Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk
Abstract Background With the development of sequencing technology, more and more long non-coding RNAs (lncRNAs) have been identified. Some lncRNAs have been confirmed that they play an important role in the process of development through the dosage compensation effect, epigenetic regulation, cell di...
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| Format: | Article |
| Language: | English |
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BMC
2018-11-01
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| Series: | BMC Medical Genomics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12920-018-0414-2 |
| _version_ | 1819068510147444736 |
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| author | Jingpu Zhang shuai Zou Lei Deng |
| author_facet | Jingpu Zhang shuai Zou Lei Deng |
| author_sort | Jingpu Zhang |
| collection | DOAJ |
| description | Abstract Background With the development of sequencing technology, more and more long non-coding RNAs (lncRNAs) have been identified. Some lncRNAs have been confirmed that they play an important role in the process of development through the dosage compensation effect, epigenetic regulation, cell differentiation regulation and other aspects. However, the majority of the lncRNAs have not been functionally characterized. Explore the function of lncRNAs and the regulatory network has become a hot research topic currently. Methods In the work, a network-based model named BiRWLGO is developed. The ultimate goal is to predict the probable functions for lncRNAs at large scale. The new model starts with building a global network composed of three networks: lncRNA similarity network, lncRNA-protein association network and protein-protein interaction (PPI) network. After that, it utilizes bi-random walk algorithm to explore the similarities between lncRNAs and proteins. Finally, we can annotate an lncRNA with the Gene Ontology (GO) terms according to its neighboring proteins. Results We compare the performance of BiRWLGO with the state-of-the-art models on a manually annotated lncRNA benchmark with known GO terms. The experimental results assert that BiRWLGO outperforms other methods in terms of both maximum F-measure (F max ) and coverage. Conclusions BiRWLGO is a relatively efficient method to predict the functions of lncRNA. When protein interaction data is integrated, the predictive performance of BiRWLGO gains a great improvement. |
| first_indexed | 2024-12-21T16:35:17Z |
| format | Article |
| id | doaj.art-1bbee2e59a44469099f470f00f027be0 |
| institution | Directory Open Access Journal |
| issn | 1755-8794 |
| language | English |
| last_indexed | 2024-12-21T16:35:17Z |
| publishDate | 2018-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Genomics |
| spelling | doaj.art-1bbee2e59a44469099f470f00f027be02022-12-21T18:57:15ZengBMCBMC Medical Genomics1755-87942018-11-0111S511010.1186/s12920-018-0414-2Gene Ontology-based function prediction of long non-coding RNAs using bi-random walkJingpu Zhang0shuai Zou1Lei Deng2School of Computer and Data Science, Henan University of Urban ConstructionSchool of Information Science and Engineering, Central South UniversitySchool of Software, Central South UniversityAbstract Background With the development of sequencing technology, more and more long non-coding RNAs (lncRNAs) have been identified. Some lncRNAs have been confirmed that they play an important role in the process of development through the dosage compensation effect, epigenetic regulation, cell differentiation regulation and other aspects. However, the majority of the lncRNAs have not been functionally characterized. Explore the function of lncRNAs and the regulatory network has become a hot research topic currently. Methods In the work, a network-based model named BiRWLGO is developed. The ultimate goal is to predict the probable functions for lncRNAs at large scale. The new model starts with building a global network composed of three networks: lncRNA similarity network, lncRNA-protein association network and protein-protein interaction (PPI) network. After that, it utilizes bi-random walk algorithm to explore the similarities between lncRNAs and proteins. Finally, we can annotate an lncRNA with the Gene Ontology (GO) terms according to its neighboring proteins. Results We compare the performance of BiRWLGO with the state-of-the-art models on a manually annotated lncRNA benchmark with known GO terms. The experimental results assert that BiRWLGO outperforms other methods in terms of both maximum F-measure (F max ) and coverage. Conclusions BiRWLGO is a relatively efficient method to predict the functions of lncRNA. When protein interaction data is integrated, the predictive performance of BiRWLGO gains a great improvement.http://link.springer.com/article/10.1186/s12920-018-0414-2lncRNAFunction annotationBi-random |
| spellingShingle | Jingpu Zhang shuai Zou Lei Deng Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk BMC Medical Genomics lncRNA Function annotation Bi-random |
| title | Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk |
| title_full | Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk |
| title_fullStr | Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk |
| title_full_unstemmed | Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk |
| title_short | Gene Ontology-based function prediction of long non-coding RNAs using bi-random walk |
| title_sort | gene ontology based function prediction of long non coding rnas using bi random walk |
| topic | lncRNA Function annotation Bi-random |
| url | http://link.springer.com/article/10.1186/s12920-018-0414-2 |
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