RNA Coding Potential Prediction Using Alignment-Free Logistic Regression Model.

Szczegóły
Abstrakt

Tytuł:: RNA Coding Potential Prediction Using Alignment-Free Logistic Regression Model.
Autorzy:: Li Y; Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, MN, USA.
Wang L; Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, MN, USA. .; Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, MN, USA. .
Źródło:: Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2021; Vol. 2254, pp. 27-39.
Typ publikacji:: Journal Article
Język:: English
Imprint Name(s):: Publication: Totowa, NJ : Humana Press
Original Publication: Clifton, N.J. : Humana Press,
MeSH Terms:: Sequence Alignment*
Computational Biology/*methods
Open Reading Frames/*genetics
RNA/*genetics
Genome ; Internet ; Logistic Models ; Probability ; Software
References:: Kong L, Zhang Y, Ye ZQ et al (2007) CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res 35:W345–W349. (PMID: 10.1093/nar/gkm391)
Lin MF, Jungreis I, Kellis M (2011) PhyloCSF: a comparative genomics method to distinguish protein coding and non-coding regions. Bioinformatics 27:i275–i282. (PMID: 10.1093/bioinformatics/btr209)
Arrial RT, Togawa RC, Brigido MM (2009) Screening non-coding RNAs in transcriptomes from neglected species using PORTRAIT: case study of the pathogenic fungus Paracoccidioides brasiliensis. BMC Bioinformatics 10:239. (PMID: 10.1186/1471-2105-10-239)
Fickett JW (1982) Recognition of protein coding regions in DNA sequences. Nucleic Acids Res 10:5303–5318. (PMID: 10.1093/nar/10.17.5303)
Fickett JW, Tung C-S (1992) Assessment of protein coding measures. Nucleic Acids Res 20:6441–6450. (PMID: 10.1093/nar/20.24.6441)
Guttman M, Garber M, Levin JZ et al (2010) Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat Biotechnol 28:503–510. (PMID: 10.1038/nbt.1633)
Trapnell C, Williams BA, Pertea G et al (2010) Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28:511–515. (PMID: 10.1038/nbt.1621)
Greiner M (1995) Two-graph receiver operating characteristic (TG-ROC): a Microsoft-EXCEL template for the selection of cutoff values in diagnostic tests. J. Immunol Methods 185:145–146. (PMID: 10.1016/0022-1759(95)00078-O)
Contributed Indexing:: Keywords: LincRNA; LncRNA; Logistic regression; Noncoding RNA; Prediction; Protein coding
Substance Nomenclature:: 63231-63-0 (RNA)
Entry Date(s):: Date Created: 20201216 Date Completed: 20210326 Latest Revision: 20210326
Update Code:: 20240104
DOI:: 10.1007/978-1-0716-1158-6_3
PMID:: 33326068
: Czasopismo naukowe

CPAT (Coding-Potential Assessment Tool) is a logistic regression model-based classifier that can accurately and quickly distinguish protein-coding and noncoding RNAs using pure linguistic features calculated from the RNA sequences. CPAT takes as input the nucleotides sequences or genomic coordinates of RNAs and outputs the probabilities p (0 ≤ p ≤ 1), which measure the likelihood of protein coding. Users can run CPAT online ( http://lilab.research.bcm.edu/cpat/ ) or from the local computers after installation. CPAT provides prebuilt logistic models to recognize RNAs originated from human (Homo sapiens), mouse (Mus musculus), zebrafish (Danio rerio), and fly (Drosophila melanogaster) genomes. Instructions on how to train models for other genomes are described in CPAT website ( http://rna-cpat.sourceforge.net/ ) and this chapter.

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