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d'Oelsnitz S, Stofel SK, Love JD, Ellington AD. Snowprint: a predictive tool for genetic biosensor discovery. Commun Biol 2024; 7:163. [PMID: 38336860 PMCID: PMC10858194 DOI: 10.1038/s42003-024-05849-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 01/23/2024] [Indexed: 02/12/2024] Open
Abstract
Bioengineers increasingly rely on ligand-inducible transcription regulators for chemical-responsive control of gene expression, yet the number of regulators available is limited. Novel regulators can be mined from genomes, but an inadequate understanding of their DNA specificity complicates genetic design. Here we present Snowprint, a simple yet powerful bioinformatic tool for predicting regulator:operator interactions. Benchmarking results demonstrate that Snowprint predictions are significantly similar for >45% of experimentally validated regulator:operator pairs from organisms across nine phyla and for regulators that span five distinct structural families. We then use Snowprint to design promoters for 33 previously uncharacterized regulators sourced from diverse phylogenies, of which 28 are shown to influence gene expression and 24 produce a >20-fold dynamic range. A panel of the newly repurposed regulators are then screened for response to biomanufacturing-relevant compounds, yielding new sensors for a polyketide (olivetolic acid), terpene (geraniol), steroid (ursodiol), and alkaloid (tetrahydropapaverine) with induction ratios up to 10.7-fold. Snowprint represents a unique, protein-agnostic tool that greatly facilitates the discovery of ligand-inducible transcriptional regulators for bioengineering applications. A web-accessible version of Snowprint is available at https://snowprint.groov.bio .
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Affiliation(s)
- Simon d'Oelsnitz
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA.
- Synthetic Biology HIVE, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA.
| | - Sarah K Stofel
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA
| | - Joshua D Love
- Independent Web Developer, Bentonville, AR, 72712, USA
| | - Andrew D Ellington
- Department of Molecular Biosciences, University of Texas at Austin, Austin, TX, 78712, USA
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2
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Pan J, Luo X, Bian J, Shao T, Li C, Zhao T, Zhang S, Zhou F, Wang G. Identification of Genomic Islands in Synechococcus sp. WH8102 Using Genomic Barcode and Whole-Genome Microarray Analysis. Curr Bioinform 2021. [DOI: 10.2174/1574893615666200121160615] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Synechococcus sp. WH8102 is one of the most abundant photosynthetic organisms in many ocean regions.
Objective:
The aim of this study is to identify genomic islands (GIs) in Synechococcus sp. WH8102 with integrated methods.
Methods:
We have applied genomic barcode to identify the GIs in Synechococcus sp. WH8102, which could make genomic regions of different origins visually apparent. The gene expression data of the predicted GIs was analyzed through microarray data which was collected for functional analysis of the relevant genes.
Results:
Seven GIs were identified in Synechococcus sp. WH8102. Most of them are involved in cell surface modification, photosynthesis and drug resistance. In addition, our analysis also revealed the functions of these GIs, which could be used for in-depth study on the evolution of this strain.
Conclusion:
Genomic barcodes provide us with a comprehensive and intuitive view of the target genome. We can use it to understand the intrinsic characteristics of the whole genome and identify GIs or other similar elements.
