Literature DB >> 24048357

miREval 2.0: a web tool for simple microRNA prediction in genome sequences.

Dadi Gao1, Robert Middleton, John E J Rasko, William Ritchie.   

Abstract

RESULT: We have developed miREval 2.0, an online tool that can simultaneously search up to 100 sequences for novel microRNAs (miRNAs) in multiple organisms. miREval 2.0 uses multiple published in silico approaches to detect miRNAs in sequences of interest. This tool can be used to discover miRNAs from DNA sequences or to validate candidates from sequencing data. AVAILABILITY: http://mimirna.centenary.org.au/mireval/.

Mesh：

Substances：

Year:  2013        PMID： 24048357      PMCID： PMC5994938          DOI： 10.1093/bioinformatics/btt545

Source DB:  PubMed          Journal:  Bioinformatics        ISSN： 1367-4803            Impact factor:   6.937


  11 in total

1.  Discovery of regulatory elements by a computational method for phylogenetic footprinting.

Authors:  Mathieu Blanchette; Martin Tompa
Journal:  Genome Res       Date:  2002-05       Impact factor: 9.043

2.  MicroRNA genes are transcribed by RNA polymerase II.

Authors:  Yoontae Lee; Minju Kim; Jinju Han; Kyu-Hyun Yeom; Sanghyuk Lee; Sung Hee Baek; V Narry Kim
Journal:  EMBO J       Date:  2004-09-16       Impact factor: 11.598

3.  Defining and providing robust controls for microRNA prediction.

Authors:  William Ritchie; Dadi Gao; John E J Rasko
Journal:  Bioinformatics       Date:  2012-03-08       Impact factor: 6.937

4.  Phylogenetic shadowing and computational identification of human microRNA genes.

Authors:  Eugene Berezikov; Victor Guryev; José van de Belt; Erno Wienholds; Ronald H A Plasterk; Edwin Cuppen
Journal:  Cell       Date:  2005-01-14       Impact factor: 41.582

5.  Circos: an information aesthetic for comparative genomics.

Authors:  Martin Krzywinski; Jacqueline Schein; Inanç Birol; Joseph Connors; Randy Gascoyne; Doug Horsman; Steven J Jones; Marco A Marra
Journal:  Genome Res       Date:  2009-06-18       Impact factor: 9.043

6.  Mireval: a web tool for simple microRNA prediction in genome sequences.

Authors:  William Ritchie; François-Xavier Théodule; Daniel Gautheret
Journal:  Bioinformatics       Date:  2008-05-03       Impact factor: 6.937

7.  Identification of clustered microRNAs using an ab initio prediction method.

Authors:  Alain Sewer; Nicodème Paul; Pablo Landgraf; Alexei Aravin; Sébastien Pfeffer; Michael J Brownstein; Thomas Tuschl; Erik van Nimwegen; Mihaela Zavolan
Journal:  BMC Bioinformatics       Date:  2005-11-07       Impact factor: 3.169

8.  Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine.

Authors:  Chenghai Xue; Fei Li; Tao He; Guo-Ping Liu; Yanda Li; Xuegong Zhang
Journal:  BMC Bioinformatics       Date:  2005-12-29       Impact factor: 3.169

9.  JASPAR 2010: the greatly expanded open-access database of transcription factor binding profiles.

Authors:  Elodie Portales-Casamar; Supat Thongjuea; Andrew T Kwon; David Arenillas; Xiaobei Zhao; Eivind Valen; Dimas Yusuf; Boris Lenhard; Wyeth W Wasserman; Albin Sandelin
Journal:  Nucleic Acids Res       Date:  2009-11-11       Impact factor: 16.971

10.  Clusters of microRNAs emerge by new hairpins in existing transcripts.

Authors:  Antonio Marco; Maria Ninova; Matthew Ronshaugen; Sam Griffiths-Jones
Journal:  Nucleic Acids Res       Date:  2013-06-17       Impact factor: 16.971
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  12 in total

1.  A framework for improving microRNA prediction in non-human genomes.

Authors:  Robert J Peace; Kyle K Biggar; Kenneth B Storey; James R Green
Journal:  Nucleic Acids Res       Date:  2015-07-10       Impact factor: 16.971

Review 2.  Computational Detection of Pre-microRNAs.

Authors:  Müşerref Duygu Saçar Demirci
Journal:  Methods Mol Biol       Date:  2022

3.  miRdentify: high stringency miRNA predictor identifies several novel animal miRNAs.

Authors:  Thomas B Hansen; Morten T Venø; Jørgen Kjems; Christian K Damgaard
Journal:  Nucleic Acids Res       Date:  2014-07-22       Impact factor: 16.971

4.  A legion of potential regulatory sRNAs exists beyond the typical microRNAs microcosm.

Authors:  Ashwani Jha; Ganesh Panzade; Rajesh Pandey; Ravi Shankar
Journal:  Nucleic Acids Res       Date:  2015-09-09       Impact factor: 16.971

5.  Delineating the impact of machine learning elements in pre-microRNA detection.

Authors:  Müşerref Duygu Saçar Demirci; Jens Allmer
Journal:  PeerJ       Date:  2017-03-29       Impact factor: 2.984

6.  On the performance of pre-microRNA detection algorithms.

Authors:  Müşerref Duygu Saçar Demirci; Jan Baumbach; Jens Allmer
Journal:  Nat Commun       Date:  2017-08-24       Impact factor: 14.919

7.  African swine fever virus does not express viral microRNAs in experimentally infected pigs.

Authors:  Fernando Núñez-Hernández; Gonzalo Vera; Armand Sánchez; Fernando Rodríguez; José I Núñez
Journal:  BMC Vet Res       Date:  2018-09-03       Impact factor: 2.741

8.  Differential expression of novel MicroRNAs from developing fetal heart of Gallus gallus domesticus implies a role in cardiac development.

Authors:  Sharad Saxena; Priyanka Mathur; Vaibhav Shukla; Vibha Rani
Journal:  Mol Cell Biochem       Date:  2019-09-07       Impact factor: 3.396

9.  Distinguishing mirtrons from canonical miRNAs with data exploration and machine learning methods.

Authors:  Grzegorz Rorbach; Olgierd Unold; Bogumil M Konopka
Journal:  Sci Rep       Date:  2018-05-15       Impact factor: 4.379

10.  Fast and accurate microRNA search using CNN.

Authors:  Xubo Tang; Yanni Sun
Journal:  BMC Bioinformatics       Date:  2019-12-27       Impact factor: 3.169
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