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Yu J, Jiang W, Zhu SB, Liao Z, Dou X, Liu J, Guo FB, Dong C. Prediction of protein-coding small ORFs in multi-species using integrated sequence-derived features and the random forest model. Methods 2023; 210:10-19. [PMID: 36621557 DOI: 10.1016/j.ymeth.2022.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/27/2022] [Accepted: 12/30/2022] [Indexed: 01/07/2023] Open
Abstract
Proteins encoded by small open reading frames (sORFs) can serve as functional elements playing important roles in vivo. Such sORFs also constitute the potential pool for facilitating the de novo gene birth, driving evolutionary innovation and species diversity. Therefore, their theoretical and experimental identification has become a critical issue. Herein, we proposed a protein-coding sORFs prediction method merely based on integrative sequence-derived features. Our prediction performance is better or comparable compared with other nine prevalent methods, which shows that our method can provide a relatively reliable research tool for the prediction of protein-coding sORFs. Our method allows users to estimate the potential expression of a queried sORF, which has been demonstrated by the correlation analysis between our possibility estimation and codon adaption index (CAI). Based on the features that we used, we demonstrated that the sequence features of the protein-coding sORFs in the two domains have significant differences implying that it might be a relatively hard task in terms of cross-domain prediction, hence domain-specific models were developed, which allowed users to predict protein-coding sORFs both in eukaryotes and prokaryotes. Finally, a web-server was developed and provided to boost and facilitate the study of the related field, which is freely available at http://guolab.whu.edu.cn/codingCapacity/index.html.
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Affiliation(s)
- Jiafeng Yu
- Shandong Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou 253023, China
| | - Wenwen Jiang
- Department of Bioinformatics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Sen-Bin Zhu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Zhen Liao
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xianghua Dou
- Shandong Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou 253023, China
| | - Jian Liu
- Shandong Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou 253023, China
| | - Feng-Biao Guo
- School of Pharmaceutical Sciences, Wuhan University, Wuhan 430071, China.
| | - Chuan Dong
- School of Pharmaceutical Sciences, Wuhan University, Wuhan 430071, China.
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2
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A Versatile Toolset for Genetic Manipulation of the Wine Yeast Hanseniaspora uvarum. Int J Mol Sci 2023; 24:ijms24031859. [PMID: 36768181 PMCID: PMC9915424 DOI: 10.3390/ijms24031859] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 01/19/2023] Open
Abstract
Hanseniaspora uvarum is an ascomycetous yeast that frequently dominates the population in the first two days of wine fermentations. It contributes to the production of many beneficial as well as detrimental aroma compounds. While the genome sequence of the diploid type strain DSM 2768 has been largely elucidated, transformation by electroporation was only recently achieved. We here provide an elaborate toolset for the genetic manipulation of this yeast. A chromosomal replication origin was isolated and used for the construction of episomal, self-replicating cloning vectors. Moreover, homozygous auxotrophic deletion markers (Huura3, Huhis3, Huleu2, Huade2) have been obtained in the diploid genome as future recipients and a proof of principle for the application of PCR-based one-step gene deletion strategies. Besides a hygromycin resistance cassette, a kanamycin resistance gene was established as a dominant marker for selection on G418. Recyclable deletion cassettes flanked by loxP-sites and the corresponding Cre-recombinase expression vectors were tailored. Moreover, we report on a chemical transformation procedure with the use of freeze-competent cells. Together, these techniques and constructs pave the way for efficient and targeted manipulations of H. uvarum.
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Genome Report of Emergent Fish Pathogen
Edwardsiella piscicida
Recovered from
Pseudoplatystoma corruscans
in Brazil. Microbiol Resour Announc 2022; 11:e0079622. [DOI: 10.1128/mra.00796-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Edwardsiella piscicida
is a Gram-negative bacteria belonging to the
Hafniaceae
family which affects several species of marine and freshwater fish. We present the complete genome of
E
.
piscicida
strain BEP80 recovered from the Brazilian catfish named Surubim (
Pseudoplatystoma corruscans
), consisting a chromosome of 3,883,256 bp and no plasmids.
