Jiafeng Yu

Dezhou University, Te-ch’ang, Sichuan, China

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Publications (5)11.7 Total impact

  • Source
    Li Guo · Jiafeng Yu · Hao Yu · Yang Zhao · Shujie Chen · Changqing Xu · Feng Chen ·
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    ABSTRACT: We mainly discussed miR-#-5p and miR-#-3p under three aspects: (1) primary evolutionary analysis of human miRNAs; (2) evolutionary analysis of miRNAs from different arms across the typical 10 vertebrates; (3) expression pattern analysis of miRNAs at the miRNA/isomiR levels using public small RNA sequencing datasets. We found that no bias can be detected between the numbers of 5p-miRNA and 3p-miRNA, while miRNAs from miR-#-5p and miR-#-3p show variable nucleotide compositions. IsomiR expression profiles from the two arms are always stable, but isomiR expressions in diseased samples are prone to show larger degree of dispersion. miR-#-5p and miR-#-3p have relative independent evolution/expression patterns and datasets of target mRNAs, which might also contribute to the phenomena of arm selection and/or arm switching. Simultaneously, miRNA/isomiR expression profiles may be regulated via arm selection and/or arm switching, and the dynamic miRNAome and isomiRome will adapt to functional and/or evolutionary pressures. A comprehensive analysis and further experimental study at the miRNA/isomiR levels are quite necessary for miRNA study.
    05/2015; 2015:1-14. DOI:10.1155/2015/168358
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    Tingming Liang · JiaFeng Yu · Chang Liu · Li Guo ·
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    ABSTRACT: The study aims to explore the potential relationships of evolution, maturation, expression and function between homologous/clustered miRNAs. mir-23∼27∼24 gene cluster, including the two gene clusters (mir-23a and mir-23b) and the three miRNA gene families (mir-23, mir-27 and mir-24), was typically selected as an example. These related miRNAs show similar evolutionary patterns and various expression patterns. Most of them show consistent isomiR expression pattern, and the "switching" phenomenon can be found between different abundant isomiR species. These findings suggest that these sequence or location related miRNAs show the similar miRNA processing and maturation processes, and the robust selection of the most dominant isomiR exists in specific tissues. Functional analysis show that these miRNAs show similar distributions of enriched gene categories, suggesting the close functional prelateships via direct or indirect coordinate regulation in biological processes. The study reveals the close evolutionary, expression and functional relationships between related homologous/clustered miRNAs, which will further enrich miRNA studies and understand direct or indirect interactions between miRNAs.
    PLoS ONE 08/2014; 9(8):e106223. DOI:10.1371/journal.pone.0106223 · 3.23 Impact Factor
  • Jihua Wang · Jiafeng Yu · Liling Zhao · Guodong Hu · Zanxia Cao ·

    Journal of biomolecular Structure & Dynamics 02/2013; 31(9). DOI:10.1080/07391102.2012.748527 · 2.92 Impact Factor
  • Source
    Jihua Wang · Zanxia Cao · Jiafeng Yu ·

    Journal of biomolecular Structure & Dynamics 02/2011; 28(4):629-32; discussion 669-674. DOI:10.1080/07391102.2011.10524968 · 2.92 Impact Factor
  • Xin Ma · Jing Guo · Jiansheng Wu · Hongde Liu · Jiafeng Yu · Jianming Xie · Xiao Sun ·
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    ABSTRACT: The identification of RNA-binding residues in proteins is important in several areas such as protein function, posttranscriptional regulation and drug design. We have developed PRBR (Prediction of RNA Binding Residues), a novel method for identifying RNA-binding residues from amino acid sequences. Our method combines a hybrid feature with the enriched random forest (ERF) algorithm. The hybrid feature is composed of predicted secondary structure information and three novel features: evolutionary information combined with conservation information of the physicochemical properties of amino acids and the information about dependency of amino acids with regards to polarity-charge and hydrophobicity in the protein sequences. Our results demonstrate that the PRBR model achieves 0.5637 Matthew's correlation coefficient (MCC) and 88.63% overall accuracy (ACC) with 53.70% sensitivity (SE) and 96.97% specificity (SP). By comparing the performance of each feature we found that all three novel features contribute to the improved predictions. Area under the curve (AUC) statistics from receiver operating characteristic curve analysis was compared between PRBR model and other models. The results show that PRBR achieves the highest AUC value (0.8675) which represents that PRBR attains excellent performance on predicting the RNA-binding residues in proteins. The PRBR web-server implementation is freely available at http://www.cbi.seu.edu.cn/PRBR/.
    Proteins Structure Function and Bioinformatics 12/2010; 79(4):1230-9. DOI:10.1002/prot.22958 · 2.63 Impact Factor

Publication Stats

28 Citations
11.70 Total Impact Points


  • 2011-2015
    • Dezhou University
      Te-ch’ang, Sichuan, China
  • 2010
    • Southeast University (China)
      Nan-ching-hsü, Jiangxi Sheng, China