CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model
By Liguo Wang, H. Park, S. Dasari, Shengqin Wang, Jean-Pierre A. Kocher, Wei Li. Nucleic Acids Research, 2013
1,734 citations recorded by the source corpus.
Thousands of novel transcripts have been identified using deep transcriptome sequencing. This discovery of large and ‘hidden’ transcriptome rejuvenates the demand for methods that can rapidly distinguish between coding and noncoding RNA. Here, we present a novel alignment-free method, Coding Potential Assessment Tool (CPAT), which rapidly recognizes coding and noncoding transcripts from a large pool of candidates. To this end, CPAT uses a logistic regression model built with four sequence features: open reading frame size, open reading frame coverage, Fickett TESTCODE statistic and hexamer usage bias. CPAT software outperformed (sensitivity: 0.96, specificity: 0.97) other state-of-the-art alignment-based software such as Coding-Potential Calculator (sensitivity: 0.99, specificity: 0.74) and Phylo Codon Substitution Frequencies (sensitivity: 0.90, specificity: 0.63). In addition to high accuracy, CPAT is approximately four orders of magnitude faster than Coding-Potential Calculator and Phylo Codon Substitution Frequencies, enabling its users to process thousands of transcripts within seconds. The software accepts input sequences in either FASTA- or BED-formatted data files. We also developed a web interface for CPAT that allows users to submit sequences and receive the prediction results almost instantly.
Subfields: Cancer Research, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/eb9cfe9fca91d60ff5d0488e980bb34e03ced11f
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Citations in the corpus
- CCAT2, a novel noncoding RNA mapping to 8q24, underlies metastatic progression and chromosomal instability in colon cancer (2013, methodology): “…noncoding RNA (Lin et al. 2011), lack of coding po- tential determined by coding potential assessment tool (CPAT; http://lilab.research.bcm.edu/cpat/) (Wang et al. 2013), and neg- ative data from the small-peptide database (Pepbank; http:// pepbank.mgh.harvard.edu/search/blast) (Supplemental Fig.”
- 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”
- CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features (2017, methodology): “The LIBSVM (23) package was employed to train an SVM model using the standard radial basis function kernel (RBF kernel) with the training dataset containing 17 984 high-confident human protein-coding transcripts and 10 452 non-coding transcripts (18).”
- Circbank: a comprehensive database for circRNA with standard nomenclature (2019, methodology): “To assess the protein-coding potential of the circRNAs, we used CPAT (Coding-Potential Assessment Tool) [34]to calculate the coding potential score of each human circRNA.”