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Affiliation(s)
- Jiahui Pan
- Department of Pathogenbiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medicine, Jilin University, Changchun, 130021,China
| | - Xizi Luo
- Department of Pathogenbiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medicine, Jilin University, Changchun, 130021,China
| | - Jiang Bian
- Jilin Provincial Center for Disease Control and Prevention, Changchun, 130062,China
| | - Tong Shao
- Department of Pathogenbiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medicine, Jilin University, Changchun, 130021,China
| | - Chaoying Li
- Department of Pathogenbiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medicine, Jilin University, Changchun, 130021,China
| | - Tingting Zhao
- Department of Pathogenbiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medicine, Jilin University, Changchun, 130021,China
| | - Shiwei Zhang
- Department of Pathogenbiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medicine, Jilin University, Changchun, 130021,China
| | - Fengfeng Zhou
- Department of Pathogenbiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medicine, Jilin University, Changchun, 130021,China
| | - Guoqing Wang
- Department of Pathogenbiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medicine, Jilin University, Changchun, 130021,China
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Long P, Zhang L, Huang B, Chen Q, Liu H. Integrating genome sequence and structural data for statistical learning to predict transcription factor binding sites. Nucleic Acids Res 2020; 48:12604-12617. [PMID: 33264415 PMCID: PMC7736823 DOI: 10.1093/nar/gkaa1134] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 09/18/2020] [Accepted: 11/10/2020] [Indexed: 01/11/2023] Open
Abstract
We report an approach to predict DNA specificity of the tetracycline repressor (TetR) family transcription regulators (TFRs). First, a genome sequence-based method was streamlined with quantitative P-values defined to filter out reliable predictions. Then, a framework was introduced to incorporate structural data and to train a statistical energy function to score the pairing between TFR and TFR binding site (TFBS) based on sequences. The predictions benchmarked against experiments, TFBSs for 29 out of 30 TFRs were correctly predicted by either the genome sequence-based or the statistical energy-based method. Using P-values or Z-scores as indicators, we estimate that 59.6% of TFRs are covered with relatively reliable predictions by at least one of the two methods, while only 28.7% are covered by the genome sequence-based method alone. Our approach predicts a large number of new TFBs which cannot be correctly retrieved from public databases such as FootprintDB. High-throughput experimental assays suggest that the statistical energy can model the TFBSs of a significant number of TFRs reliably. Thus the energy function may be applied to explore for new TFBSs in respective genomes. It is possible to extend our approach to other transcriptional factor families with sufficient structural information.
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Affiliation(s)
- Pengpeng Long
- School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Lu Zhang
- School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Bin Huang
- School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Quan Chen
- School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China
- Hefei National Laboratory for Physical Sciences at the Microscale, Hefei, Anhui 230026, China
| | - Haiyan Liu
- School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China
- Hefei National Laboratory for Physical Sciences at the Microscale, Hefei, Anhui 230026, China
- School of Data Science, University of Science and Technology of China, Hefei, Anhui 230026, China
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4
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Taboada-Castro H, Castro-Mondragón JA, Aguilar-Vera A, Hernández-Álvarez AJ, van Helden J, Encarnación-Guevara S. RhizoBindingSites, a Database of DNA-Binding Motifs in Nitrogen-Fixing Bacteria Inferred Using a Footprint Discovery Approach. Front Microbiol 2020; 11:567471. [PMID: 33250866 PMCID: PMC7674921 DOI: 10.3389/fmicb.2020.567471] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 10/13/2020] [Indexed: 11/30/2022] Open
Abstract
Basic knowledge of transcriptional regulation is needed to understand the mechanisms governing biological processes, i.e., nitrogen fixation by Rhizobiales bacteria in symbiosis with leguminous plants. The RhizoBindingSites database is a computer-assisted framework providing motif-gene-associated conserved sequences potentially implicated in transcriptional regulation in nine symbiotic species. A dyad analysis algorithm was used to deduce motifs in the upstream regulatory region of orthologous genes, and only motifs also located in the gene seed promoter with a p-value of 1e-4 were accepted. A genomic scan analysis of the upstoream sequences with these motifs was performed. These predicted binding sites were categorized according to low, medium and high homology between the matrix and the upstream regulatory sequence. On average, 62.7% of the genes had a motif, accounting for 80.44% of the genes per genome, with 19613 matrices (a matrix is a representation of a motif). The RhizoBindingSites database provides motif and gene information, motif conservation in the order Rhizobiales, matrices, motif logos, regulatory networks constructed from theoretical or experimental data, a criterion for selecting motifs and a guide for users. The RhizoBindingSites database is freely available online at rhizobindingsites.ccg.unam.mx.
