lncRNAtor: a comprehensive resource for functional investigation of long non-coding RNAs

Park, Charny; Yu, Namhee; Choi, Ikjung; Kim, Wankyu; Lee, Sanghyuk

doi:10.1093/bioinformatics/btu325

Cited by 119 publications

(80 citation statements)

References 26 publications

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“…The lncRNAs that were successfully validated by means of quantitative polymerase chain reaction analysis were used as inputs for the lncRNA functional analysis tool (lncRNAtor version 1.0; Ewha Research Center for Systems Biology, Seoul, Korea). This tool allows functional investigation of lncRNAs by using gene expression data from 208 RNA sequencing studies (4995 samples) collected from genome databases (Gene Expression Omnibus, Encyclopedia of DNA Elements, and the Cancer Genome Atlas ) (28). Software (Ingenuity Pathway Analysis; Qiagen, Redwood City, Calif) was used for lncRNA pathway enrichment analysis.…”

Section: Discussionmentioning

confidence: 99%

Breast Cancer: Radiogenomic Biomarker Reveals Associations among Dynamic Contrast-enhanced MR Imaging, Long Noncoding RNA, and Metastasis

Yamamoto¹,

Han²,

Kim³

et al. 2015

Radiology

111

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Section: Discussionmentioning

confidence: 99%

Breast Cancer: Radiogenomic Biomarker Reveals Associations among Dynamic Contrast-enhanced MR Imaging, Long Noncoding RNA, and Metastasis

Yamamoto¹,

Han²,

Kim³

et al. 2015

Radiology

111

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“…LncRNA Map, Starbase v2.0, and LncRNAtor give insights into the potential regulatory roles of human lncRNAs and their interaction with miRNAs, as well as sRNAs (LncRNA Map), and proteins (Starbase v2.0 and LncRNAtor). In addition, LncRNAtor provides information on coexpression between mRNAs and lncRNAs in various tissues (Chan et al 2014;Li et al 2014;Park et al 2014). Moreover, an updated version of the Noncode database is now available as Noncode v4.0 (Xie et al 2014).…”

Section: Discussionmentioning

confidence: 99%

Databases for lncRNAs: a comparative evaluation of emerging tools

Fritah

Niclou

Azuaje

2014

RNA

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The vast majority of the human transcriptome does not code for proteins. Advances in transcriptome arrays and deep sequencing are giving rise to a fast accumulation of large data sets, particularly of long noncoding RNAs (lncRNAs). Although it is clear that individual lncRNAs may play important and diverse biological roles, there is a large gap between the number of existing lncRNAs and their known relation to molecular/cellular function. This and related information have recently been gathered in several databases dedicated to lncRNA research. Here, we review the content of general and more specialized databases on lncRNAs. We evaluate these resources in terms of the quality of annotations, the reporting of validated or predicted molecular associations, and their integration with other resources and computational analysis tools. We illustrate our findings using known and novel cancer-related lncRNAs. Finally, we discuss limitations and highlight potential future directions for these databases to help delineating functions associated with lncRNAs.

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“…The LncRNADisease database stores more than 1000 lncRNAs and associated disease information . 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).…”

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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lncRNAtor: a comprehensive resource for functional investigation of long non-coding RNAs

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 119 publications

References 26 publications

Breast Cancer: Radiogenomic Biomarker Reveals Associations among Dynamic Contrast-enhanced MR Imaging, Long Noncoding RNA, and Metastasis

Breast Cancer: Radiogenomic Biomarker Reveals Associations among Dynamic Contrast-enhanced MR Imaging, Long Noncoding RNA, and Metastasis

Databases for lncRNAs: a comparative evaluation of emerging tools

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

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