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This article mainly shows you "what is the use of CPC tools", the content is easy to understand, clear, hope to help you solve your doubts, the following let the editor lead you to study and learn "what is the use of CPC tools" this article.

CPC is a lncRNA prediction tool developed by Peking University. Only by entering the transcript sequence in fasta format, the software can judge and score the protein coding potential of each transcript. According to the score, the transcripts are divided into two categories: coding and non-coding.

Http://cpc2.cbi.pku.edu.cn/

The latest version of the software is CPC2, which runs about 1000 times faster and more accurate than the CPC1 version. The installation method is as follows

Wget http://cpc2.cbi.pku.edu.cn/data/CPC2-beta.tar.gztar xzvf CPC2-beta.tar.gzcd CPC2-beta/export CPC_HOME= "$PWD" cd libs/libsvmtar xzvf libsvm-3.18.tar.gzcd libsvm-3.18make clean & & make

It should be noted that the software relies on the biopython module and needs to be installed first. After the software is installed, you can view the usage of the software with the commands shown in the following figure

The usage is very simple: the-I parameter specifies the output transcript sequence in fasta format, and the-o parameter specifies the name of the output result. Examples are as follows

Python CPC2.py-I transcript.fasta-o output.txt

The contents of the result file are as follows

The above results come out directly from the sequence of human transcripts in RefSeq, and the last column represents the software's classification of transcripts. We can see that some transcripts at the beginning of NR are classified as coding, and some transcripts at the beginning of NM are classified as noncoding, so although the software prediction literature will say that the accuracy is very high, in fact, there are still a lot of misjudgments.

In order to reduce the false positive of lncRNA prediction, in addition to the results predicted by software, it is generally combined with the results of comparison with swissprot protein database and Rfam database to remove transcript sequences that can match the database.

In addition to the local installation, there is also an online service that supports uploading files in Fasta, Bed and GTF formats, as shown below

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