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Affiliation(s)
| | | | - Alejandro Aguilar-Vera
- Center for Genomic Sciences, National Autonomous University of Mexico, Cuernavaca, Mexico
| | | | - Jacques van Helden
- CNRS, IFB-core, UMS 3601, Institut Français de Bioinformatique, Évry, France.,Laboratoire Theory and Approaches of Genome Complexity (TAGC), Inserm, Aix-Marseille Univ, Marseille, France
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Cao H, Ma Q, Chen X, Xu Y. DOOR: a prokaryotic operon database for genome analyses and functional inference. Brief Bioinform 2020; 20:1568-1577. [PMID: 28968679 DOI: 10.1093/bib/bbx088] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 06/13/2017] [Indexed: 11/14/2022] Open
Abstract
The rapid accumulation of fully sequenced prokaryotic genomes provides unprecedented information for biological studies of bacterial and archaeal organisms in a systematic manner. Operons are the basic functional units for conducting such studies. Here, we review an operon database DOOR (the Database of prOkaryotic OpeRons) that we have previously developed and continue to update. Currently, the database contains 6 975 454 computationally predicted operons in 2072 complete genomes. In addition, the database also contains the following information: (i) transcriptional units for 24 genomes derived using publicly available transcriptomic data; (ii) orthologous gene mapping across genomes; (iii) 6408 cis-regulatory motifs for transcriptional factors of some operons for 203 genomes; (iv) 3 456 718 Rho-independent terminators for 2072 genomes; as well as (v) a suite of tools in support of applications of the predicted operons. In this review, we will explain how such data are computationally derived and demonstrate how they can be used to derive a wide range of higher-level information needed for systems biology studies to tackle complex and fundamental biology questions.
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Li Q, Sapkota M, van der Knaap E. Perspectives of CRISPR/Cas-mediated cis-engineering in horticulture: unlocking the neglected potential for crop improvement. HORTICULTURE RESEARCH 2020; 7:36. [PMID: 32194972 PMCID: PMC7072075 DOI: 10.1038/s41438-020-0258-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 01/09/2020] [Accepted: 02/11/2020] [Indexed: 05/14/2023]
Abstract
Directed breeding of horticultural crops is essential for increasing yield, nutritional content, and consumer-valued characteristics such as shape and color of the produce. However, limited genetic diversity restricts the amount of crop improvement that can be achieved through conventional breeding approaches. Natural genetic changes in cis-regulatory regions of genes play important roles in shaping phenotypic diversity by altering their expression. Utilization of CRISPR/Cas editing in crop species can accelerate crop improvement through the introduction of genetic variation in a targeted manner. The advent of CRISPR/Cas-mediated cis-regulatory region engineering (cis-engineering) provides a more refined method for modulating gene expression and creating phenotypic diversity to benefit crop improvement. Here, we focus on the current applications of CRISPR/Cas-mediated cis-engineering in horticultural crops. We describe strategies and limitations for its use in crop improvement, including de novo cis-regulatory element (CRE) discovery, precise genome editing, and transgene-free genome editing. In addition, we discuss the challenges and prospects regarding current technologies and achievements. CRISPR/Cas-mediated cis-engineering is a critical tool for generating horticultural crops that are better able to adapt to climate change and providing food for an increasing world population.
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Affiliation(s)
- Qiang Li
- College of Horticultural Science and Engineering, Shandong Agricultural University, Tai’an, China
- Center for Applied Genetic Technologies, University of Georgia, Athens, GA USA
| | - Manoj Sapkota
- Institute for Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA USA
| | - Esther van der Knaap
- Center for Applied Genetic Technologies, University of Georgia, Athens, GA USA
- Institute for Plant Breeding, Genetics and Genomics, University of Georgia, Athens, GA USA
- Department of Horticulture, University of Georgia, Athens, GA USA
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7
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Genome-scale exploration of transcriptional regulation in the nisin Z producer Lactococcus lactis subsp. lactis IO-1. Sci Rep 2020; 10:3787. [PMID: 32123183 PMCID: PMC7051946 DOI: 10.1038/s41598-020-59731-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Accepted: 01/13/2020] [Indexed: 02/06/2023] Open
Abstract
Transcription is of the most crucial steps of gene expression in bacteria, whose regulation guarantees the bacteria's ability to adapt to varying environmental conditions. Discovering the molecular basis and genomic principles of the transcriptional regulation is thus one of the most important tasks in cellular and molecular biology. Here, a comprehensive phylogenetic footprinting framework was implemented to predict maximal regulons of Lactococcus lactis subsp. lactis IO-1, a lactic acid bacterium known for its high potentials in nisin Z production as well as efficient xylose consumption which have made it a promising biotechnological strain. A total set of 321 regulons covering more than 90% of all the bacterium's operons have been elucidated and validated according to available data. Multiple novel biologically-relevant members were introduced amongst which arsC, mtlA and mtl operon for BusR, MtlR and XylR regulons can be named, respectively. Moreover, the effect of riboflavin on nisin biosynthesis was assessed in vitro and a negative correlation was observed. It is believed that understandings from such networks not only can be useful for studying transcriptional regulatory potentials of the target organism but also can be implemented in biotechnology to rationally design favorable production conditions.
