Online Tools
Linc2GO: A Human LincRNA Function Annotation Resource Based On ceRNA Hypothesis
Large numbers of lincRNA (long intergenic non-coding RNA) have been detected through high-throughput sequencing technology. However, currently we still know very little about their functions. Therefore, a lincRNA function annotation database is needed to facilitate the study in this field. In this article, researchers from the Tsinghua University, China present Linc2GO, a web resource which aims to provide comprehensive functional annotations for human lincRNA. MicroRNA-mRNA as well as microRNA-lincRNA interaction data were integrated to generate lincRNA functional annotations based on the “ceRNA hypothesis”. To the author’s knowledge, Linc2GO is the first database that makes use of the “ceRNA hypothesis” to predict lincRNA functions.
AVAILABILITY - Freely available at http://www.bioinfo.tsinghua.edu.cn/~liuke/Linc2GO/index.html
CONTACT: sunzhr@mail.tsinghua.edu.cn
- Liu K, Yan Z, Li Y, Sun Z. (2013) Linc2GO: A Human LincRNA Function Annotation Resource Based On ceRNA Hypothesis. Bioinformatics [Epub ahead of print]. [abstract]
Incoming search terms:
- linc2GO
Software.ncrna.org
A portal site for web servers and software tools for sequence/structure analyses of non-conding RNAs
This portal site offers web servers and downloading of software tools of ncRNA.org for the analyses of RNA sequences and their structures. The secondary structures of the RNAs as well as their sequences are important for the comparison, clustering and the finding non-coding RNAs. The following software tools focus on the secondary structures for the analyses of RNAs. We recommend the integrated web server, which also offers blat search of the input sequences on various genome sequences and mapping to UCSC GenomeBrowser for Functional RNA
Brief explanations of the software tools are located below the following table. (read more…)
CentroidAlign - fast and accurate aligner for structured RNAs by maximizing expected sum-of-pairs score
The importance of accurate and fast predictions of multiple alignments for RNA sequences has increased due to recent findings about functional non-coding RNAs. Recent studies suggest that maximizing the expected accuracy of predictions will be useful for many problems in bioinformatics.
Researchers at the Mizuho Information & Research Institute designed a novel estimator for multiple alignments of structured RNAs, based on maximizing the expected accuracy of predictions. First, they define the maximum expected accuracy (MEA) estimator for pairwise alignment of RNA sequences. This maximizes the expected sum-of-pairs score (SPS) of a predicted alignment under a probability distribution of alignments given by marginalizing the Sankoff model. Then, by approximating the MEA estimator, they obtain an estimator whose time complexity is O(L(3)+c(2)dL(2)) where L is the length of input sequences and both c and d are constants independent of L. The proposed estimator can handle uncertainty of secondary structures and alignments that are obstacles in Bioinformatics because it considers all the secondary structures and all the pairwise alignments as input sequences. Moreover, they integrate the probabilistic consistency transformation (PCT) on alignments into the proposed estimator. Computational experiments using six benchmark datasets indicate that the proposed method achieved a favorable SPS and was the fastest of many state-of-the-art tools for multiple alignments of structured RNAs.
AVAILABILITY: The software called CentroidAlign, which is an implementation of the algorithm in this article, is freely available at: http://www.ncrna.org/software/centroidalign/
CONTACT: hamada-michiaki@aist.go.jp
- Hamada M, Sato K, Kiryu H, Mituyama T, Asai K. (2009) CentroidAlign: fast and accurate aligner for structured RNAs by maximizing expected sum-of-pairs score. Bioinformatics 25(24), 3236-43. [article]
RegRNA 2.0 - an easy to use web server for identifying regulatory RNA motifs and functional sites
Functional RNA molecules participate in numerous biological processes, ranging from gene regulation to protein synthesis. Analysis of functional RNA motifs and elements in RNA sequences can obtain useful information for deciphering RNA regulatory mechanisms. A previous version, RegRNA, is widely used in the identification of regulatory motifs, and this work extends it by incorporating more comprehensive and updated data sources and analytical approaches into a new platform.
An integrated web-based system, RegRNA 2.0, has been developed for comprehensively identifying the functional RNA motifs and sites in an input RNA sequence. Numerous data sources and analytical approaches are integrated, and several types of functional RNA motifs and sites can be identified by RegRNA 2.0: (i) splicing donor/acceptor sites; (ii) splicing regulatory motifs; (iii) polyadenylation sites; (iv) ribosome binding sites; (v) rho-independent terminator; (vi) motifs in mRNA 5′-untranslated region (5’UTR) and 3’UTR; (vii) AU-rich elements; (viii) C-to-U editing sites; (ix) riboswitches; (x) RNA cis-regulatory elements; (xi) transcriptional regulatory motifs; (xii) user-defined motifs; (xiii) similar functional RNA sequences; (xiv) microRNA target sites; (xv) non-coding RNA hybridization sites; (xvi) long stems; (xvii) open reading frames; (xviii) related information of an RNA sequence. User can submit an RNA sequence and obtain the predictive results through RegRNA 2.0 web page.
RegRNA 2.0 is an easy to use web server for identifying regulatory RNA motifs and functional sites. Through its integrated user-friendly interface, user is capable of using various analytical approaches and observing results with graphical visualization conveniently.
RegRNA 2.0 is now available at - http://regrna2.mbc.nctu.edu.tw.
Chang TH, Huang HY, Hsu JB, Weng SL, Horng JT, Huang HD. (2013) An enhanced computational platform for investigating the roles of regulatory RNA and for identifying functional RNA motifs. BMC Bioinformatics 14 Suppl 2:S4. [article]
Incoming search terms:
- RegRNA
iSeeRNA: identification of long intergenic non-coding RNA transcripts from RNA-Seq data
Long intergenic non-coding RNAs (lincRNAs) are emerging as a novel class of non-coding RNAs and potent gene regulators. High-throughput RNA-sequencing combined with de novo assembly promises quantity discovery of novel transcripts. However, the identification of lincRNAs from thousands of assembled transcripts is still challenging due to the difficulties of separating them from protein coding transcripts (PCTs).
A team of scientists at The Chinese University of Hong Kong have developed iSeeRNA, a support vector machine (SVM)-based classifier for the identification of lincRNAs. iSeeRNA shows better performance compared to other software.
iSeeRNA demonstrates high prediction accuracy and runs several magnitudes faster than other similar programs. It can be integrated into the transcriptome data analysis pipelines or run as a web server, thus offering a valuable tool for lincRNA study.
Availability - iSeeRNA is available as a user-friendly web server with free accessibility at http://www.myogenesisdb.org/iSeeRNA
- Sun K, Chen X, Jiang P, Song X, Wang H, Sun H. (2013) iSeeRNA: identification of long intergenic non-coding RNA transcripts from transcriptome sequencing data. BMC Genomics 14(supp 2). [article]
Incoming search terms:
- long intergenic noncoding rnas