LncCeRBase: a database of experimentally validated human competing endogenous long non-coding RNAs

Zhao

et al. 2021

Nucleic Acids Research

LncRNAWiki, a knowledgebase of human long non-coding RNAs (lncRNAs), has been rapidly expanded by incorporating more experimentally validated lncRNAs. Since it was built based on MediaWiki as its database system, it fails to manage data in a structured way and is ineffective to support systematic exploration of lncRNAs. Here we present LncRNAWiki 2.0 (https://ngdc.cncb.ac.cn/lncrnawiki), which is significantly improved with enhanced database system and curation model. In LncRNAWiki 2.0, all contents are organized in a structured manner powered by MySQL/Java and curators are able to submit/edit annotations based on the curation model that includes a wider range of annotation items. Moreover, it is equipped with popular online tools to help users identify lncRNAs with potentially important functions, and provides more user-friendly web interfaces to facilitate data curation, retrieval and visualization. Consequently, LncRNAWiki 2.0 incorporates a total of 2512 lncRNAs and 106 242 associations for disease, function, drug, interacting partner, molecular signature, experimental sample, CRISPR design, etc., thus providing a comprehensive and up-to-date resource of functionally annotated lncRNAs in human.

Section: Data Integration and Curationmentioning

confidence: 99%

“…LncRNADisease ( 12 ), LncR2metasta ( 13 ) and Lnc2Cancer ( 14 )), interaction (e.g. NPInter ( 15 ), LncCeRBase ( 16 ), LncRNA2Target ( 17 ), LncACTdb ( 18 ) and LncTarD ( 19 )), biological function (e.g. LncTarD ( 19 ) and LncR2metasta ( 13 )), clinical information (e.g.…”

Section: Introductionmentioning

confidence: 99%

LncRNAWiki 2.0: a knowledgebase of human long non-coding RNAs with enhanced curation model and database system

Zhao

et al. 2021

Nucleic Acids Research

BioMed Research International

“…To gain insight into the interactions among long noncoding RNAs, miRNAs, and mRNAs in IPMN, various publicly available databases and web tools were explored. We used LncCeRBase [12], RNAInter: RNA Interactome Database [13], RAID v2.0 RNA Association Interaction Database [14], miRcode [15], miRTarBase [16], ENCORI StarBase/starBase v2.0 [17], and LncRNA2Target v2.0 [18] to list down the interactions in Homo sapiens, relevant to the DE-lncRNAs we had identified. Finally, the interacting miRNAs and mRNAs from this list were compared with DE-miRs and DE-Target genes identified previously.…”

Section: De-mirs Selectionmentioning

confidence: 99%

Bioinformatic Analysis and Integration of Transcriptome and Proteome Results Identify Key Coding and Noncoding Genes Predicting Malignancy in Intraductal Papillary Mucinous Neoplasms of the Pancreas

Saha

Chhatriya

Pramanick

et al. 2021

Background. Intraductal papillary mucinous neoplasms (IPMNs) are precursor lesions of pancreatic ductal adenocarcinoma (PDAC). IPMNs are generally associated with high risk of developing malignancy and therefore need to be diagnosed and assessed accurately, once detected. Existing diagnostic methods are inadequate, and identification of efficient biomarker capable of detecting high-risk IPMNs is necessitated. Moreover, the mechanism of development of malignancy in IPMNs is also elusive. Methods. Gene expression meta-analysis conducted using 12 low-risk IPMN and 23 high-risk IPMN tissue samples. We have also listed all the altered miRNAs and long noncoding RNAs (lncRNAs), identified their target genes, and performed pathway analysis. We further enlisted cyst fluid proteins detected to be altered in high-risk or malignant IPMNs and compared them with fraction of differentially expressed genes secreted into cyst fluid. Results. Our meta-analysis identified 270 upregulated and 161 downregulated genes characteristically altered in high-risk IPMNs. We further identified 61 miRNAs and 14 lncRNAs and their target genes and key pathways contributing towards understanding of the gene regulation during the progression of the disease. Most importantly, we have detected 12 genes altered significantly both in cystic lesions and cyst fluid. Conclusion. Our study reports, for the first time, a meta-analysis identifying key changes in gene expression between low-risk and high-risk IPMNs and also explains the regulatory aspect through construction of a miRNA-lncRNA-mRNA interaction network. The 12-gene-signature could function as potential biomarker in cyst fluid for detection of IPMN with a high risk of developing malignancy.

“…lncRNAs are called for competing endogeneous RNAs (ceRNAs) when they bind to miRNA and regulate their functions. In LncCeRBase [ 30 ] there are details for ceRNAs, and lncRNAs containing genomic variants that disturb ceRNA network regulation [ 31 ]. All kinds of interactions of lncRNAs are available in LIVE [ 32 ].…”

Section: Databases For Rna Variationsmentioning

confidence: 99%

Systematics for types and effects of RNA variations

Vihinen

2020

RNA Biology

Systematics is described for annotation of variations in RNA molecules. The conceptual framework is part of Variation Ontology (VariO) and facilitates depiction of types of variations, their functional and structural effects and other consequences in any RNA molecule in any organism. There are more than 150 RNA related VariO terms in seven levels, which can be further combined to generate even more complicated and detailed annotations. The terms are described together with examples, usually for variations and effects in human and in diseases. RNA variation type has two subcategories: variation classification and origin with subterms. Altogether six terms are available for function description. Several terms are available for affected RNA properties. The ontology contains also terms for structural description for affected RNA type, post-transcriptional RNA modifications, secondary and tertiary structure effects and RNA sugar variations. Together with the DNA and protein concepts and annotations, RNA terms allow comprehensive description of variations of genetic and non-genetic origin at all possible levels. The VariO annotations are readable both for humans and computer programs for advanced data integration and mining.