Literature DB >> 25958395

Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences.

Bin Liu1, Fule Liu2, Xiaolong Wang3, Junjie Chen2, Longyun Fang2, Kuo-Chen Chou4.   

Abstract

With the avalanche of biological sequences generated in the post-genomic age, one of the most challenging problems in computational biology is how to effectively formulate the sequence of a biological sample (such as DNA, RNA or protein) with a discrete model or a vector that can effectively reflect its sequence pattern information or capture its key features concerned. Although several web servers and stand-alone tools were developed to address this problem, all these tools, however, can only handle one type of samples. Furthermore, the number of their built-in properties is limited, and hence it is often difficult for users to formulate the biological sequences according to their desired features or properties. In this article, with a much larger number of built-in properties, we are to propose a much more flexible web server called Pse-in-One (http://bioinformatics.hitsz.edu.cn/Pse-in-One/), which can, through its 28 different modes, generate nearly all the possible feature vectors for DNA, RNA and protein sequences. Particularly, it can also generate those feature vectors with the properties defined by users themselves. These feature vectors can be easily combined with machine-learning algorithms to develop computational predictors and analysis methods for various tasks in bioinformatics and system biology. It is anticipated that the Pse-in-One web server will become a very useful tool in computational proteomics, genomics, as well as biological sequence analysis. Moreover, to maximize users' convenience, its stand-alone version can also be downloaded from http://bioinformatics.hitsz.edu.cn/Pse-in-One/download/, and directly run on Windows, Linux, Unix and Mac OS.
© The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Entities:  

Mesh:

Year:  2015        PMID: 25958395      PMCID: PMC4489303          DOI: 10.1093/nar/gkv458

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


  26 in total

1.  Predicting the in vivo signature of human gene regulatory sequences.

Authors:  William Stafford Noble; Scott Kuehn; Robert Thurman; Man Yu; John Stamatoyannopoulos
Journal:  Bioinformatics       Date:  2005-06       Impact factor: 6.937

2.  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

3.  repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects.

Authors:  Bin Liu; Fule Liu; Longyun Fang; Xiaolong Wang; Kuo-Chen Chou
Journal:  Bioinformatics       Date:  2014-12-10       Impact factor: 6.937

4.  iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition.

Authors:  Shou-Hui Guo; En-Ze Deng; Li-Qin Xu; Hui Ding; Hao Lin; Wei Chen; Kuo-Chen Chou
Journal:  Bioinformatics       Date:  2014-02-06       Impact factor: 6.937

5.  PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition.

Authors:  Hong-Bin Shen; Kuo-Chen Chou
Journal:  Anal Biochem       Date:  2007-10-13       Impact factor: 3.365

6.  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

7.  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

8.  A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis.

Authors:  Bin Liu; Xiaolong Wang; Lei Lin; Qiwen Dong; Xuan Wang
Journal:  BMC Bioinformatics       Date:  2008-12-01       Impact factor: 3.169

9.  iSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition.

Authors:  Yan Xu; Jun Ding; Ling-Yun Wu; Kuo-Chen Chou
Journal:  PLoS One       Date:  2013-02-07       Impact factor: 3.240

10.  Predicting human nucleosome occupancy from primary sequence.

Authors:  Shobhit Gupta; Jonathan Dennis; Robert E Thurman; Robert Kingston; John A Stamatoyannopoulos; William Stafford Noble
Journal:  PLoS Comput Biol       Date:  2008-08-22       Impact factor: 4.475

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

Authors:  Bin Liu; Fule Liu; Longyun Fang; Xiaolong Wang; Kuo-Chen Chou
Journal:  Mol Genet Genomics       Date:  2015-06-18       Impact factor: 3.291

3.  ProtDCal-Suite: A web server for the numerical codification and functional analysis of proteins.

Authors:  Sandra Romero-Molina; Yasser B Ruiz-Blanco; James R Green; Elsa Sanchez-Garcia
Journal:  Protein Sci       Date:  2019-09       Impact factor: 6.725

4.  PyFeat: a Python-based effective feature generation tool for DNA, RNA and protein sequences.

Authors:  Rafsanjani Muhammod; Sajid Ahmed; Dewan Md Farid; Swakkhar Shatabda; Alok Sharma; Abdollah Dehzangi
Journal:  Bioinformatics       Date:  2019-10-01       Impact factor: 6.937

5.  iN6-methylat (5-step): identifying DNA N6-methyladenine sites in rice genome using continuous bag of nucleobases via Chou's 5-step rule.

Authors:  Nguyen Quoc Khanh Le
Journal:  Mol Genet Genomics       Date:  2019-05-04       Impact factor: 3.291

6.  An information-based network approach for protein classification.

Authors:  Xiaogeng Wan; Xin Zhao; Stephen S T Yau
Journal:  PLoS One       Date:  2017-03-28       Impact factor: 3.240

7.  Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC.

Authors:  Ahmad Hassan Butt; Nouman Rasool; Yaser Daanial Khan
Journal:  Mol Biol Rep       Date:  2018-09-20       Impact factor: 2.316

8.  Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework.

Authors:  Fuyi Li; Jinxiang Chen; Zongyuan Ge; Ya Wen; Yanwei Yue; Morihiro Hayashida; Abdelkader Baggag; Halima Bensmail; Jiangning Song
Journal:  Brief Bioinform       Date:  2021-03-22       Impact factor: 11.622

9.  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

10.  Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.

Authors:  Zhen Chen; Xuhan Liu; Fuyi Li; Chen Li; Tatiana Marquez-Lago; André Leier; Tatsuya Akutsu; Geoffrey I Webb; Dakang Xu; Alexander Ian Smith; Lei Li; Kuo-Chen Chou; Jiangning Song
Journal:  Brief Bioinform       Date:  2019-11-27       Impact factor: 11.622

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.