CPPVec: an accurate coding potential predictor based on a distributed representation of protein sequence
Abstract Long non-coding RNAs (lncRNAs) play a crucial role in numbers of biological processes and have received wide attention during the past years. Since the rapid development of high-throughput transcriptome sequencing technologies (RNA-seq) lead to a large amount of RNA data, it is urgent to de...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-05-01
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| Series: | BMC Genomics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12864-023-09365-7 |
| _version_ | 1827943557868027904 |
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| author | Chao Wei Zhiwei Ye Junying Zhang Aimin Li |
| author_facet | Chao Wei Zhiwei Ye Junying Zhang Aimin Li |
| author_sort | Chao Wei |
| collection | DOAJ |
| description | Abstract Long non-coding RNAs (lncRNAs) play a crucial role in numbers of biological processes and have received wide attention during the past years. Since the rapid development of high-throughput transcriptome sequencing technologies (RNA-seq) lead to a large amount of RNA data, it is urgent to develop a fast and accurate coding potential predictor. Many computational methods have been proposed to address this issue, they usually exploit information on open reading frame (ORF), protein sequence, k-mer, evolutionary signatures, or homology. Despite the effectiveness of these approaches, there is still much room to improve. Indeed, none of these methods exploit the contextual information of RNA sequence, for example, k-mer features that counts the occurrence frequencies of continuous nucleotides (k-mer) in the whole RNA sequence cannot reflect local contextual information of each k-mer. In view of this shortcoming, here, we present a novel alignment-free method, CPPVec, which exploits the contextual information of RNA sequence for coding potential prediction for the first time, it can be easily implemented by distributed representation (e.g., doc2vec) of protein sequence translated from the longest ORF. The experimental findings demonstrate that CPPVec is an accurate coding potential predictor and significantly outperforms existing state-of-the-art methods. |
| first_indexed | 2024-03-13T10:17:13Z |
| format | Article |
| id | doaj.art-dc46449e2321411ab68dc753e455e5a6 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-03-13T10:17:13Z |
| publishDate | 2023-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-dc46449e2321411ab68dc753e455e5a62023-05-21T11:09:39ZengBMCBMC Genomics1471-21642023-05-012411910.1186/s12864-023-09365-7CPPVec: an accurate coding potential predictor based on a distributed representation of protein sequenceChao Wei0Zhiwei Ye1Junying Zhang2Aimin Li3School of Computer Science, Hubei University of TechnologySchool of Computer Science, Hubei University of TechnologySchool of Computer Science and Technology, Xidian UniversitySchool of Computer Science and Engineering, Xi’an University of TechnologyAbstract Long non-coding RNAs (lncRNAs) play a crucial role in numbers of biological processes and have received wide attention during the past years. Since the rapid development of high-throughput transcriptome sequencing technologies (RNA-seq) lead to a large amount of RNA data, it is urgent to develop a fast and accurate coding potential predictor. Many computational methods have been proposed to address this issue, they usually exploit information on open reading frame (ORF), protein sequence, k-mer, evolutionary signatures, or homology. Despite the effectiveness of these approaches, there is still much room to improve. Indeed, none of these methods exploit the contextual information of RNA sequence, for example, k-mer features that counts the occurrence frequencies of continuous nucleotides (k-mer) in the whole RNA sequence cannot reflect local contextual information of each k-mer. In view of this shortcoming, here, we present a novel alignment-free method, CPPVec, which exploits the contextual information of RNA sequence for coding potential prediction for the first time, it can be easily implemented by distributed representation (e.g., doc2vec) of protein sequence translated from the longest ORF. The experimental findings demonstrate that CPPVec is an accurate coding potential predictor and significantly outperforms existing state-of-the-art methods.https://doi.org/10.1186/s12864-023-09365-7Coding potential predictionDistributed representationContextual information |
| spellingShingle | Chao Wei Zhiwei Ye Junying Zhang Aimin Li CPPVec: an accurate coding potential predictor based on a distributed representation of protein sequence BMC Genomics Coding potential prediction Distributed representation Contextual information |
| title | CPPVec: an accurate coding potential predictor based on a distributed representation of protein sequence |
| title_full | CPPVec: an accurate coding potential predictor based on a distributed representation of protein sequence |
| title_fullStr | CPPVec: an accurate coding potential predictor based on a distributed representation of protein sequence |
| title_full_unstemmed | CPPVec: an accurate coding potential predictor based on a distributed representation of protein sequence |
| title_short | CPPVec: an accurate coding potential predictor based on a distributed representation of protein sequence |
| title_sort | cppvec an accurate coding potential predictor based on a distributed representation of protein sequence |
| topic | Coding potential prediction Distributed representation Contextual information |
| url | https://doi.org/10.1186/s12864-023-09365-7 |
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