Advances in next generation sequencing technologies have detected numerous synonymous mutations in the human genome that do not alter amino acids. In recent years, much work have pointed out the important role of synonymous mutations in many human diseases, including psychiatric disease, congenital heart disease, and cancer. However, it is difficult to distinguish disease-associated from benign synonymous mutations. Experimental characterization of all identified synonymous mutations is unpractical and usually time-consuming, costly and labor intensive. Consequently, the scientific community needs computational methods to highlight the most likely deleterious synonymous mutations.
In this study, we develop an ensemble predictor PrDSM (v1.0) by conducting a comprehensive performance evaluation of existing tools for predicting deleterious synonymous mutations. Independent tests demonstrate that the ensemble predictor outperform the current tools.
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PrDSM supports prediction of synonymous mutations in the GRCh37/hg19 assembly of the human genome
Insert the list of synonymous mutations using the tab separated values format chr, pos, id, ref, alt (maximum 5,000 mutations) Example
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PrDSM, the pre-computed score is available on here.
Na Cheng, Menglu Li, Le Zhao, Bo Zhang, Yuhua Yang, Chun-Hou Zheng and Junfeng Xia*, Comparison and integration of computational methods for deleterious synonymous mutation prediction[J]. Briefings in Bioinformatics, 2020, 21(3): 970-981.
If you have any problem with the website, please contact Junfeng Xia: jfxia@ahu.edu.cn
Note:PrDSM is intended for research purposes only. Do not use the results to make clinical decisions.