tRFTars: predicting the targets of tRNA-derived fragments
Abstract Background tRNA-derived fragments (tRFs) are 14–40-nucleotide-long, small non-coding RNAs derived from specific tRNA cleavage events with key regulatory functions in many biological processes. Many studies have shown that tRFs are associated with Argonaute (AGO) complexes and inhibit gene e...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2021-02-01
|
| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-021-02731-7 |
| _version_ | 1818619481806602240 |
|---|---|
| author | Qiong Xiao Peng Gao Xuanzhang Huang Xiaowan Chen Quan Chen Xinger Lv Yu Fu Yongxi Song Zhenning Wang |
| author_facet | Qiong Xiao Peng Gao Xuanzhang Huang Xiaowan Chen Quan Chen Xinger Lv Yu Fu Yongxi Song Zhenning Wang |
| author_sort | Qiong Xiao |
| collection | DOAJ |
| description | Abstract Background tRNA-derived fragments (tRFs) are 14–40-nucleotide-long, small non-coding RNAs derived from specific tRNA cleavage events with key regulatory functions in many biological processes. Many studies have shown that tRFs are associated with Argonaute (AGO) complexes and inhibit gene expression in the same manner as miRNAs. However, there are currently no tools for accurately predicting tRF target genes. Methods We used tRF-mRNA pairs identified by crosslinking, ligation, and sequencing of hybrids (CLASH) and covalent ligation of endogenous AGO-bound RNAs (CLEAR)-CLIP to assess features that may participate in tRF targeting, including the sequence context of each site and tRF-mRNA interactions. We applied genetic algorithm (GA) to select key features and support vector machine (SVM) to construct tRF prediction models. Results We first identified features that globally influenced tRF targeting. Among these features, the most significant were the minimum free folding energy (MFE), position 8 match, number of bases paired in the tRF-mRNA duplex, and length of the tRF, which were consistent with previous findings. Our constructed model yielded an area under the receiver operating characteristic (ROC) curve (AUC) = 0.980 (0.977–0.983) in the training process and an AUC = 0.847 (0.83–0.861) in the test process. The model was applied to all the sites with perfect Watson–Crick complementarity to the seed in the 3′ untranslated region (3′-UTR) of the human genome. Seven of nine target/nontarget genes of tRFs confirmed by reporter assay were predicted. We also validated the predictions via quantitative real-time PCR (qRT-PCR). Thirteen potential target genes from the top of the predictions were significantly down-regulated at the mRNA levels by overexpression of the tRFs (tRF-3001a, tRF-3003a or tRF-3009a). Conclusions Predictions can be obtained online, tRFTars, freely available at http://trftars.cmuzhenninglab.org:3838/tar/ , which is the first tool to predict targets of tRFs in humans with a user-friendly interface. |
| first_indexed | 2024-12-16T17:38:11Z |
| format | Article |
| id | doaj.art-8700feaf58754948a1f6b22aa44fa2c5 |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-12-16T17:38:11Z |
| publishDate | 2021-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-8700feaf58754948a1f6b22aa44fa2c52022-12-21T22:22:41ZengBMCJournal of Translational Medicine1479-58762021-02-0119111510.1186/s12967-021-02731-7tRFTars: predicting the targets of tRNA-derived fragmentsQiong Xiao0Peng Gao1Xuanzhang Huang2Xiaowan Chen3Quan Chen4Xinger Lv5Yu Fu6Yongxi Song7Zhenning Wang8Department of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, The First Affiliated Hospital of China Medical UniversityDepartment of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, The First Affiliated Hospital of China Medical UniversityDepartment of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, The First Affiliated Hospital of China Medical UniversityDepartment of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, The First Affiliated Hospital of China Medical UniversityDepartment of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, The First Affiliated Hospital of China Medical UniversityDepartment of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, The First Affiliated Hospital of China Medical UniversityDepartment of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, The