, (ii) Gene Expression Nebulas, a data portal of gene expression profiles based entirely on RNA-Seq data, (iii) Genome Variation Map, a comprehensive collection of genome variations for featured species, (iv) Genome Warehouse, a centralized resource housing genome-scale data with particular focus on economically important animals and plants, (v) Methylation Bank, an integrated database of whole-genome single-base resolution methylomes and (vi) Science Wikis, a central access point for biological wikis developed for community annotations. The BIG Data Center is dedicated to constructing and maintaining biological databases through big data integration and value-added curation, conducting basic research to translate big data into big knowledge and providing freely open access to a variety of data resources in support of worldwide research activities in both academia and industry. All of these resources are publicly available and can be found at http://bigd.big.ac.cn.

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RNAcentral: an international database of ncRNA sequences

Cited by 107 publications

References 24 publications

Evaluation of serum extracellular vesicle isolation methods for profiling miRNAs by next‐generation sequencing

Evaluation of serum extracellular vesicle isolation methods for profiling miRNAs by next‐generation sequencing

The state of play in higher eukaryote gene annotation

The BIG Data Center: from deposition to integration to translation

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