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Construction Of The Epigenetics Database Of Moso Bamboo

Posted on:2021-05-16Degree:MasterType:Thesis
Country:ChinaCandidate:X XuFull Text:PDF
GTID:2393330614954704Subject:Bioinformatics
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Epigenetics mainly includes DNA methylation,long non-coding RNA,small molecule RNA and RNA editing.Studies from model plants have shown that epigenetics can regulate development.Phyllostachys edulis,as a versatile material and excellent species of bamboo presents important economic and ecological values.Bamboo attracts the attention of researchers by some important characteristics such as fast growth and long flowering.However,epigenetic research in bamboo is relatively rare comparing with other model plants.With the development of NGS technology,high-quality genomes of Phyllostachys edulis and a large amount of epigenetic data have emerged,which provide an opportunity for researchers to perform epigenetic analysis.However,at present a comprehensive and user-friendly database for in-depth data analysis of this resource is not available.Thus,delevompment of relative platform is necessary and urgent for storage and analysis of epigenomic data of Phyllostachys edulis.This project performs multiomics analysis and storage of date from bisulfite sequencing(BS-seq),transcriptome sequencing(RNA-Seq),and small molecule RNA sequencing in different periods of Phyllostachys edulis.We developed Phyllostachys edulis epigenetics database to meet the needs from different researchers for data retrieval,visualization,primer design,RNA Editing site prediction,and online alignment using web technologies such as LAMP framework,HTML5/CSS/Javascript front-end,Python/R back-end,My SQL relational database system.In addition,we also provided powerful visualization using Jbrowse software and BLAST alignment software,which allowed users to view and compare with reference sequences,gene annotation,u ORF,DNA methylation,transcriptome,circular RNA,and small RNA data.Besides the data from our own group,the database also integrated other published research results.The database provided up to 67 Phyllostachys edulis epigenetic data for users to explore.The database URL is available at following hyperlink: http://forestry.fafu.edu.cn/db/PheEpig.As an important epigenetic regulation method,RNA editing has not been studied in Phyllostachys edulis.In total,we predicted 14,855 RNA editing sites in the nucleus after excluding SNP data of Phyllostachys edulis.RNA editing was biased toward G/C base and has the potential to regulate RNA synthesis and affected editing events in the most widely distributed organelles.In addition,the analysis also showed that the RNA editing in the organelles was closely related to the flowering stage of Phyllostachys edulis.It was likely to participate in the formation of cell membrane functions during the young stage.A total of 37 RNA editing sites in Phyllostachys edulis were predicted in chloroplasts,which occured more frequently in ndh-like genes.RNA editing has higher preference editing in leucine.In order to facilitate users to predict the RNA editing sites using their own data,we also used machine learning algorithm to build an online prediction tool using support vector machine(SVM),which was highly accurate.Users can upload non-nuclear gene sequences of plant to predict RNA editing sites in organelles.In addition to the site prediction tool,this project has also developed other bioinformatics tools to provide a rich facility for molecular biology research in bamboo.Users can submit custom sequences through our website for VIGS design,CRISPR/Cas9 target sequence design and visualization of alignment sequence in bamboo.These tools present valueable information for design based on the purpose of downstream experiment.In summary,this project provides a multi-functional database platform for researchers to use multi-omics epigenetics data and multiple bioinformatics tools for investigation of functional mechanism in Phyllostachys edulis.
Keywords/Search Tags:Phyllostachys edulis, epigenetics, biological databases, DNA methylation, RNA editing, machine learning
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