First Affiliated Hospital of China Medical UniversityDepartment of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, The First Affiliated Hospital of China Medical UniversityDepartment of Surgical Oncology and General Surgery, Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, The First Affiliated Hospital of China Medical UniversityAbstract Background tRNA-derived fragments (tRFs) are 14–40-nucleotide-long, small non-coding RNAs derived from specific tRNA cleavage events with key regulatory functions in many biological processes. Many studies have shown that tRFs are associated with Argonaute (AGO) complexes and inhibit gene expression in the same manner as miRNAs. However, there are currently no tools for accurately predicting tRF target genes. Methods We used tRF-mRNA pairs identified by crosslinking, ligation, and sequencing of hybrids (CLASH) and covalent ligation of endogenous AGO-bound RNAs (CLEAR)-CLIP to assess features that may participate in tRF targeting, including the sequence context of each site and tRF-mRNA interactions. We applied genetic algorithm (GA) to select key features and support vector machine (SVM) to construct tRF prediction models. Results We first identified features that globally influenced tRF targeting. Among these features, the most significant were the minimum free folding energy (MFE), position 8 match, number of bases paired in the tRF-mRNA duplex, and length of the tRF, which were consistent with previous findings. Our constructed model yielded an area under the receiver operating characteristic (ROC) curve (AUC) = 0.980 (0.977–0.983) in the training process and an AUC = 0.847 (0.83–0.861) in the test process. The model was applied to all the sites with perfect Watson–Crick complementarity to the seed in the 3′ untranslated region (3′-UTR) of the human genome. Seven of nine target/nontarget genes of tRFs confirmed by reporter assay were predicted. We also validated the predictions via quantitative real-time PCR (qRT-PCR). Thirteen potential target genes from the top of the predictions were significantly down-regulated at the mRNA levels by overexpression of the tRFs (tRF-3001a, tRF-3003a or tRF-3009a). Conclusions Predictions can be obtained online, tRFTars, freely available at http://trftars.cmuzhenninglab.org:3838/tar/ , which is the first tool to predict targets of tRFs in humans with a user-friendly interface.https://doi.org/10.1186/s12967-021-02731-7tRNA derived fragmentsCrosslinking, ligation and sequencing of hybridsFeatures of tRF targetingSupport vector machineThe first tRF target predicting tool |
| spellingShingle | Qiong Xiao Peng Gao Xuanzhang Huang Xiaowan Chen Quan Chen Xinger Lv Yu Fu Yongxi Song Zhenning Wang tRFTars: predicting the targets of tRNA-derived fragments Journal of Translational Medicine tRNA derived fragments Crosslinking, ligation and sequencing of hybrids Features of tRF targeting Support vector machine The first tRF target predicting tool |
| title | tRFTars: predicting the targets of tRNA-derived fragments |
| title_full | tRFTars: predicting the targets of tRNA-derived fragments |
| title_fullStr | tRFTars: predicting the targets of tRNA-derived fragments |
| title_full_unstemmed | tRFTars: predicting the targets of tRNA-derived fragments |
| title_short | tRFTars: predicting the targets of tRNA-derived fragments |
| title_sort | trftars predicting the targets of trna derived fragments |
| topic | tRNA derived fragments Crosslinking, ligation and sequencing of hybrids Features of tRF targeting Support vector machine The first tRF target predicting tool |
| url | https://doi.org/10.1186/s12967-021-02731-7 |
| work_keys_str_mv | AT qiongxiao trftarspredictingthetargetsoftrnaderivedfragments AT penggao trftarspredictingthetargetsoftrnaderivedfragments AT xuanzhanghuang trftarspredictingthetargetsoftrnaderivedfragments AT xiaowanchen trftarspredictingthetargetsoftrnaderivedfragments AT quanchen trftarspredictingthetargetsoftrnaderivedfragments AT xingerlv trftarspredictingthetargetsoftrnaderivedfragments AT yufu trftarspredictingthetargetsoftrnaderivedfragments AT yongxisong trftarspredictingthetargetsoftrnaderivedfragments AT zhenningwang trftarspredictingthetargetsoftrnaderivedfragments |