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Dao FY, Lv H, Fullwood MJ, Lin H. Accurate Identification of DNA Replication Origin by Fusing Epigenomics and Chromatin Interaction Information. RESEARCH (WASHINGTON, D.C.) 2022; 2022:9780293. [PMID: 36405252 PMCID: PMC9667886 DOI: 10.34133/2022/9780293] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 09/30/2022] [Indexed: 07/29/2023]
Abstract
DNA replication initiation is a complex process involving various genetic and epigenomic signatures. The correct identification of replication origins (ORIs) could provide important clues for the study of a variety of diseases caused by replication. Here, we design a computational approach named iORI-Epi to recognize ORIs by incorporating epigenome-based features, sequence-based features, and 3D genome-based features. The iORI-Epi displays excellent robustness and generalization ability on both training datasets and independent datasets of K562 cell line. Further experiments confirm that iORI-Epi is highly scalable in other cell lines (MCF7 and HCT116). We also analyze and clarify the regulatory role of epigenomic marks, DNA motifs, and chromatin interaction in DNA replication initiation of eukaryotic genomes. Finally, we discuss gene enrichment pathways from the perspective of ORIs in different replication timing states and heuristically dissect the effect of promoters on replication initiation. Our computational methodology is worth extending to ORI identification in other eukaryotic species.
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Affiliation(s)
- Fu-Ying Dao
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
- School of Biological Sciences, Nanyang Technological University, Singapore 639798, Singapore
- Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore 117599, Singapore
| | - Hao Lv
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
- Department of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Melissa J. Fullwood
- School of Biological Sciences, Nanyang Technological University, Singapore 639798, Singapore
- Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore 117599, Singapore
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A∗STAR), Singapore 138673, Singapore
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
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Dong MJ, Luo H, Gao F. Ori-Finder 2022: A Comprehensive Web Server for Prediction and Analysis of Bacterial Replication Origins. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:1207-1213. [PMID: 36257484 DOI: 10.1016/j.gpb.2022.10.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 09/21/2022] [Accepted: 10/11/2022] [Indexed: 12/26/2022]
Abstract
The replication of DNA is a complex biological process that is essential for life. Bacterial DNA replication is initiated at genomic loci referred to as replication origins (oriCs). Integrating the Z-curve method, DnaA box distribution, and comparative genomic analysis, we developed a web server to predict bacterial oriCs in 2008 called Ori-Finder, which contributes to clarify the characteristics of bacterial oriCs. The oriCs of hundreds of sequenced bacterial genomes have been annotated in the genome reports using Ori-Finder and the predicted results have been deposited in DoriC, a manually curated database of oriCs. This has facilitated large-scale data mining of functional elements in oriCs and strand-biased analysis. Here, we describe Ori-Finder 2022 with updated prediction framework, interactive visualization module, new analysis module, and user-friendly interface. More species-specific indicator genes and functional elements of oriCs are integrated into the updated framework, which has also been redesigned to predict oriCs in draft genomes. The interactive visualization module displays more genomic information related to oriCs and their functional elements. The analysis module includes regulatory protein annotation, repeat sequence discovery, homologous oriC search, and strand-biased analyses. The redesigned interface provides additional customization options for oriC prediction. Ori-Finder 2022 is freely available at http://tubic.tju.edu.cn/Ori-Finder/ and https://tubic.org/Ori-Finder/.
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Affiliation(s)
- Mei-Jing Dong
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China
| | - Hao Luo
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China
| | - Feng Gao
- Department of Physics, School of Science, Tianjin University, Tianjin 300072, China; Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin 300072, China; SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300072, China.
