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Citations per year

Number of citations per year for the bioinformatics software tool RactIP
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Tool usage distribution map

This map represents all the scientific publications referring to RactIP per scientific context
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RactIP specifications

Information


Unique identifier OMICS_09342
Name RactIP
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data Two RNA sequences (FASTA format or only a sequence in 5'–3' direction)
Operating system Unix/Linux, Mac OS, Windows
Programming languages C++
Computer skills Advanced
Stability Stable
Maintained Yes

Versioning


No version available

Maintainer


  • person_outline Yuki Kato

Information


Unique identifier OMICS_09342
Name RactIP
Interface Web user interface
Restrictions to use None
Input data Two RNA sequences (FASTA format or only a sequence in 5'–3' direction)
Programming languages C++
Computer skills Basic
Stability Stable
Maintained Yes

Maintainer


  • person_outline Yuki Kato

Publication for RactIP

RactIP citations

 (7)
library_books

Computational prediction of lncRNA mRNA interactionsby integrating tissue specificity in human transcriptome

2017
Biol Direct
PMCID: 5465533
PMID: 28595592
DOI: 10.1186/s13062-017-0183-4

[…] We previously developed a series of computational pipelines including various computational tools for sequence analysis, such as Raccess [], TanTan [], LAST [, ], IntaRNA [], and RactIP [], for predicting human lncRNA-RNA interactions []. Furthermore, all predicted human lncRNA-RNA interactions are available from our database (http://rtools.cbrc.jp/cgi-bin/RNARNA/index.pl). Ou […]

library_books

A comprehensive benchmark of RNA–RNA interaction prediction tools for all domains of life

2016
Bioinformatics
PMCID: 5408919
PMID: 27993777
DOI: 10.1093/bioinformatics/btw728

[…] FE methods also become inaccurate with longer RNA sequences (; ; ; ). RNA interaction prediction algorithms generally do not consider multiple binding regions—only a few of which such as bistaRNA and ractIP, include multiple binding positions in their model (; ). Cellular dynamics (i.e. interaction with other molecules, ion concentrations, etc.) can influence RNA structures () and RNA interactions […]

library_books

Comprehensive prediction of lncRNA–RNA interactions in human transcriptome

2016
BMC Genomics
PMCID: 4895283
PMID: 26818453
DOI: 10.1186/s12864-015-2307-5

[…] etween a region of lncRNA and the 3 ′UTR in mRNA. Additionally, the joint secondary structure of the two subsequences (processed by our pipeline) of the two 1/2-sbsRNA–mRNA interactions (predicted by RactIP [] and shown in Additional file : Figure S5) indicates that it includes a long anti-sense-like interaction and the binding sites are located in the 3 ′UTR of the mRNAs (Fig. ). It is also noted […]

library_books

A comprehensive comparison of general RNA–RNA interaction prediction methods

2015
Nucleic Acids Res
PMCID: 4838349
PMID: 26673718
DOI: 10.1093/nar/gkv1477

[…] IntaRNA and RNAplex-a, interaction-only RIsearch and RNAplex-c, and concatenation-based Pairfold and RNAcofold show highly similar performance profiles (Supplementary Figure S2, left). RNAduplex and RactIP also cluster together, which was not apparent from their algorithmic strategies.The mean of performance results is shown on Supplementary Table S1 for the sRNA data set, seen for results run wi […]

library_books

Evidence that avian reovirus σNS is an RNA chaperone: implications for genome segment assortment

2015
Nucleic Acids Res
PMCID: 4538827
PMID: 26109354
DOI: 10.1093/nar/gkv639

[…] 91 nucleotides of the 3′ end (1552–1643 nt) of segment s1 precursor and a similarly sized RNA fragment (422–513 nt) of segment s4 precursor (S1133 strain of ARV). These regions were identified using RactIP tool () with the minimum folding energies and structures of the respective RNA sequences computed using mfold (). DNA Ultramers (IDT), incorporating T7 promoter sequences upstream of either s1 […]

library_books

RNA RNA interaction prediction using genetic algorithm

2014
PMCID: 4122056
PMID: 25114714
DOI: 10.1186/1748-7188-9-17

[…] Tcofold has the best MCC value and in other two pairs RyhB-uof-fur and RyhB-sodB, GRNAs has the highest MCC value.We also compared GRNAs with four state-of-the-art methods: inRNAs, IntaRNA, RNAup and RactIP. Table  shows the results of prediction in binding sites in sensitivity and positive predictive values on the datasets [,] using the proposed approach and mentioned methods. Here, only external […]


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RactIP institution(s)
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, Japan; Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Chiba, Japan; Mizuho Information & Research Institute, Inc, Chiyoda-ku, Tokyo, Japan; Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), Aomi, Koto-ku, Tokyo, Japan; Department of Mathematical Sciences, Faculty of Science and Engineering, Doshisha University, Kyotanabe, Kyoto, Japan

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