cncRNAdb: a manually curated resource of experimentally supported RNAs with both protein-coding and noncoding function

Huang, Yan; Wang, Jing; Zhao, Yue; Wang, Huafeng; Liu, Tianyuan; Li, Yuhe; Cui, Tianyu; Li, Weiyi; Feng, Yi; Luo, Jiaxin; Gong, Jiaqi; Lin, Ning; Zhang, Yong; Wang, Dong; Zhang, Yang

doi:10.1093/nar/gkaa791

Cited by 42 publications

(27 citation statements)

References 52 publications

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“…To check if the candidate list of misannotated transcripts overlap with already reported misannoated ncRNAs, we find the overlap with the cncRNA database. The cncRNA database provides a manually curated list of experimentally validated coding lncRNAs [16]. Figure 3b shows the overlap between the list of misannotated lncRNAs generated by our deep learning models and the cncRNA database [16].…”

Section: Resultsmentioning

confidence: 99%

“…The cncRNA database provides a manually curated list of experimentally validated coding lncRNAs [16]. Figure 3b shows the overlap between the list of misannotated lncRNAs generated by our deep learning models and the cncRNA database [16]. There are 225 common misannotated lncRNAs, this overlap is highly significant (hyper-geometric test, p-value (1 e -6)).…”

Section: Resultsmentioning

confidence: 99%

“…We downloaded data from the cncRNAdb [16], a resource that provides a manually curated list of experimentally validated ncRNAs found to be coding. We filtered data to get lncRNAs found to be coding in Homo sapiens and compared the list to the misannotated lncRNA candidates generated from the deep learning models.…”

Section: Methodsmentioning

confidence: 99%

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Detecting Misannotated Long Non-coding RNAs with Training Dynamics of Deep Sequence Classification

Nabi

Adebali

Taştan

2020

Preprint

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Long non-coding RNAs (lncRNAs) are the largest class of non-coding RNAs (ncRNAs). However, recent experimental evidence has shown that some lncRNAs contain small open reading frames (sORFs) that are translated into functional micropeptides. Current methods to detect misannotated lncRNAs rely on ribosome-profiling (ribo-seq) experiments, which are expensive and cell-type dependent. In addition, while very accurate machine learning models have been trained to distinguish between coding and non-coding sequences, little attention has been paid to the increasing evidence about the incorrect ground-truth labels of some lncRNAs in the underlying training datasets. We present a framework that leverages deep learning models' training dynamics to determine whether a given lncRNA transcript is misannotated. Our models achieve AUC scores >91 and AUPR >93 in classifying non-coding vs. coding sequences while allowing us to identify possible misannotated lncRNAs present in the dataset. Our results overlap significantly with a set of experimentally validated misannotated lncRNAs as well as with coding sORFs within lncRNAs found by a ribo-seq dataset. The general framework applied here offers promising potential for use in curating datasets used for training coding potential predictors and assisting experimental efforts in characterizing the hidden proteome encoded by misannotated lncRNAs. Source code is available at https://github.com/nabiafshan/DetectingMisannotatedLncRNAs.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Detecting Misannotated Long Non-coding RNAs with Training Dynamics of Deep Sequence Classification

Nabi

Adebali

Taştan

2020

Preprint

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“…We collected the lncRNAs encoded peptides from four manually curated databases, cncRNAdb ( 29 ), EVlncRNAs ( 30 ), ncEP ( 31 ) and FuncPEP ( 32 ). In total, 1185, 18, 13 and 41 lncRNAs that can encode peptides were obtained from four databases.…”

Section: Methodsmentioning

confidence: 99%

TransLnc: a comprehensive resource for translatable lncRNAs extends immunopeptidome

