Literature DB >> 23625170

MiRAuto: an automated user-friendly microRNA prediction tool utilizing plant small RNA sequencing data.

Jeongsoo Lee1, Dong-In Kim, June Hyun Park, Ik-Young Choi, Chanseok Shin.   

Abstract

MicroRNAs (miRNAs) are a class of small RNAs that post-transcriptionally regulate gene expression in animals and plants. The recent rapid advancement in miRNA biology, including high-throughput sequencing of small RNA libraries, inspired the development of a bioinformatics software, miRAuto, which predicts putative miRNAs in model plant genomes computationally. Furthermore, miRAuto enables users to identify miRNAs in non-model plant species whose genomes have yet to be fully sequenced. miRAuto analyzes the expression of the 5'-end position of mapped small RNAs in reference sequences to prevent the possibility of mRNA fragments being included as candidate miRNAs. We validated the utility of miRAuto on a small RNA dataset, and the results were compared to other publicly available miRNA prediction programs. In conclusion, miRAuto is a fully automated user-friendly tool for predicting miRNAs from small RNA sequencing data in both model and non-model plant species. miRAuto is available at http://nature.snu.ac.kr/software/miRAuto.htm.

Mesh:

Substances:

Year:  2013        PMID: 23625170      PMCID: PMC3887891          DOI: 10.1007/s10059-013-0019-8

Source DB:  PubMed          Journal:  Mol Cells        ISSN: 1016-8478            Impact factor:   5.034


  18 in total

Review 1.  RNA stem-loops: to be or not to be cleaved by RNAse III.

Authors:  William Ritchie; Matthieu Legendre; Daniel Gautheret
Journal:  RNA       Date:  2007-02-13       Impact factor: 4.942

2.  Large-scale sequencing reveals 21U-RNAs and additional microRNAs and endogenous siRNAs in C. elegans.

Authors:  J Graham Ruby; Calvin Jan; Christopher Player; Michael J Axtell; William Lee; Chad Nusbaum; Hui Ge; David P Bartel
Journal:  Cell       Date:  2006-12-15       Impact factor: 41.582

3.  Arabidopsis microRNA167 controls patterns of ARF6 and ARF8 expression, and regulates both female and male reproduction.

Authors:  Miin-Feng Wu; Qing Tian; Jason W Reed
Journal:  Development       Date:  2006-10-04       Impact factor: 6.868

4.  A diverse and evolutionarily fluid set of microRNAs in Arabidopsis thaliana.

Authors:  Ramya Rajagopalan; Hervé Vaucheret; Jerry Trejo; David P Bartel
Journal:  Genes Dev       Date:  2006-12-15       Impact factor: 11.361

5.  Novel and stress-regulated microRNAs and other small RNAs from Arabidopsis.

Authors:  Ramanjulu Sunkar; Jian-Kang Zhu
Journal:  Plant Cell       Date:  2004-07-16       Impact factor: 11.277

6.  Improved northern blot method for enhanced detection of small RNA.

Authors:  Gurman S Pall; Andrew J Hamilton
Journal:  Nat Protoc       Date:  2008       Impact factor: 13.491

7.  Discovering microRNAs from deep sequencing data using miRDeep.

Authors:  Marc R Friedländer; Wei Chen; Catherine Adamidi; Jonas Maaskola; Ralf Einspanier; Signe Knespel; Nikolaus Rajewsky
Journal:  Nat Biotechnol       Date:  2008-04       Impact factor: 54.908

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.  High-throughput sequencing of Arabidopsis microRNAs: evidence for frequent birth and death of MIRNA genes.

Authors:  Noah Fahlgren; Miya D Howell; Kristin D Kasschau; Elisabeth J Chapman; Christopher M Sullivan; Jason S Cumbie; Scott A Givan; Theresa F Law; Sarah R Grant; Jeffery L Dangl; James C Carrington
Journal:  PLoS One       Date:  2007-02-14       Impact factor: 3.240

10.  Small RNA and transcriptome deep sequencing proffers insight into floral gene regulation in Rosa cultivars.

Authors:  Jungeun Kim; June Hyun Park; Chan Ju Lim; Jae Yun Lim; Jee-Youn Ryu; Bong-Woo Lee; Jae-Pil Choi; Woong Bom Kim; Ha Yeon Lee; Yourim Choi; Donghyun Kim; Cheol-Goo Hur; Sukweon Kim; Yoo-Sun Noh; Chanseok Shin; Suk-Yoon Kwon
Journal:  BMC Genomics       Date:  2012-11-21       Impact factor: 3.969

View more
  2 in total

1.  miRCat2: accurate prediction of plant and animal microRNAs from next-generation sequencing datasets.

Authors:  Claudia Paicu; Irina Mohorianu; Matthew Stocks; Ping Xu; Aurore Coince; Martina Billmeier; Tamas Dalmay; Vincent Moulton; Simon Moxon
Journal:  Bioinformatics       Date:  2017-08-15       Impact factor: 6.937

Review 2.  Biogenesis, Functions, Interactions, and Resources of Non-Coding RNAs in Plants.

Authors:  Haoyu Chao; Yueming Hu; Liang Zhao; Saige Xin; Qingyang Ni; Peijing Zhang; Ming Chen
Journal:  Int J Mol Sci       Date:  2022-03-28       Impact factor: 5.923

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.