Literature DB >> 33313647

Prediction of bio-sequence modifications and the associations with diseases.

Chunyan Ao, Liang Yu, Quan Zou.   

Abstract

Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identification and comprehensive understanding of protein, RNA and DNA modification sites can promote research on disease treatment and prevention. With the development of sequencing technology, the number of known sequences has continued to increase. In the past decade, many computational tools that can be used to predict protein, RNA and DNA modification sites have been developed. In this review, we comprehensively summarized the modification site predictors for three different biological sequences and the association with diseases. The relevant web server is accessible at http://lab.malab.cn/∼acy/PTM_data/ some sample data on protein, RNA and DNA modification can be downloaded from that website.
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Keywords:  bio-sequence modifications; diseases; machine learning; prediction tool

Year:  2020        PMID: 33313647     DOI: 10.1093/bfgp/elaa023

Source DB:  PubMed          Journal:  Brief Funct Genomics        ISSN: 2041-2649            Impact factor:   4.241


  16 in total

1.  ATGPred-FL: sequence-based prediction of autophagy proteins with feature representation learning.

Authors:  Shihu Jiao; Zheng Chen; Lichao Zhang; Xun Zhou; Lei Shi
Journal:  Amino Acids       Date:  2022-03-14       Impact factor: 3.520

2.  Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation.

Authors:  Daiyun Huang; Kunqi Chen; Bowen Song; Zhen Wei; Jionglong Su; Frans Coenen; João Pedro de Magalhães; Daniel J Rigden; Jia Meng
Journal:  Nucleic Acids Res       Date:  2022-10-14       Impact factor: 19.160

Review 3.  Role of Posttranslational Modifications of Proteins in Cardiovascular Disease.

Authors:  Yong-Ping Liu; Tie-Ning Zhang; Ri Wen; Chun-Feng Liu; Ni Yang
Journal:  Oxid Med Cell Longev       Date:  2022-07-09       Impact factor: 7.310

4.  pSuc-FFSEA: Predicting Lysine Succinylation Sites in Proteins Based on Feature Fusion and Stacking Ensemble Algorithm.

Authors:  Jianhua Jia; Genqiang Wu; Wangren Qiu
Journal:  Front Cell Dev Biol       Date:  2022-05-24

5.  Identification of Diagnostic Markers for Breast Cancer Based on Differential Gene Expression and Pathway Network.

Authors:  Shumei Zhang; Haoran Jiang; Bo Gao; Wen Yang; Guohua Wang
Journal:  Front Cell Dev Biol       Date:  2022-01-12

6.  Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D.

Authors:  Xiaomei Gu; Lina Guo; Bo Liao; Qinghua Jiang
Journal:  Front Genet       Date:  2021-12-01       Impact factor: 4.599

7.  Deep-4mCGP: A Deep Learning Approach to Predict 4mC Sites in Geobacter pickeringii by Using Correlation-Based Feature Selection Technique.

Authors:  Hasan Zulfiqar; Qin-Lai Huang; Hao Lv; Zi-Jie Sun; Fu-Ying Dao; Hao Lin
Journal:  Int J Mol Sci       Date:  2022-01-23       Impact factor: 5.923

8.  KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest.

Authors:  Yuran Jia; Shan Huang; Tianjiao Zhang
Journal:  Front Genet       Date:  2021-11-29       Impact factor: 4.599

9.  Identification of Disease-Related 2-Oxoglutarate/Fe (II)-Dependent Oxygenase Based on Reduced Amino Acid Cluster Strategy.

Authors:  Jian Zhou; Suling Bo; Hao Wang; Lei Zheng; Pengfei Liang; Yongchun Zuo
Journal:  Front Cell Dev Biol       Date:  2021-07-16

10.  Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features.

Authors:  Mujiexin Liu; Hui Chen; Dong Gao; Cai-Yi Ma; Zhao-Yue Zhang
Journal:  Comput Math Methods Med       Date:  2022-01-12       Impact factor: 2.238

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