CPPred is available:

CPPred(CPPred.tar.gz,3.10M)

Training sets:

Human-Training: coding RNAs(33360,117M)+ noncoding RNAs(24163,24M)

Integrate-Training: coding RNAs(52530,112M)+ noncoding RNAs(27600,18M)

Testing sets:

Human-Testing: 8557 coding RNAs+ 8241 noncoding RNAs

Human-sORF-Testing: 641 coding RNAs+ 641 noncoding RNAs

Mouse-Testing: 31102 coding RNAs+ 19930 noncoding RNAs

Mouse-sORF-Testing: 846 coding RNAs+ 1000 noncoding RNAs

Zebrafish-Testing: 15594 coding RNAs+ 10662 noncoding RNAs

Zebrafish-sORF-Testing: 387 coding RNAs+ 500 noncoding RNAs

Fruit-fly-Testing: 17400 coding RNAs+ 4098 noncoding RNAs

Fruit-fly-sORF-Testing: 381 coding RNAs+ 381 noncoding RNAs

S.cerevisiae-Testing: 6713 coding RNAs+ 413 noncoding RNAs

S.cerevisiae-sORF-Testing: 505 coding RNAs+ 413 noncoding RNAs

Integrate-Testing: 13903 coding RNAs+ 13903 noncoding RNAs

Integrate-sORF-Testing: 11634 coding RNAs+ 11634 noncoding RNAs

Uncompress and usage of CPPred:

1. Install the biopython.

  pip install biopython==1.75 --user

2. Download the CPPred package.

(1) Type "tar -zxvf CPPred.tar.gz" to uncompress the package

(2) Type "cd CPPred/bin" to change the current directory

(3) Run "python CPPred.py -i input_RNA.fa -hex Hexamer.tsv -r range -m model -spe species -o result" to predict. Here, "input_RNA.fa" is RNAs file in FASTA format. "Hexamer.tsv" is a pre-built hexamer frequency table. "range" is pre-built training range file. "model" is pre-built training model. "species" is the model of the species to choose (Human,Integrated). The "result" file in it is the final result for each prediction.

Example :

If your data is a single species, use the example below:

python CPPred.py -i ../data/Human_coding_RNA_test.fa -hex ../Hexamer/Human_Hexamer.tsv -r ../Human_Model/Human.range -mol ../Human_Model/Human.model -spe Human -o Human_coding.result

python CPPred.py -i ../data/Human_ncRNA_test.fa -hex ../Hexamer/Human_Hexamer.tsv -r ../Human_Model/Human.range -mol ../Human_Model/Human.model -spe Human -o Human_ncRNA.result

python CPPred.py -i ../data/Mouse(Zebrafish,S.cerevisiae or Fruit_fly)_coding_RNA_test.fa -hex ../Hexamer/Human_Hexamer.tsv -r ../Human_Model/Human.range -mol ../Human_Model/Human.model -spe Human -o Mouse(Zebrasfish,S.cerevisiae or Fruit_fly)_coding.result

python CPPred.py -i ../data/Mouse(Zebrafish,S.cerevisiae or Fruit_fly)_ncRNA_test.fa -hex ../Hexamer/Human_Hexamer.tsv -r ../Human_Model/Human.range -mol ../Human_Model/Human.model -spe Human -o Mouse(Zebrasfish,S.cerevisiae or Fruit_fly)_ncRNA.result

If your data contains multiple species, use the example below:

python CPPred.py -i ../data/Integrated_coding_RNA_test.fa -hex ../Hexamer/Integrated_Hexamer.tsv -r ../Integrated_Model/Integrated.range -mol ../Integrated_Model/Integrated.model -spe Integrated -o Integrated_coding.result

python CPPred.py -i ../data/Integrated_ncRNA_test.fa -hex ../Hexamer/Integrated_Hexamer.tsv -r ../Integrated_Model/Integrated.range -mol ../Integrated_Model/Integrated.model -spe Integrated -o Integrated_ncRNA.result

Program and modules connected with CPPred:

LIBSVM: A Library of Support Vector Machines (Version 3.22, December 2016). It is downloaded from http://www.csie.ntu.edu.tw/~cjlin/cgi-bin/libsvm.cgi?+http://www.csie.ntu.edu.tw/~cjlin/libsvm+tar.gz. For more information, please see https://www.csie.ntu.edu.tw/~cjlin/libsvm/

FrameKmer.py and fickett.py: The python scripts of CPAT (Version 1.2.2) are downloaded from https://sourceforge.net/projects/rna-cpat/files/v1.2.2/. For more information, please see http://rna-cpat.sourceforge.net/

Contact us:

Any questions about CPPred, please email to liushiyong@gmail.com.

Reference:

If you use CPPred, please cite:

1. Xiaoxue Tong and Shiyong Liu. CPPred: Coding Potential Prediction based on the global description of RNA sequence. Nucleic Acids Research, 1 February 2019 [PubMed] [PDF]

Last modified: Tues. Feb. 12 17:00:00 CST 2019