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This article will explain in detail about the use of CNCI tools, the editor thinks it is very practical, so share it for you to do a reference, I hope you can get something after reading this article.

CPC is a very popular lncRNA forecasting software, but it also has some problems. Using the transcriptome data obtained by the second generation sequencing, the transcripts we assembled are often incomplete, based on the non-full-length transcripts to predict lncRNA, if there is overlap in this lncRNA and protein coding gene, then it is easy to cause misjudgment; secondly, for species without species annotations, the effect is also very poor.

In order to overcome the above problems, the researchers developed a new tool, CNCI, which, unlike CPC, distinguishes coding from noncoding transcripts based on the composition of triplet bases. The paper is published on Nucleic Acids Research.

Https://academic.oup.com/nar/article/41/17/e166/2411728

Triplet bases refer to three consecutive bases, similar to codons, called ANT. The software uses transcripts of human and mouse data to build a support vector machine model for classifying vertebrates, as shown below.

For transcript sequences of different lengths, the performance comparison with other software is as follows

You can see that CNCI performs better in all length ranges. The source code of the software is saved on github at the following URL

Https://github.com/www-bioinfo-org/CNCI

The installation method is as follows

Git clone git@github.com:www-bioinfo-org/CNCI.gitcd CNCIunzip libsvm-3.0.zipcd libsvm-3.0make

The execution script of CNCI is developed in python and can be used directly, but the software depends on libsvm, so you need to install this library file. The basic usage is as follows

Python CNCI.py\-f transcript.fasta\-o test\-m ve\-p 8\

-f specifies the transcribed sequence file, which can be in fasta format or gtf format. In the case of gtf format, you need to specify both-g and-d parameters;-p parameter specifies the number of parallel CPU;-m specifies the model to be used, and ve represents spinal motion.

Object, p represents the plant;-o specifies the directory where the results are output.

Under the results directory, there is a file called CNCI.index, which shows the following

The second column shows the classification results of the transcript. Please refer to the official documentation for more usage and details.

This is the end of this article on "what is the use of CNCI tools". I hope the above content can be of some help to you, so that you can learn more knowledge. if you think the article is good, please share it for more people to see.

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