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Tseng KC, Chiang-Hsieh YF, Pai H, Wu NY, Zheng HQ, Chow CN, Lee TY, Chang SB, Lin NS, Chang WC. sRIS: A Small RNA Illustration System for Plant Next-Generation Sequencing Data Analysis. Plant Cell Physiol 2020; 61:1204-1212. [PMID: 32181856 DOI: 10.1093/pcp/pcaa034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 03/08/2020] [Indexed: 06/10/2023]
Abstract
Small RNA (sRNA), such as microRNA (miRNA) and short interfering RNA, are well-known to control gene expression based on degradation of target mRNA in plants. A considerable amount of research has applied next-generation sequencing (NGS) to reveal the regulatory pathways of plant sRNAs. Consequently, numerous bioinformatics tools have been developed for the purpose of analyzing sRNA NGS data. However, most methods focus on the study of sRNA expression profiles or novel miRNAs predictions. The analysis of sRNA target genes is usually not integrated into their pipelines. As a result, there is still no means available for identifying the interaction mechanisms between host and virus or the synergistic effects between two viruses. For the present study, a comprehensive system, called the Small RNA Illustration System (sRIS), has been developed. This system contains two main components. The first is for sRNA overview analysis and can be used not only to identify miRNA but also to investigate virus-derived small interfering RNA. The second component is for sRNA target prediction, and it employs both bioinformatics calculations and degradome sequencing data to enhance the accuracy of target prediction. In addition, this system has been designed so that figures and tables for the outputs of each analysis can be easily retrieved and accessed, making it easier for users to quickly identify and quantify their results. sRIS is available at http://sris.itps.ncku.edu.tw/.
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Affiliation(s)
- Kuan-Chieh Tseng
- Department of Life Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Yi-Fan Chiang-Hsieh
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences and Microbiology, National Cheng Kung University, Tainan 701, Taiwan
| | - Hsuan Pai
- Institute of Plant and Microbial Biology, Academia Sinica, Nankang, Taipei 115, Taiwan
| | - Nai-Yun Wu
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences and Microbiology, National Cheng Kung University, Tainan 701, Taiwan
| | - Han-Qin Zheng
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences and Microbiology, National Cheng Kung University, Tainan 701, Taiwan
| | - Chi-Nga Chow
- College of Biosciences and Biotechnology, NCKU-AS Graduate Program in Translational Agricultural Sciences, National Cheng Kung University, Tainan 70101, Taiwan
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China
| | - Tzong-Yi Lee
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China
| | - Song-Bin Chang
- Department of Life Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Na-Sheng Lin
- Institute of Plant and Microbial Biology, Academia Sinica, Nankang, Taipei 115, Taiwan
| | - Wen-Chi Chang
- Department of Life Sciences, National Cheng Kung University, Tainan 701, Taiwan
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences and Microbiology, National Cheng Kung University, Tainan 701, Taiwan
- College of Biosciences and Biotechnology, NCKU-AS Graduate Program in Translational Agricultural Sciences, National Cheng Kung University, Tainan 70101, Taiwan
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Tseng KC, Chiang-Hsieh YF, Pai H, Chow CN, Lee SC, Zheng HQ, Kuo PL, Li GZ, Hung YC, Lin NS, Chang WC. microRPM: a microRNA prediction model based only on plant small RNA sequencing data. Bioinformatics 2019; 34:1108-1115. [PMID: 29136092 DOI: 10.1093/bioinformatics/btx725] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2017] [Accepted: 11/08/2017] [Indexed: 01/15/2023] Open
Abstract
Motivation MicroRNAs (miRNAs) are endogenous non-coding small RNAs (of about 22 nucleotides), which play an important role in the post-transcriptional regulation of gene expression via either mRNA cleavage or translation inhibition. Several machine learning-based approaches have been developed to identify novel miRNAs from next generation sequencing (NGS) data. Typically, precursor/genomic sequences are required as references for most methods. However, the non-availability of genomic sequences is often a limitation in miRNA discovery in non-model plants. A systematic approach to determine novel miRNAs without reference sequences is thus necessary. Results In this study, an effective method was developed to identify