Literature DB >> 26085220

repRNA: a web server for generating various feature vectors of RNA sequences.

Bin Liu1,2,3, Fule Liu4, Longyun Fang5, Xiaolong Wang6,7, Kuo-Chen Chou8,9.   

Abstract

With the rapid growth of RNA sequences generated in the postgenomic age, it is highly desired to develop a flexible method that can generate various kinds of vectors to represent these sequences by focusing on their different features. This is because nearly all the existing machine-learning methods, such as SVM (support vector machine) and KNN (k-nearest neighbor), can only handle vectors but not sequences. To meet the increasing demands and speed up the genome analyses, we have developed a new web server, called "representations of RNA sequences" (repRNA). Compared with the existing methods, repRNA is much more comprehensive, flexible and powerful, as reflected by the following facts: (1) it can generate 11 different modes of feature vectors for users to choose according to their investigation purposes; (2) it allows users to select the features from 22 built-in physicochemical properties and even those defined by users' own; (3) the resultant feature vectors and the secondary structures of the corresponding RNA sequences can be visualized. The repRNA web server is freely accessible to the public at http://bioinformatics.hitsz.edu.cn/repRNA/ .

Keywords:  Physicochemical properties; PseAAC; PseKNC; Secondary structure of RNA; User-defined properties; repDNA; repRNA

Mesh:

Substances:

Year:  2015        PMID: 26085220     DOI: 10.1007/s00438-015-1078-7

Source DB:  PubMed          Journal:  Mol Genet Genomics        ISSN: 1617-4623            Impact factor:   3.291


  50 in total

1.  Multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization.

Authors:  Suyu Mei
Journal:  J Theor Biol       Date:  2011-10-21       Impact factor: 2.691

2.  Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses.

Authors:  Maryam Esmaeili; Hassan Mohabatkar; Sasan Mohsenzadeh
Journal:  J Theor Biol       Date:  2009-12-02       Impact factor: 2.691

3.  iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition.

Authors:  Zi Liu; Xuan Xiao; Wang-Ren Qiu; Kuo-Chen Chou
Journal:  Anal Biochem       Date:  2015-01-14       Impact factor: 3.365

4.  iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach.

Authors:  Bin Liu; Longyun Fang; Fule Liu; Xiaolong Wang; Kuo-Chen Chou
Journal:  J Biomol Struct Dyn       Date:  2015-03-03

5.  PseDNA-Pro: DNA-Binding Protein Identification by Combining Chou's PseAAC and Physicochemical Distance Transformation.

Authors:  Bin Liu; Jinghao Xu; Shixi Fan; Ruifeng Xu; Jiyun Zhou; Xiaolong Wang
Journal:  Mol Inform       Date:  2014-09-26       Impact factor: 3.353

6.  Prediction of β-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine.

Authors:  Ravindra Kumar; Abhishikha Srivastava; Bandana Kumari; Manish Kumar
Journal:  J Theor Biol       Date:  2014-10-22       Impact factor: 2.691

7.  PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition.

Authors:  Wei Chen; Tian-Yu Lei; Dian-Chuan Jin; Hao Lin; Kuo-Chen Chou
Journal:  Anal Biochem       Date:  2014-04-13       Impact factor: 3.365

8.  Identification of real microRNA precursors with a pseudo structure status composition approach.

Authors:  Bin Liu; Longyun Fang; Fule Liu; Xiaolong Wang; Junjie Chen; Kuo-Chen Chou
Journal:  PLoS One       Date:  2015-03-30       Impact factor: 3.240

9.  Some remarks on protein attribute prediction and pseudo amino acid composition.

Authors:  Kuo-Chen Chou
Journal:  J Theor Biol       Date:  2010-12-17       Impact factor: 2.691

10.  iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition.

Authors:  Wei Chen; Peng-Mian Feng; Hao Lin; Kuo-Chen Chou
Journal:  Biomed Res Int       Date:  2014-05-21       Impact factor: 3.411

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  54 in total

1.  iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.

Authors:  Muhammad Kabir; Maqsood Hayat
Journal:  Mol Genet Genomics       Date:  2015-08-30       Impact factor: 3.291

2.  Evolutionary mechanism and biological functions of 8-mers containing CG dinucleotide in yeast.

Authors:  Yan Zheng; Hong Li; Yue Wang; Hu Meng; Qiang Zhang; Xiaoqing Zhao
Journal:  Chromosome Res       Date:  2017-02-09       Impact factor: 5.239

3.  MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors.

Authors:  Robson P Bonidia; Douglas S Domingues; Danilo S Sanches; André C P L F de Carvalho
Journal:  Brief Bioinform       Date:  2022-01-17       Impact factor: 11.622

4.  Comparison of genomic data via statistical distribution.

Authors:  Saeid Amiri; Ivo D Dinov
Journal:  J Theor Biol       Date:  2016-07-25       Impact factor: 2.691

5.  Multi-feature Fusion Method Based on Linear Neighborhood Propagation Predict Plant LncRNA-Protein Interactions.

Authors:  Lijuan Jia; Yushi Luan
Journal:  Interdiscip Sci       Date:  2022-01-17       Impact factor: 2.233

6.  iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets.

Authors:  Zhen Chen; Xuhan Liu; Pei Zhao; Chen Li; Yanan Wang; Fuyi Li; Tatsuya Akutsu; Chris Bain; Robin B Gasser; Junzhou Li; Zuoren Yang; Xin Gao; Lukasz Kurgan; Jiangning Song
Journal:  Nucleic Acids Res       Date:  2022-05-07       Impact factor: 19.160

7.  DephosSite: a machine learning approach for discovering phosphotase-specific dephosphorylation sites.

Authors:  Xiaofeng Wang; Renxiang Yan; Jiangning Song
Journal:  Sci Rep       Date:  2016-03-22       Impact factor: 4.379

8.  Improving classification of mature microRNA by solving class imbalance problem.

Authors:  Ying Wang; Xiaoye Li; Bairui Tao
Journal:  Sci Rep       Date:  2016-05-16       Impact factor: 4.379

9.  Benchmark data for identifying N(6)-methyladenosine sites in the Saccharomyces cerevisiae genome.

Authors:  Wei Chen; Pengmian Feng; Hui Ding; Hao Lin; Kuo-Chen Chou
Journal:  Data Brief       Date:  2015-09-30

10.  BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions.

Authors:  Jie Dong; Zhi-Jiang Yao; Ming Wen; Min-Feng Zhu; Ning-Ning Wang; Hong-Yu Miao; Ai-Ping Lu; Wen-Bin Zeng; Dong-Sheng Cao
Journal:  J Cheminform       Date:  2016-06-21       Impact factor: 5.514

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