Chang

Cai

et al. 2021

Nucleic Acids Research

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LncRNAs are not only well-known as non-coding elements, but also serve as templates for peptide translation, playing important roles in fundamental cellular processes and diseases. Here, we describe a database, TransLnc (http://bio-bigdata.hrbmu.edu.cn/TransLnc/), which aims to provide comprehensive experimentally supported and predicted lncRNA peptides in multiple species. TransLnc currently documents approximate 583 840 peptides encoded by 33 094 lncRNAs. Six types of direct and indirect evidences supporting the coding potential of lncRNAs were integrated, and 65.28% peptides entries were with at least one type of evidence. Considering the strong tissue-specific expression of lncRNAs, TransLnc allows users to access lncRNA peptides in any of the 34 tissues involved in. In addition, both the unique characteristic and homology relationship were also predicted and provided. Importantly, TransLnc provides computationally predicted tumour neoantigens from peptides encoded by lncRNAs, which would provide novel insights into cancer immunotherapy. There were 220 791 and 237 915 candidate neoantigens binding by major histocompatibility complex (MHC) class I or II molecules, respectively. Several flexible tools were developed to aid retrieve and analyse, particularly lncRNAs tissue expression patterns, clinical relevance across cancer types. TransLnc will serve as a valuable resource for investigating the translation capacity of lncRNAs and greatly extends the cancer immunopeptidome.

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“…Long non-coding RNA (lncRNA) is a non-coding RNA varying in length from 200 nucleotides to 100 kilobases, which has been involved in a wide range of biological processes from regulation transcription to immune responses. [4][5][6][7] Many researchers have discovered that lncRNA may play an important role in regulated genes expression and the progression of endometriosis. [8][9][10] Huang et al reported that lncRNA UCA1 was downregulated in endometriosis, 9 and Zhu et al showed an effect of lncRNA BANCR on endometriosis through the ERK/MAPK pathway.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Potential Key IncRNAs with Endometriosis by Construction of a ceRNA Network

Gu¹,

Meng²,

Meng³

et al. 2021

IJGM

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The etiology and pathophysiology of endometriosis remain unclear. Current research indicates long noncoding RNA (lncRNA) may play an important role in the pathogenesis and development of endometriosis. However, the molecular mechanism of lncRNA in endometriosis is far from clear. Patients and Methods: The lncRNA and mRNA expression of 8 patients with ovarian endometriosis were determined by high-throughput RNA sequencing (8 ectopic endometria samples vs 8 eutopic endometria samples), and miRNA expression profiles were obtained from our previous study. Then a lncRNA-associated competing endogenous RNA (ceRNA) network was constructed by combining the regulatory interaction and negative co-expression interaction between the differentially expressed lncRNAs/mRNAs and miRNAs by different rules. Results: The constructed lncRNA-related ceRNA network was composed of two separate networks, network 1 including 14,137 dysregulated lncRNA-mRNA interactions, referring to 242 lncRNAs, 55 miRNAs and 1600 mRNAs, network 2 including 4459 dysregulated lncRNA-mRNA interactions, referring to 111 lncRNAs, 39 miRNAs and 1151 mRNAs. The top six hub lncRNAs (LINC01140, MSC-AS1, HAGLR, CKMT2-AS1, JAKMIP2-AS1, AL365361.1) in the significant ternary relationship of mRNA-miRNA-lncRNA in network 1, and the top six hub lncRNAs (PAX8-AS1, MIR17HC, PART1, HOXA-AS3, PLAC4, LINC00511) in the significant ternary relationship of mRNA-miRNA-lncRNA in network 2 were selected. Functional enrichment analysis of these lncRNA-related mRNAs indicated that the lncRNAs in network 1 mainly take part in positive regulation of phagocytosis, myeloid leukocyte activation, and tissue remodeling, while the lncRNAs in network 2 mainly take part in negative regulation of cell proliferation, blood vessel development and regulation of epithelial cell differentiation, which is consistent with the results obtained from the different rules to construct the networks. Conclusion: lncRNA-related ceRNA network analysis recognized key lncRNAs related to the development of endometriosis.

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cncRNAdb: a manually curated resource of experimentally supported RNAs with both protein-coding and noncoding function

Cited by 42 publications

References 52 publications

Detecting Misannotated Long Non-coding RNAs with Training Dynamics of Deep Sequence Classification

Detecting Misannotated Long Non-coding RNAs with Training Dynamics of Deep Sequence Classification

TransLnc: a comprehensive resource for translatable lncRNAs extends immunopeptidome

Exploring the Potential Key IncRNAs with Endometriosis by Construction of a ceRNA Network

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