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Liu B, Han L, Liu X, Wu J, Ma Q. Computational Prediction of Sigma-54 Promoters in Bacterial Genomes by Integrating Motif Finding and Machine Learning Strategies. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1211-1218. [PMID: 29993815 DOI: 10.1109/tcbb.2018.2816032] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Sigma factor, as a unit of RNA polymerase holoenzyme, is a critical factor in the process of gene transcriptional regulation. It recognizes the specific DNA sites and brings the core enzyme of RNA polymerase to the upstream regions of target genes. Therefore, the prediction of the promoters for a particular sigma factor is essential for interpreting functional genomic data and observation. This paper develops a new method to predict sigma-54 promoters in bacterial genomes. The new method organically integrates motif finding and machine learning strategies to capture the intrinsic features of sigma-54 promoters. The experiments on E. coli benchmark test set show that our method has good capability to distinguish sigma-54 promoters from surrounding or randomly selected DNA sequences. The applications of the other three bacterial genomes indicate the potential robustness and applicative power of our method on a large number of bacterial genomes. The source code of our method can be freely downloaded at https://github.com/maqin2001/PromotePredictor.
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9
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Wei L, Hu J, Li F, Song J, Su R, Zou Q. Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms. Brief Bioinform 2018; 21:106-119. [PMID: 30383239 DOI: 10.1093/bib/bby107] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 09/18/2018] [Accepted: 10/05/2018] [Indexed: 12/11/2022] Open
Abstract
Quorum-sensing peptides (QSPs) are the signal molecules that are closely associated with diverse cellular processes, such as cell-cell communication, and gene expression regulation in Gram-positive bacteria. It is therefore of great importance to identify QSPs for better understanding and in-depth revealing of their functional mechanisms in physiological processes. Machine learning algorithms have been developed for this purpose, showing the great potential for the reliable prediction of QSPs. In this study, several sequence-based feature descriptors for peptide representation and machine learning algorithms are comprehensively reviewed, evaluated and compared. To effectively use existing feature descriptors, we used a feature representation learning strategy that automatically learns the most discriminative features from existing feature descriptors in a supervised way. Our results demonstrate that this strategy is capable of effectively capturing the sequence determinants to represent the characteristics of QSPs, thereby contributing to the improved predictive performance. Furthermore, wrapping this feature representation learning strategy, we developed a powerful predictor named QSPred-FL for the detection of QSPs in large-scale proteomic data. Benchmarking results with 10-fold cross validation showed that QSPred-FL is able to achieve better performance as compared to the state-of-the-art predictors. In addition, we have established a user-friendly webserver that implements QSPred-FL, which is currently available at http://server.malab.cn/QSPred-FL. We expect that this tool will be useful for the high-throughput prediction of QSPs and the discovery of important functional mechanisms of QSPs.