miRNAs from non-model plants based only on NGS datasets. The miRNA prediction model was trained with several duplex structure-related features of mature miRNAs and their passenger strands using a support vector machine algorithm. The accuracy of the independent test reached 96.61% and 93.04% for dicots (Arabidopsis) and monocots (rice), respectively. Furthermore, true small RNA sequencing data from orchids was tested in this study. Twenty-one predicted orchid miRNAs were selected and experimentally validated. Significantly, 18 of them were confirmed in the qRT-PCR experiment. This novel approach was also compiled as a user-friendly program called microRPM (miRNA Prediction Model). Availability and implementation This resource is freely available at http://microRPM.itps.ncku.edu.tw. Contact nslin@sinica.edu.tw or sarah321@mail.ncku.edu.tw. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kuan-Chieh Tseng
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan
| | - Yi-Fan Chiang-Hsieh
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan
| | - Hsuan Pai
- Institute of Plant and Microbial Biology, Academia Sinica, NanKang, Taipei 115, Taiwan
| | - Chi-Nga Chow
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan
| | - Shu-Chuan Lee
- Institute of Plant and Microbial Biology, Academia Sinica, NanKang, Taipei 115, Taiwan
| | - Han-Qin Zheng
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan
| | - Po-Li Kuo
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan
| | - Guan-Zhen Li
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan
| | - Yu-Cheng Hung
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan
| | - Na-Sheng Lin
- Institute of Plant and Microbial Biology, Academia Sinica, NanKang, Taipei 115, Taiwan
| | - Wen-Chi Chang
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan
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Hou PF, Chien CH, Chiang-Hsieh YF, Tseng KC, Chow CN, Huang HJ, Chang WC. Paddy-upland rotation for sustainable agriculture with regards to diverse soil microbial community. Sci Rep 2018; 8:7966. [PMID: 29789586 PMCID: PMC5964091 DOI: 10.1038/s41598-018-26181-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 05/01/2018] [Indexed: 12/02/2022] Open
Abstract
Diverse soil microbial community is determinant for sustainable agriculture. Rich microbial diversity has presumably improved soil health for economic crops to grow. In this work, the benefits of paddy-upland rotation on soil microbial diversity and specific microbes are thus intensively explored. The microbiome from multiple factor experiment (three fertilizations coupled with two rotation systems) were investigated by novel enrichment and co-occurrence analysis in a field well maintained for 25 years. Using next-generation sequencing technique, we firstly present explicit evidence that different rotation systems rather than fertilizations mightily governed the soil microbiome. Paddy-upland rotation (R1) obviously increase more microbial diversity than upland rotation (R2) whether organic (OF), chemical (CF) or integrated fertilizers (IF) were concomitantly applied. Besides, the specific bacterial composition dominated in OF soil is more similar to that of R1 than to CF, suggesting that paddy-upland rotation might be the best option for sustainable agriculture if chemical fertilizer is still required. Interestingly, the pot bioassay verified clearly the novel analysis prediction, illustrating that greater microbial diversity and specific microbial composition correlated significantly with disease resistance. This finding highlights the eminence of paddy-upland rotation in promoting microbial diversity and specific microbial compositions, preserving soil health for sustainable agriculture.
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Affiliation(s)
- Ping-Fu Hou
- Kaohsiung District Agricultural Research and Extension Station, Pingtung County, 90846, Taiwan
- Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan, 70101, Taiwan
- Department of Life Sciences, National Cheng Kung University, Tainan, 70101, Taiwan
| | - Chia-Hung Chien
- Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan, 70101, Taiwan
| | - Yi-Fan Chiang-Hsieh
- Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan, 70101, Taiwan
| | - Kuan-Chieh Tseng
- Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan, 70101, Taiwan
| | - Chi-Nga Chow
- Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan, 70101, Taiwan
| | - Hao-Jen Huang
- Department of Life Sciences, National Cheng Kung University, Tainan, 70101, Taiwan.
| | - Wen-Chi Chang
- Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan, 70101, Taiwan.
