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

By Lei Kong, Yong Zhang, Zhi-Qiang Ye, Xiao-Qiao Liu, Shuqi Zhao, Liping Wei, Ge Gao. Nucleic Acids Res., 2007

2,478 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5bdf59970d48034eb39bc9009af8fe271b60ecba

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Citations in the corpus

Unexpected features of the dark proteome (2015, methodology): “The unexpected features of dark proteins may raise the following question: Are they really proteins? Indeed, some overrepresented Swiss-Prot annotations suggest that a fraction of dark proteins are noncoding (Dataset S2); to examine this, we calculated a coding potential score for each human protein”
LNCipedia: a database for annotated human lncRNA transcript sequences and structures (2012, methodology): “The Coding Potential Calculator (CPC) applies a support vector machine classifier to the output of open reading frame analysis and Basic Local Alignment Search Tool search (22).”
NONCODE v3.0: integrative annotation of long noncoding RNAs (2011, methodology): “Since not all published ncRNAs have undergone detailed experimental analysis, we calculated a coding potential calculator score [CPC score (27)] and Coding Non-Coding Index (CNCI (software in-house)) for each ncRNA to evaluate its coding potential.”
CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features (2017, methodology): “As a response to the challenge, we updated our Coding Potential Calculator (CPC) algorithm (20) to version 2.”
Differentiating Protein-Coding and Noncoding RNA: Challenges and Ambiguities (2008, neutral): “distinguish mRNAs from ncRNAs [48,49].”
FEELnc: a tool for long non-coding RNA annotation and its application to the dog transcriptome (2017, methodology): “We benchmarked FEELnc and five existing programs (PhyloCSF (14), CPC (15), CPAT (16), PLEK (17) and CNCI (18)) using known sets of lncRNAs annotated in multiple organisms (GENCODE for human and mouse (27) and NONCODE for other species (28)), and showed that FEELnc performance metrics outperformed or”
Machine Learning in Agriculture: A Review (2018, background): “Year by year, ML applies in more and more scientific fields including, for example, bioinformatics [2,3], biochemistry [4,5],”
NONCODEv4: exploring the world of long non-coding RNA genes (2013, methodology): “Each transcript in NONCODE is annotated with the following information:(i) basic description, including the ncRNA name, alias, sequence, length, genomic location, coding potential assessment by CPC (27) and CNCI, organisms and references; (ii) biological information, concerning its function, cellula”
lincRNAs: Genomics, Evolution, and Mechanisms (2013, neutral): “, 2011), as indicated; the CPC algorithm, which evaluates ORF properties and similarity to known proteins (Kong et al., 2007); the HMMER algorithm, which tests for potential to encode a known protein domain (Pfam); ribosome profiling, and polyribosome association.”
Genome-wide computational identification and manual annotation of human long noncoding RNA genes. (2010, methodology): “A similar analysis was also carried out for 27,864 coding RefSeq genes, having accession numbers commencing with NM. CPC scores for each transcript are plotted as a continuous distribution in A , and the fractions of transcripts are broken down by classification (our ncRNA genes versus NCBI RefSeq g”