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CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine

Lei KongCenter for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University, Beijing 100871, PR ChinaYong ZhangCenter for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University, Beijing 100871, P. R. ChinaZhiqiang YeCenter for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University, Beijing 100871, P. R. ChinaXiao LiuCenter for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University, Beijing 100871, P. R. ChinaShu-Qi ZhaoCenter for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University, Beijing 100871, P. R. ChinaLiping WeiCenter for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University, Beijing 100871, P. R. ChinaGe GaoCenter for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University, Beijing 100871, P. R. China

2007en

ABI

Annotatsiya

Recent transcriptome studies have revealed that a large number of transcripts in mammals and other organisms do not encode proteins but function as noncoding RNAs (ncRNAs) instead. As millions of transcripts are generated by large-scale cDNA and EST sequencing projects every year, there is a need for automatic methods to distinguish protein-coding RNAs from noncoding RNAs accurately and quickly. We developed a support vector machine-based classifier, named Coding Potential Calculator (CPC), to assess the protein-coding potential of a transcript based on six biologically meaningful sequence features. Tenfold cross-validation on the training dataset and further testing on several large datasets showed that CPC can discriminate coding from noncoding transcripts with high accuracy. Furthermore, CPC also runs an order-of-magnitude faster than a previous state-of-the-art tool and has higher accuracy. We developed a user-friendly web-based interface of CPC at http://cpc.cbi.pku.edu.cn. In addition to predicting the coding potential of the input transcripts, the CPC web server also graphically displays detailed sequence features and additional annotations of the transcript that may facilitate users' further investigation.

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DOI: 10.1093/nar/gkm391

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