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Chow CN, Zheng HQ, Wu NY, Chien CH, Huang HD, Lee TY, Chiang-Hsieh YF, Hou PF, Yang TY, Chang WC. PlantPAN 2.0: an update of plant promoter analysis navigator for reconstructing transcriptional regulatory networks in plants. Nucleic Acids Res 2015; 44:D1154-60. [PMID: 26476450 PMCID: PMC4702776 DOI: 10.1093/nar/gkv1035] [Citation(s) in RCA: 242] [Impact Index Per Article: 26.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Accepted: 09/29/2015] [Indexed: 12/17/2022] Open
Abstract
Transcription factors (TFs) are sequence-specific DNA-binding proteins acting as critical regulators of gene expression. The Plant Promoter Analysis Navigator (PlantPAN; http://PlantPAN2.itps.ncku.edu.tw) provides an informative resource for detecting transcription factor binding sites (TFBSs), corresponding TFs, and other important regulatory elements (CpG islands and tandem repeats) in a promoter or a set of plant promoters. Additionally, TFBSs, CpG islands, and tandem repeats in the conserve regions between similar gene promoters are also identified. The current PlantPAN release (version 2.0) contains 16 960 TFs and 1143 TF binding site matrices among 76 plant species. In addition to updating of the annotation information, adding experimentally verified TF matrices, and making improvements in the visualization of transcriptional regulatory networks, several new features and functions are incorporated. These features include: (i) comprehensive curation of TF information (response conditions, target genes, and sequence logos of binding motifs, etc.), (ii) co-expression profiles of TFs and their target genes under various conditions, (iii) protein-protein interactions among TFs and their co-factors, (iv) TF-target networks, and (v) downstream promoter elements. Furthermore, a dynamic transcriptional regulatory network under various conditions is provided in PlantPAN 2.0. The PlantPAN 2.0 is a systematic platform for plant promoter analysis and reconstructing transcriptional regulatory networks.
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Affiliation(s)
- Chi-Nga Chow
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Han-Qin Zheng
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Nai-Yun Wu
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Chia-Hung Chien
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Hsien-Da Huang
- Department of Biological Science and Technology, Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsin-Chu 300, Taiwan
| | - Tzong-Yi Lee
- Department of Computer Science and Engineering, Yuan Ze University, Chung-Li 320, Taiwan
| | - Yi-Fan Chiang-Hsieh
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Ping-Fu Hou
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 701, Taiwan College of Biosciences and Biotechnology, Department of Life Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Tien-Yi Yang
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 701, Taiwan
| | - Wen-Chi Chang
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 701, Taiwan
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Chien CH, Chiang-Hsieh YF, Chen YA, Chow CN, Wu NY, Hou PF, Chang WC. AtmiRNET: a web-based resource for reconstructing regulatory networks of Arabidopsis microRNAs. Database (Oxford) 2015; 2015:bav042. [PMID: 25972521 PMCID: PMC4429749 DOI: 10.1093/database/bav042] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2014] [Accepted: 04/15/2015] [Indexed: 11/15/2022]
Abstract
Compared with animal microRNAs (miRNAs), our limited knowledge of how miRNAs involve in significant biological processes in plants is still unclear. AtmiRNET is a novel resource geared toward plant scientists for reconstructing regulatory networks of Arabidopsis miRNAs. By means of highlighted miRNA studies in target recognition, functional enrichment of target genes, promoter identification and detection of cis- and trans-elements, AtmiRNET allows users to explore mechanisms of transcriptional regulation and miRNA functions in Arabidopsis thaliana, which are rarely investigated so far. High-throughput next-generation sequencing datasets from transcriptional start sites (TSSs)-relevant experiments as well as five core promoter elements were collected to establish the support vector machine-based prediction model for Arabidopsis miRNA TSSs. Then, high-confidence transcription factors participate in transcriptional regulation of Arabidopsis miRNAs are provided based on statistical approach. Furthermore, both experimentally verified and putative miRNA-target interactions, whose validity was supported by the correlations between the expression levels of miRNAs and their targets, are elucidated for functional enrichment analysis. The inferred regulatory networks give users an intuitive insight into the pivotal roles of Arabidopsis miRNAs through the crosstalk between miRNA transcriptional regulation (upstream) and miRNA-mediate (downstream) gene circuits. The valuable information that is visually oriented in AtmiRNET recruits the scant understanding of plant miRNAs and will be useful (e.g. ABA-miR167c-auxin signaling pathway) for further research. Database URL:http://AtmiRNET.itps.ncku.edu.tw/
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Affiliation(s)
- Chia-Hung Chien
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan
| | - Yi-Fan Chiang-Hsieh
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan
| | - Yi-An Chen
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan