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6
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Wu F, Yang R, Zhang C, Zhang L. A deep learning framework combined with word embedding to identify DNA replication origins. Sci Rep 2021; 11:844. [PMID: 33436981 PMCID: PMC7804333 DOI: 10.1038/s41598-020-80670-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 12/24/2020] [Indexed: 01/29/2023] Open
Abstract
The DNA replication influences the inheritance of genetic information in the DNA life cycle. As the distribution of replication origins (ORIs) is the major determinant to precisely regulate the replication process, the correct identification of ORIs is significant in giving an insightful understanding of DNA replication mechanisms and the regulatory mechanisms of genetic expressions. For eukaryotes in particular, multiple ORIs exist in each of their gene sequences to complete the replication in a reasonable period of time. To simplify the identification process of eukaryote's ORIs, most of existing methods are developed by traditional machine learning algorithms, and target to the gene sequences with a fixed length. Consequently, the identification results are not satisfying, i.e. there is still great room for improvement. To break through the limitations in previous studies, this paper develops sequence segmentation methods, and employs the word embedding technique, 'Word2vec', to convert gene sequences into word vectors, thereby grasping the inner correlations of gene sequences with different lengths. Then, a deep learning framework to perform the ORI identification task is constructed by a convolutional neural network with an embedding layer. On the basis of the analysis of similarity reduction dimensionality diagram, Word2vec can effectively transform the inner relationship among words into numerical feature. For four species in this study, the best models are obtained with the overall accuracy of 0.975, 0.765, 0.885, 0.967, the Matthew's correlation coefficient of 0.940, 0.530, 0.771, 0.934, and the AUC of 0.975, 0.800, 0.888, 0.981, which indicate that the proposed predictor has a stable ability and provide a high confidence coefficient to classify both of ORIs and non-ORIs. Compared with state-of-the-art methods, the proposed predictor can achieve ORI identification with significant improvement. It is therefore reasonable to anticipate that the proposed method will make a useful high throughput tool for genome analysis.
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Affiliation(s)
- Feng Wu
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, Weihai, 264200, China
| | - Runtao Yang
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, Weihai, 264200, China.
| | - Chengjin Zhang
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, Weihai, 264200, China
| | - Lina Zhang
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, Weihai, 264200, China
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Manavalan B, Basith S, Shin TH, Lee G. Computational prediction of species-specific yeast DNA replication origin via iterative feature representation. Brief Bioinform 2020; 22:6000361. [PMID: 33232970 PMCID: PMC8294535 DOI: 10.1093/bib/bbaa304] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 10/08/2020] [Accepted: 10/09/2020] [Indexed: 12/13/2022] Open
Abstract
Deoxyribonucleic acid replication is one of the most crucial tasks taking place in the cell, and it has to be precisely regulated. This process is initiated in the replication origins (ORIs), and thus it is essential to identify such sites for a deeper understanding of the cellular processes and functions related to the regulation of gene expression. Considering the important tasks performed by ORIs, several experimental and computational approaches have been developed in the prediction of such sites. However, existing computational predictors for ORIs have certain curbs, such as building only single-feature encoding models, limited systematic feature engineering efforts and failure to validate model robustness. Hence, we developed a novel species-specific yeast predictor called yORIpred that accurately identify ORIs in the yeast genomes. To develop yORIpred, we first constructed optimal 40 baseline models by exploring eight different sequence-based encodings and five different machine learning classifiers. Subsequently, the predicted probability of 40 models was considered as the novel feature vector and carried out iterative feature learning approach independently using five different classifiers. Our systematic analysis revealed that the feature representation learned by the support vector machine algorithm (yORIpred) could well discriminate the distribution characteristics between ORIs and non-ORIs when compared with the other four algorithms. Comprehensive benchmarking experiments showed that yORIpred achieved superior and stable performance when compared with the existing predictors on the same training datasets. Furthermore, independent evaluation showcased the best and accurate performance of yORIpred thus underscoring the significance of iterative feature representation. To facilitate the users in obtaining their desired results without undergoing any mathematical, statistical or computational hassles, we developed a web server for the yORIpred predictor, which is available at: http://thegleelab.org/yORIpred.
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Affiliation(s)
| | - Shaherin Basith
- Department of Physiology, Ajou University School of Medicine, Republic of Korea
| | - Tae Hwan Shin
- Department of Physiology, Ajou University School of Medicine, Republic of Korea
| | - Gwang Lee
- Department of Physiology, Ajou University School of Medicine, Republic of Korea
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