- deepBase v2.0: identification, expression, evolution and function of small RNAs, LncRNAs and circular RNAs from deep-sequencing data. [PMID: 26590255]
Ling-Ling Zheng, Jun-Hao Li, Jie Wu, Wen-Ju Sun, Shun Liu, Ze-Lin Wang, Hui Zhou, Jian-Hua Yang, Liang-Hu Qu
Nucleic acids research 2016:44(D1)
Citation (to be updated)
Abstract: Small non-coding RNAs (e.g. miRNAs) and long non-coding RNAs (e.g. lincRNAs and circRNAs) are emerging as key regulators of various cellular processes. However, only a very small fraction of these enigmatic RNAs have been well functionally characterized. In this study, we describe deepBase v2.0 (http://biocenter.sysu.edu.cn/deepBase/), an updated platform, to decode evolution, expression patterns and functions of diverse ncRNAs across 19 species. deepBase v2.0 has been updated to provide the most comprehensive collection of ncRNA-derived small RNAs generated from 588 sRNA-Seq datasets. Moreover, we developed a pipeline named lncSeeker to identify 176 680 high-confidence lncRNAs from 14 species. Temporal and spatial expression patterns of various ncRNAs were profiled. We identified approximately 24 280 primate-specific, 5193 rodent-specific lncRNAs, and 55 highly conserved lncRNA orthologs between human and zebrafish. We annotated 14 867 human circRNAs, 1260 of which are orthologous to mouse circRNAs. By combining expression profiles and functional genomic annotations, we developed lncFunction web-server to predict the function of lncRNAs based on protein-lncRNA co-expression networks. This study is expected to provide considerable resources to facilitate future experimental studies and to uncover ncRNA functions. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.
- DeepBase: annotation and discovery of microRNAs and other noncoding RNAs from deep-sequencing data. [PMID: 22144203]
Jian-Hua Yang, Liang-Hu Qu
Methods in molecular biology (Clifton, N.J.) 2012:822
8 Citations (Google Scholar as of 2016-01-29)
Abstract: Recent advances in high-throughput deep-sequencing technology have produced large numbers of short and long RNA sequences and enabled the detection and profiling of known and novel microRNAs (miRNAs) and other noncoding RNAs (ncRNAs) at unprecedented sensitivity and depth. In this chapter, we describe the use of deepBase, a database that we have developed to integrate all public deep-sequencing data and to facilitate the comprehensive annotation and discovery of miRNAs and other ncRNAs from these data. deepBase provides an integrative, interactive, and versatile web graphical interface to evaluate miRBase-annotated miRNA genes and other known ncRNAs, explores the expression patterns of miRNAs and other ncRNAs, and discovers novel miRNAs and other ncRNAs from deep-sequencing data. deepBase also provides a deepView genome browser to comparatively analyze these data at multiple levels. deepBase is available at http://deepbase.sysu.edu.cn/.
- deepBase: a database for deeply annotating and mining deep sequencing data. [PMID: 19966272]
Jian-Hua Yang, Peng Shao, Hui Zhou, Yue-Qin Chen, Liang-Hu Qu
Nucleic acids research 2010:38(Database issue)
94 Citations (Google Scholar as of 2016-01-29)
Abstract: Advances in high-throughput next-generation sequencing technology have reshaped the transcriptomic research landscape. However, exploration of these massive data remains a daunting challenge. In this study, we describe a novel database, deepBase, which we have developed to facilitate the comprehensive annotation and discovery of small RNAs from transcriptomic data. The current release of deepBase contains deep sequencing data from 185 small RNA libraries from diverse tissues and cell lines of seven organisms: human, mouse, chicken, Ciona intestinalis, Drosophila melanogaster, Caenhorhabditis elegans and Arabidopsis thaliana. By analyzing approximately 14.6 million unique reads that perfectly mapped to more than 284 million genomic loci, we annotated and identified approximately 380,000 unique ncRNA-associated small RNAs (nasRNAs), approximately 1.5 million unique promoter-associated small RNAs (pasRNAs), approximately 4.0 million unique exon-associated small RNAs (easRNAs) and approximately 6 million unique repeat-associated small RNAs (rasRNAs). Furthermore, 2038 miRNA and 1889 snoRNA candidates were predicted by miRDeep and snoSeeker. All of the mapped reads can be grouped into about 1.2 million RNA clusters. For the purpose of comparative analysis, deepBase provides an integrative, interactive and versatile display. A convenient search option, related publications and other useful information are also provided for further investigation. deepBase is available at: http://deepbase.sysu.edu.cn/.