| | - Chi-Nga Chow
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan
| | - Nai-Yun Wu
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan
| | - Ping-Fu Hou
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan
| | - Wen-Chi Chang
- College of Biosciences and Biotechnology, Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan
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Chien CH, Chow CN, Wu NY, Chiang-Hsieh YF, Hou PF, Chang WC. EXPath: a database of comparative expression analysis inferring metabolic pathways for plants. BMC Genomics 2015; 16 Suppl 2:S6. [PMID: 25708775 PMCID: PMC4331720 DOI: 10.1186/1471-2164-16-s2-s6] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND In general, the expression of gene alters conditionally to catalyze a specific metabolic pathway. Microarray-based datasets have been massively produced to monitor gene expression levels in parallel with numerous experimental treatments. Although several studies facilitated the linkage of gene expression data and metabolic pathways, none of them are amassed for plants. Moreover, advanced analysis such as pathways enrichment or how genes express under different conditions is not rendered. DESCRIPTION Therefore, EXPath was developed to not only comprehensively congregate the public microarray expression data from over 1000 samples in biotic stress, abiotic stress, and hormone secretion but also allow the usage of this abundant resource for coexpression analysis and differentially expression genes (DEGs) identification, finally inferring the enriched KEGG pathways and gene ontology (GO) terms of three model plants: Arabidopsis thaliana, Oryza sativa, and Zea mays. Users can access the gene expression patterns of interest under various conditions via five main functions (Gene Search, Pathway Search, DEGs Search, Pathways/GO Enrichment, and Coexpression analysis) in EXPath, which are presented by a user-friendly interface and valuable for further research. CONCLUSIONS In conclusion, EXPath, freely available at http://expath.itps.ncku.edu.tw, is a database resource that collects and utilizes gene expression profiles derived from microarray platforms under various conditions to infer metabolic pathways for plants.
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Zheng HQ, Chiang-Hsieh YF, Chien CH, Hsu BKJ, Liu TL, Chen CNN, Chang WC. AlgaePath: comprehensive analysis of metabolic pathways using transcript abundance data from next-generation sequencing in green algae. BMC Genomics 2014; 15:196. [PMID: 24628857 PMCID: PMC4028061 DOI: 10.1186/1471-2164-15-196] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2013] [Accepted: 02/26/2014] [Indexed: 01/10/2023] Open
Abstract
Background Algae are important non-vascular plants that have many research applications, including high species diversity, biofuel sources, and adsorption of heavy metals and, following processing, are used as ingredients in health supplements. The increasing availability of next-generation sequencing (NGS) data for algae genomes and transcriptomes has made the development of an integrated resource for retrieving gene expression data and metabolic pathway essential for functional analysis and systems biology. In a currently available resource, gene expression profiles and biological pathways are displayed separately, making it impossible to easily search current databases to identify the cellular response mechanisms. Therefore, in this work the novel AlgaePath database was developed to retrieve transcript abundance profiles efficiently under various conditions in numerous metabolic pathways. Description AlgaePath is a web-based database that integrates gene information, biological pathways, and NGS datasets for the green algae Chlamydomonas reinhardtii and Neodesmus sp. UTEX 2219–4. Users can search this database to identify transcript abundance profiles and pathway information using five query pages (Gene Search, Pathway Search, Differentially Expressed Genes (DEGs) Search, Gene Group Analysis, and Co-expression Analysis). The transcript abundance data of 45 and four samples from C. reinhardtii and Neodesmus sp. UTEX 2219–4, respectively, can be obtained directly on pathway maps. Genes that are differentially expressed between two conditions can be identified using Folds Search. The Gene Group Analysis page includes a pathway enrichment analysis, and can be used to easily compare the transcript abundance profiles of functionally related genes on a map. Finally, the Co-expression Analysis page can be used to search for co-expressed transcripts of a target gene. The results of the searches will provide a valuable reference for designing further experiments and for elucidating critical mechanisms from high-throughput data. Conclusions AlgaePath is an effective interface that can be used to clarify the transcript response mechanisms in different metabolic pathways under various conditions. Importantly, AlgaePath can be mined to identify critical mechanisms based on high-throughput sequencing. To our knowledge, AlgaePath is the most comprehensive resource for integrating numerous databases and analysis tools in algae. The system can be accessed freely online at http://algaepath.itps.ncku.edu.tw. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-196) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | | | | | | | | | - Ching-Nen Nathan Chen
- Institute of Tropical Plant Sciences, National Cheng Kung University, Tainan 70101, Taiwan.
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