Literature DB >> 25948580

PACCMIT/PACCMIT-CDS: identifying microRNA targets in 3' UTRs and coding sequences.

Miroslav Šulc1, Ray M Marín1, Harlan S Robins2, Jiří Vaníček3.   

Abstract

The purpose of the proposed web server, publicly available at http://paccmit.epfl.ch, is to provide a user-friendly interface to two algorithms for predicting messenger RNA (mRNA) molecules regulated by microRNAs: (i) PACCMIT (Prediction of ACcessible and/or Conserved MIcroRNA Targets), which identifies primarily mRNA transcripts targeted in their 3' untranslated regions (3' UTRs), and (ii) PACCMIT-CDS, designed to find mRNAs targeted within their coding sequences (CDSs). While PACCMIT belongs among the accurate algorithms for predicting conserved microRNA targets in the 3' UTRs, the main contribution of the web server is 2-fold: PACCMIT provides an accurate tool for predicting targets also of weakly conserved or non-conserved microRNAs, whereas PACCMIT-CDS addresses the lack of similar portals adapted specifically for targets in CDS. The web server asks the user for microRNAs and mRNAs to be analyzed, accesses the precomputed P-values for all microRNA-mRNA pairs from a database for all mRNAs and microRNAs in a given species, ranks the predicted microRNA-mRNA pairs, evaluates their significance according to the false discovery rate and finally displays the predictions in a tabular form. The results are also available for download in several standard formats.
© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Entities:  

Mesh:

Substances:

Year:  2015        PMID: 25948580      PMCID: PMC4489287          DOI: 10.1093/nar/gkv457

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  23 in total

1.  Analysis of the accessibility of CLIP bound sites reveals that nucleation of the miRNA:mRNA pairing occurs preferentially at the 3'-end of the seed match.

Authors:  Ray M Marín; Franziska Voellmy; Thibaud von Erlach; Jiří Vaníček
Journal:  RNA       Date:  2012-08-22       Impact factor: 4.942

2.  Searching the coding region for microRNA targets.

Authors:  Ray M Marín; Miroslav Sulc; Jirí Vanícek
Journal:  RNA       Date:  2013-02-12       Impact factor: 4.942

3.  Most mammalian mRNAs are conserved targets of microRNAs.

Authors:  Robin C Friedman; Kyle Kai-How Farh; Christopher B Burge; David P Bartel
Journal:  Genome Res       Date:  2008-10-27       Impact factor: 9.043

4.  Host microRNA regulation of human cytomegalovirus immediate early protein translation promotes viral latency.

Authors:  Christine M O'Connor; Jiri Vanicek; Eain A Murphy
Journal:  J Virol       Date:  2014-03-05       Impact factor: 5.103

Review 5.  MicroRNAs: target recognition and regulatory functions.

Authors:  David P Bartel
Journal:  Cell       Date:  2009-01-23       Impact factor: 41.582

6.  Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP.

Authors:  Markus Hafner; Markus Landthaler; Lukas Burger; Mohsen Khorshid; Jean Hausser; Philipp Berninger; Andrea Rothballer; Manuel Ascano; Anna-Carina Jungkamp; Mathias Munschauer; Alexander Ulrich; Greg S Wardle; Scott Dewell; Mihaela Zavolan; Thomas Tuschl
Journal:  Cell       Date:  2010-04-02       Impact factor: 41.582

7.  miRBase: integrating microRNA annotation and deep-sequencing data.

Authors:  Ana Kozomara; Sam Griffiths-Jones
Journal:  Nucleic Acids Res       Date:  2010-10-30       Impact factor: 16.971

8.  Efficient use of accessibility in microRNA target prediction.

Authors:  Ray M Marín; Jirí Vanícek
Journal:  Nucleic Acids Res       Date:  2010-08-30       Impact factor: 16.971

9.  Optimal use of conservation and accessibility filters in microRNA target prediction.

Authors:  Ray M Marín; Jiří Vaníček
Journal:  PLoS One       Date:  2012-02-27       Impact factor: 3.240

10.  Prediction of altered 3'- UTR miRNA-binding sites from RNA-Seq data: the swine leukocyte antigen complex (SLA) as a model region.

Authors:  Marie-Laure Endale Ahanda; Eric R Fritz; Jordi Estellé; Zhi-Liang Hu; Ole Madsen; Martien A M Groenen; Dario Beraldi; Ronan Kapetanovic; David A Hume; Robert R R Rowland; Joan K Lunney; Claire Rogel-Gaillard; James M Reecy; Elisabetta Giuffra
Journal:  PLoS One       Date:  2012-11-06       Impact factor: 3.240

View more
  8 in total

1.  Combinatorial ensemble miRNA target prediction of co-regulation networks with non-prediction data.

Authors:  Jason A Davis; Sita J Saunders; Martin Mann; Rolf Backofen
Journal:  Nucleic Acids Res       Date:  2017-09-06       Impact factor: 16.971

Review 2.  Synonymous Variants: Necessary Nuance in Our Understanding of Cancer Drivers and Treatment Outcomes.

Authors:  Nayiri M Kaissarian; Douglas Meyer; Chava Kimchi-Sarfaty
Journal:  J Natl Cancer Inst       Date:  2022-08-08       Impact factor: 11.816

3.  Downregulation of microRNA-30d promotes cell proliferation and invasion by targeting LRH-1 in colorectal carcinoma.

Authors:  Likun Yan; Jian Qiu; Jianfeng Yao
Journal:  Int J Mol Med       Date:  2017-04-20       Impact factor: 4.101

4.  miRgo: integrating various off-the-shelf tools for identification of microRNA-target interactions by heterogeneous features and a novel evaluation indicator.

Authors:  Yen-Wei Chu; Kai-Po Chang; Chi-Wei Chen; Yu-Tai Liang; Zhi Thong Soh; Li-Ching Hsieh
Journal:  Sci Rep       Date:  2020-01-30       Impact factor: 4.379

5.  Piperlongumine inhibits the growth of non-small cell lung cancer cells via the miR-34b-3p/TGFBR1 pathway.

Authors:  Xinhua Lu; Chenyang Xu; Zhexuan Xu; Chunya Lu; Rui Yang; Furui Zhang; Guojun Zhang
Journal:  BMC Complement Med Ther       Date:  2021-01-07

Review 6.  miRNA Targets: From Prediction Tools to Experimental Validation.

Authors:  Giulia Riolo; Silvia Cantara; Carlotta Marzocchi; Claudia Ricci
Journal:  Methods Protoc       Date:  2020-12-24

7.  miR-30d inhibits cell biological progression of Ewing's sarcoma by suppressing the MEK/ERK and PI3K/Akt pathways in vitro.

Authors:  Conglin Ye; Xiaolong Yu; Xuqiang Liu; Min Dai; Bin Zhang
Journal:  Oncol Lett       Date:  2018-01-29       Impact factor: 2.967

8.  miRAW: A deep learning-based approach to predict microRNA targets by analyzing whole microRNA transcripts.

Authors:  Albert Pla; Xiangfu Zhong; Simon Rayner
Journal:  PLoS Comput Biol       Date:  2018-07-13       Impact factor: 4.475

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.