deep sequencing

miRBase: integrating microRNA annotation and deep-sequencing data

by Chris on January 10, 2011

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: http://www.mirbase.org/.

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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TAM: A tool for annotations of microRNAs

by Chris on August 27, 2010

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 http://cmbi.bjmu.edu.cn/tam

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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Press Release – LC Sciences Pairs Deep Sequencing with Customized Microarrays to Offer New Seq-Array Service for Discovery & Profiling Applications

by Chris on March 19, 2010

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)

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Deep Sequencing – a Tool for Novel microRNA Discovery in Many Species

by Chris on January 8, 2010

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

by Chris on 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 of the novel miRNAs from the high-throughput sequencing technology.

Identification of the differentially expressed miRNAs according to read tag counts (the number of reads for each tag reflects relative express level).

miRDeep – Discovering known and novel miRNAs from deep sequencing data

The miRDeep package was developed to discover active known or novel miRNAs from deep sequencing data (Solexa/Illumina, 454, …). The package consists of everything you need to analyze your own deep sequencing data after removal of ligation adapters: a number of scripts to preprocess the mapped data, and the core miRDeep algorithm that will analyze and score these data.

deepBase – a database for deeply annotating and mining deep sequencing data

a novel database, 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. 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. [click to continue…]

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