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Affiliation(s)
- Leyi Wei
- School of Computer Science and Technology, Tianjin University, Tianjin, China
| | - Jie Hu
- School of Computer Science and Technology, Tianjin University, Tianjin, China
| | - Fuyi Li
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia.,Monash Centre for Data Science, Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Jiangning Song
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC, Australia.,Monash Centre for Data Science, Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Ran Su
- School of Computer Software, Tianjin University, Tianjin, China
| | - Quan Zou
- School of Computer Science and Technology, Tianjin University, Tianjin, China.,Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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10
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Chen X, Ma A, McDermaid A, Zhang H, Liu C, Cao H, Ma Q. RECTA: Regulon Identification Based on Comparative Genomics and Transcriptomics Analysis. Genes (Basel) 2018; 9:genes9060278. [PMID: 29849014 PMCID: PMC6027394 DOI: 10.3390/genes9060278] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Revised: 05/19/2018] [Accepted: 05/25/2018] [Indexed: 11/16/2022] Open
Abstract
Regulons, which serve as co-regulated gene groups contributing to the transcriptional regulation of microbial genomes, have the potential to aid in understanding of underlying regulatory mechanisms. In this study, we designed a novel computational pipeline, regulon identification based on comparative genomics and transcriptomics analysis (RECTA), for regulon prediction related to the gene regulatory network under certain conditions. To demonstrate the effectiveness of this tool, we implemented RECTA on Lactococcus lactis MG1363 data to elucidate acid-response regulons. A total of 51 regulons were identified, 14 of which have computational-verified significance. Among these 14 regulons, five of them were computationally predicted to be connected with acid stress response. Validated by literature, 33 genes in Lactococcus lactis MG1363 were found to have orthologous genes which were associated with six regulons. An acid response related regulatory network was constructed, involving two trans-membrane proteins, eight regulons (llrA, llrC, hllA, ccpA, NHP6A, rcfB, regulons #8 and #39), nine functional modules, and 33 genes with orthologous genes known to be associated with acid stress. The predicted response pathways could serve as promising candidates for better acid tolerance engineering in Lactococcus lactis. Our RECTA pipeline provides an effective way to construct a reliable gene regulatory network through regulon elucidation, and has strong application power and can be effectively applied to other bacterial genomes where the elucidation of the transcriptional regulation network is needed.
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Affiliation(s)
- Xin Chen
- Center for Applied Mathematics, Tianjin University, Tianjin 300072, China.
| | - Anjun Ma
- Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD 57006, USA.
- Department of Mathematics and Statistics, South Dakota State University, Brookings, SD 57006, USA.
| | - Adam McDermaid
- Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD 57006, USA.
- Department of Mathematics and Statistics, South Dakota State University, Brookings, SD 57006, USA.
| | - Hanyuan Zhang
- College of Computer Science and Engineering, University of Nebraska Lincoln, Lincoln, NE 68588, USA.
| | - Chao Liu
- Shandong Provincial Hospital affiliated to Shandong University, Jinan 250021, China.
| | - Huansheng Cao
- Center for Fundamental and Applied Microbiomics, Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA.
| | - Qin Ma
- Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD 57006, USA.
- Department of Mathematics and Statistics, South Dakota State University, Brookings, SD 57006, USA.
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Brody T, Yavatkar AS, Park DS, Kuzin A, Ross J, Odenwald WF. Flavivirus and Filovirus EvoPrinters: New alignment tools for the comparative analysis of viral evolution. PLoS Negl Trop Dis 2017. [PMID: 28622346 PMCID: PMC5489223 DOI: 10.1371/journal.pntd.0005673] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Background Flavivirus and Filovirus infections are serious epidemic threats to human populations. Multi-genome comparative analysis of these evolving pathogens affords a view of their essential, conserved sequence elements as well as progressive evolutionary changes. While phylogenetic analysis has yielded important insights, the growing number of available genomic sequences makes comparisons between hundreds of viral strains challenging. We report here a new approach for the comparative analysis of these hemorrhagic fever viruses that can superimpose an unlimited number of one-on-one alignments to identify important features within genomes of interest. Methodology/Principal finding We have adapted EvoPrinter alignment algorithms for the rapid comparative analysis of Flavivirus or Filovirus sequences including Zika and Ebola strains. The user can input a full genome or partial viral sequence and then view either individual comparisons or generate color-coded readouts that superimpose hundreds of one-on-one alignments to identify unique or shared identity SNPs that reveal ancestral relationships between strains. The user can also opt to select a database genome in order to access a library of pre-aligned genomes of either 1,094 