deep sequencing

microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) and represent two classes of important non-coding RNAs in eukaryotes. Although these non-coding RNAs have been implicated in organismal development and in various human diseases, surprisingly little is known about their transcriptional regulation. Recent advances in chromatin immunoprecipitation with next-generation DNA sequencing (ChIP-Seq) have provided methods of detecting transcription factor binding sites (TFBSs) with unprecedented sensitivity. In this study, we describe ChIPBase (, a novel database that we have developed to facilitate the comprehensive annotation and discovery of transcription factor binding maps and transcriptional regulatory relationships of miRNAs and lncRNAs from ChIP-Seq data.

chipBase workflow

The current release of ChIPBase includes high-throughput sequencing data that were generated by 543 ChIP-Seq experiments in diverse tissues and cell lines from six organisms. By analysing millions of TFBSs, we identified tens of thousands of TF-lncRNA and TF-miRNA regulatory relationships. Furthermore, we constructed TF->miRNA->mRNAs regulatory networks by integrating CLIP-Seq data and ChIP-Seq data. In addition, we constructed expression profiles of human lncRNAs and mRNAs from RNA-Seq data from 22 normal tissues.

The ChIPBase is available at

Yang JH, Li JH, Jiang S, Zhou H and Qu LH.
ChIPBase: A database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data.
Nucleic Acids Res. 2013, First published online: November 17, 2012.

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miRBase is the primary online repository for all microRNA sequences and annotation. The current release (miRBase 16) contains over 15,000 microRNA gene loci in over 140 species, and over 17,000 distinct mature microRNA sequences.

Deep-sequencing technologies have delivered a sharp rise in the rate of novel microRNA discovery. The miRBase curators have mapped reads from short RNA deep-sequencing experiments to microRNAs in miRBase and developed web interfaces to view these mappings. The user can view all read data associated with a given microRNA annotation, filter reads by experiment and count, and search for microRNAs by tissue- and stage-specific expression. These data can be used as a proxy for relative expression levels of microRNA sequences, provide detailed evidence for microRNA annotations and alternative isoforms of mature microRNAs, and allow us to revisit previous annotations.

miRBase is available online at:

Kozomara A, Griffiths-Jones S (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic acids research 39(Database issue), D152-57. [article]

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An emerging major challenge is the interpretation of the genome-scale miRNA datasets, including those derived from microarray and deep-sequencing. It is interesting and important to know the common rules or patterns behind a list of miRNAs, (i.e. the deregulated miRNAs resulted from an experiment of miRNA microarray or deep-sequencing).  TAM is a tool for annotations that can efficiently identify meaningful categories for given miRNAs. In addition, TAM can be used to identify novel miRNA biomarkers.

TAM tool, source codes, and miRNA category data are freely available at

Lu M, Shi B, Wang J, Cao Q, Cui Q. (2010) TAM: A method for enrichment and depletion analysis of a microRNA category in a list of microRNAs. BMC Bioinformatics 11, 419. [abstract]

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Houston (PRWEB) March 19, 2010 — LC Sciences today announced the launch of its new Seq-Array services designed to take full advantage of both the latest deep sequencing capabilities and the proven genomics tool – microarray. This combination of technologies advances microRNA research to the next level of depth and understanding that was not possible before with either of the technologies alone. LC Sciences has been a leading provider of microRNA discovery and profiling services since 2005.  (read more)


One of the limitations of microarray expression profiling is the requirement of prior sequence information, to be used for probe design.  Until recently, this has been limited mostly to that found in public databases (i.e. miRBase), these data having been gathered mainly through a combination of bioinformatics and extensive cloning experiments.  In contrast, deep sequencing is not dependent on any prior sequence information, instead providing information about all RNA species in the sample and allowing for discovery of novel microRNAs or other types of small RNAs.  Thus providing an excellent tool for those studying species where limited sequence information is currently available.  Additionally, new sequence information provided by deep sequencing can be used to design microarray probe content for future large scale expression studies.

Deep sequencing identifies novel and conserved microRNAs in peanut (Arachis hypogaea L.).
Zhao CZ, Xia H, Frazier TP, Yao YY, Bi YP, Li AQ, Li MJ, Li CS, Zhang BH, Wang XJ.
BMC Plant Biol. 2010 Jan 5;10(1):3.

Genome-wide identification of Schistosoma japonicum microRNAs using a deep-sequencing approach.
Huang J, Hao P, Chen H, Hu W, Yan Q, Liu F, Han ZG.
PLoS One. 2009 Dec 8;4(12):e8206.

Novel microRNAs uncovered by deep sequencing of small RNA transcriptomes in bread wheat (Triticum aestivum L.) and Brachypodium distachyon (L.) Beauv.
Wei B, Cai T, Zhang R, Li A, Huo N, Li S, Gu YQ, Vogel J, Jia J, Qi Y, Mao L.
Funct Integr Genomics. 2009 Nov;9(4):499-511.

Abundant and dynamically expressed miRNAs, piRNAs, and other small RNAs in the vertebrate Xenopus tropicalis.
Armisen J, Gilchrist MJ, Wilczynska A, Standart N, Miska EA.
Genome Res. 2009 Oct;19(10):1766-75.

Deep sequencing of Brachypodium small RNAs at the global genome level identifies microRNAs involved in cold stress response.
Zhang J, Xu Y, Huan Q, Chong K.
BMC Genomics. 2009 Sep 23;10:449.

Genome-wide Medicago truncatula small RNA analysis revealed novel microRNAs and isoforms differentially regulated in roots and nodules.
Lelandais-Brière C, Naya L, Sallet E, Calenge F, Frugier F, Hartmann C, Gouzy J, Crespi M.
Plant Cell. 2009 Sep;21(9):2780-96.

High throughput sequencing of microRNAs in chicken somites.
Rathjen T, Pais H, Sweetman D, Moulton V, Munsterberg A, Dalmay T.
FEBS Lett. 2009 May 6;583(9):1422-6.

Deep sequencing of tomato short RNAs identifies microRNAs targeting genes involved in fruit ripening.
Moxon S, Jing R, Szittya G, Schwach F, Rusholme Pilcher RL, Moulton V, Dalmay T.
Genome Res. 2008 Oct;18(10):1602-9.

Identification of novel and candidate miRNAs in rice by high throughput sequencing.
Sunkar R, Zhou X, Zheng Y, Zhang W, Zhu JK.
BMC Plant Biol. 2008 Feb 29;8:25.

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Web-Based Tools for MicroRNA Analysis from Deep Sequencing Data

December 23, 2009

mirTools – a web server for microRNA profiling and discovery based on high-throughput sequencing data. Classification of the large-scale short reads into known categories, such as known miRNAs, non-coding RNA, genomic repeats or coding sequences. Providing a detailed annotation information of known miRNAs, such as miRNA/miRNA*, absolute/relative reads count and the most abundant tag. Discovery […]

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