lncRNome: a comprehensive knowledgebase of human long noncoding RNAs

Bhartiya, Deeksha; Pal, Koustav; Ghosh, Sourav; Kapoor, Shruti; Jalali, Saakshi; Panwar, Bharat; Jain, Sakshi; Sati, Satish; Sengupta, Shantanu; Sachidanandan, Chetana; Raghava, Gajendra P. S.; Sivasubbu, Sridhar; Scaria, Vinod

doi:10.1093/database/bat034

Cited by 133 publications

(72 citation statements)

References 42 publications

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“…Databases like lncRNAtor (Park et al 2014) and ncRNAimprint (Zhang et al 2010) store comprehensive information about lncRNAs. Furthermore, the PLncDB deals with the information related to plant lncRNAs (Jin et al 2013), and lncRNome database has similar information stored for human (Bhartiya et al 2013). The ncFANs web server is useful for functional annotation of lncRNAs (Liao et al 2011), whereas DIANA-LncBase hosts elaborated information for both predicted and experimentally verified miRNA-lncRNA interactions (Paraskevopoulou et al 2013).…”

Section: Lncrnas and Associated Databasesmentioning

confidence: 99%

DeepLNC, a long non-coding RNA prediction tool using deep neural network

Tripathi

Patel

Kumari

et al. 2016

Netw Model Anal Health Inform Bioinforma

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The significant role of long non-coding RNAs (lncRNAs) in various cellular functions, such as gene imprinting, immune response, embryonic pluripotency, tumorogenesis, and genetic regulations, has been widely studied and reported in recent years. Several experimental and computational methods involving genome-wide search and screenings of ncRNAs are being proposed utilizing sequence features-length, occurrence, and composition of bases with various limitations. The proposed classifier, Deep Neural Network (DNN) is fast and an accurate alternative for the identification of lncRNAs as compared to other existing classifiers. The information content stored in k-mer pattern has been used as a sole feature for the DNN classifier using manually annotated training datasets from LNCipedia and RefSeq database, obtaining accuracy of 98.07 %, sensitivity of 98.98 %, and specificity of 97.19 %, respectively, on test dataset. The k-mer information content generated on the basis of Shannon entropy function has resulted in improved classifier accuracy. This classification framework was also tested on known human genome dataset, and the framework has successfully identified known lncRNAs with 99 % accuracy rate. The said algorithm has been implemented as a web prediction tool, which is available on server interface http:// bioserver.iiita.ac.in/deeplnc.

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Section: Lncrnas and Associated Databasesmentioning

confidence: 99%

DeepLNC, a long non-coding RNA prediction tool using deep neural network

Tripathi

Patel

Kumari

et al. 2016

Netw Model Anal Health Inform Bioinforma

View full text Add to dashboard Cite

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“…Several databases have been developed to curate computationally predicted and experimentally veriied lncRNAs, such as LncRNAdb [194], GENCODE [137], lncRNAtor [7], lncRNome [195], NONCODE [135], lncRNAWiki [134], LncRNA2Function [143] and starBase v2.0 [196]. LncRNAdb was the irst lncRNA database [194] and its updated version (LncRNAdb v2.0) integrates lncRNAs reported in livestock species (catle, sheep, pig, horse and chicken) [131].…”

Section: Functional Inference Of Lncrnamentioning

confidence: 99%

Transcriptome Analysis of Non‐Coding RNAs in Livestock Species: Elucidating the Ambiguity

Ngoc¹,

Dudemaine²,

Fomenky³

et al. 2017

Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

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The recent remarkable development of transcriptomics technologies, especially next generation sequencing technologies, allows deeper exploration of the hidden landscapes of complex traits and creates great opportunities to improve livestock productivity and welfare. Non-coding RNAs (ncRNAs), RNA molecules that are not translated into proteins, are key transcriptional regulators of health and production traits, thus, transcriptomics analyses of ncRNAs are important for a beter understanding of the regulatory architecture of livestock phenotypes. In this chapter, we present an overview of common frameworks for generating and processing RNA sequence data to obtain ncRNA transcripts. Then, we review common approaches for analyzing ncRNA transcriptome data and present current state of the art methods for identiication of ncRNAs and functional inference of identiied ncRNAs, with emphasis on tools for livestock species. We also discuss future challenges and perspectives for ncRNA transcriptome data analysis in livestock species.

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“…A number of distinct examples of each of these interactions and their biological regulatory outcomes have been reviewed in the literature. 48,49 A number of recent papers also suggest that a subset of lncRNAs could interact with smaller RNAs, including miRNAs, and modulate their regulatory effect. 50,51 Our group has validated one such regulatory interaction between a conserved pair of miRNA and lncRNA in zebrafish.…”

Section: Biological Function and Disease Associationsmentioning

confidence: 99%

“…Recently, our group has analyzed functional elements in lncRNAs, characterized by experimental datasets of RNA-protein, RNA-RNA interaction sites, and small RNA processing sites. 49,54 Our analysis hints that the functional elements in lncRNA are characterized by a lower variation frequency, almost comparable to that of protein-coding genes, suggesting that a comparison of sequence divergence based on whole lncRNA transcripts may be misleading. Nevertheless, other studies have also identified a small number of lncRNAs with recognizable degrees of sequence conservation between vertebrates.…”

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confidence: 93%

Short Stories on Zebrafish Long Noncoding RNAs

et al. 2014

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The recent re-annotation of the transcriptome of human and other model organisms, using next-generation sequencing approaches, has unravelled a hitherto unknown repertoire of transcripts that do not have a potential to code for proteins. These transcripts have been largely classified into an amorphous class popularly known as long noncoding RNAs (lncRNA). This discovery of lncRNAs in human and other model systems have added a new layer to the understanding of gene regulation at the transcriptional and post-transcriptional levels. In recent years, three independent studies have discovered a number of lncRNAs expressed in different stages of zebrafish development and adult tissues using a high-throughput RNA sequencing approach, significantly adding to the repertoire of genes known in zebrafish. A subset of these transcripts also shows distinct and specific spatiotemporal patterns of gene expression, pointing to a tight regulatory control and potential functional roles in development, organogenesis, and/ or homeostasis. This review provides an overview of the lncRNAs in zebrafish and discusses how their discovery could provide new insights into understanding biology, explaining mutant phenotypes, and helping in potentially modeling disease processes.

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lncRNome: a comprehensive knowledgebase of human long noncoding RNAs

Cited by 133 publications

References 42 publications

DeepLNC, a long non-coding RNA prediction tool using deep neural network

DeepLNC, a long non-coding RNA prediction tool using deep neural network

Transcriptome Analysis of Non‐Coding RNAs in Livestock Species: Elucidating the Ambiguity

Short Stories on Zebrafish Long Noncoding RNAs

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