Flaviviruses or 460 Filoviruses for rapid comparative analysis with all database entries or a select subset. Using EvoPrinter search and alignment programs, we show the following: 1) superimposing alignment data from many related strains identifies lineage identity SNPs, which enable the assessment of sublineage complexity within viral outbreaks; 2) whole-genome SNP profile screens uncover novel Dengue2 and Zika recombinant strains and their parental lineages; 3) differential SNP profiling identifies host cell A-to-I hyper-editing within Ebola and Marburg viruses, and 4) hundreds of superimposed one-on-one Ebola genome alignments highlight ultra-conserved regulatory sequences, invariant amino acid codons and evolutionarily variable protein-encoding domains within a single genome. Conclusions/Significance EvoPrinter allows for the assessment of lineage complexity within Flavivirus or Filovirus outbreaks, identification of recombinant strains, highlights sequences that have undergone host cell A-to-I editing, and identifies unique input and database SNPs within highly conserved sequences. EvoPrinter’s ability to superimpose alignment data from hundreds of strains onto a single genome has allowed us to identify unique Zika virus sublineages that are currently spreading in South, Central and North America, the Caribbean, and in China. This new set of integrated alignment programs should serve as a useful addition to existing tools for the comparative analysis of these viruses. Flaviviruses, including Zika and Dengue viruses, and Filoviruses, including Ebola and Marburg viruses, are significant global public health threats. Genetic surveillance of viral isolates provides important insights into the origin of outbreaks, reveals lineage heterogeneity and diversification, and facilitates identification of novel recombinant strains and host cell modified viral genomes. We report the development of EvoPrinter, a web-accessed alignment tool for the rapid comparative analysis of viral genomes. EvoPrinter superimposes alignment data from multiple pairwise comparisons onto a single reference sequence of interest, to reveal both similarities and differences detected in hundreds of selected viral isolates. Evoprinter databases provide easy access to hundreds of non-redundant Flavivirus and Filovirus genomes. allowing the user to distinguish between sublineage identity SNPs and unique strain-specific SNPs, thus facilitating analysis of the history of viral diversification during an epidemic. EvoPrinter also proves useful in identifying recombinant strains and their parental lineages and detecting host-cell genomic editing. EvoPrinter should serve as a useful addition to existing tools for the comparative analysis of these viruses.
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Affiliation(s)
- Thomas Brody
- Neural Cell-Fate Determinants Section, NINDS, NIH, Bethesda, Maryland, United States of America
- * E-mail: (TB); (WFO)
| | - Amarendra S. Yavatkar
- Division of Intramural Research Information Technology Program, NINDS, NIH, Bethesda, Maryland, United States of America
| | - Dong Sun Park
- Division of Intramural Research Information Technology Program, NINDS, NIH, Bethesda, Maryland, United States of America
| | - Alexander Kuzin
- Neural Cell-Fate Determinants Section, NINDS, NIH, Bethesda, Maryland, United States of America
| | - Jermaine Ross
- Neural Cell-Fate Determinants Section, NINDS, NIH, Bethesda, Maryland, United States of America
| | - Ward F. Odenwald
- Neural Cell-Fate Determinants Section, NINDS, NIH, Bethesda, Maryland, United States of America
- * E-mail: (TB); (WFO)
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12
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Yang J, Chen X, McDermaid A, Ma Q. DMINDA 2.0: integrated and systematic views of regulatory DNA motif identification and analyses. Bioinformatics 2017; 33:2586-2588. [DOI: 10.1093/bioinformatics/btx223] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Accepted: 04/12/2017] [Indexed: 11/14/2022] Open
Affiliation(s)
- Jinyu Yang
- Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA
- Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA
| | - Xin Chen
- Center for Applied Mathematics, Tianjin University, Tianjin, China
| | - Adam McDermaid
- Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA
- Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA
| | - Qin Ma
- Bioinformatics and Mathematical Biosciences Lab, Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA
- Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA
- BioSNTR, Brookings, SD, USA
- Population Health group, Sanford Research, Sioux Falls, SD, USA
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13
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Liu B, Yang J, Li Y, McDermaid A, Ma Q. An algorithmic perspective of de novo cis-regulatory motif finding based on ChIP-seq data. Brief Bioinform 2017; 19:1069-1081. [DOI: 10.1093/bib/bbx026] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Indexed: 01/06/2023] Open
Affiliation(s)
- Bingqiang Liu
- School of Mathematics, Shandong University, Jinan Shandong, P. R. China
| | - Jinyu Yang
- Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA
| | - Yang Li
- School of Mathematics, Shandong University, Jinan Shandong, P. R. China
| | - Adam McDermaid
- Department of Mathematics and Statistics, South Dakota State University, Brookings, SD, USA
| | - Qin Ma
- Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA
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