PlantMirnaT: miRNA and mRNA integrated analysis fully utilizing characteristics of plant sequencing data.

miRNA is known to regulate up to several hundreds coding genes, thus the integrated analysis of miRNA and mRNA expression data is an important problem. Unfortunately, the integrated analysis is challenging since it needs to consider expression data of two different types, miRNA and mRNA, and target relationship between miRNA and mRNA is not clear, especially when microarray data is used. Fortunately, due to the low sequencing cost, small RNA and RNA sequencing are routinely processed and we may be able to infer regulation relationships between miRNAs and mRNAs more accurately by using sequencing data. However, no method is developed specifically for sequencing data. Thus we developed PlantMirnaT, a new miRNA-mRNA integrated analysis system. To fully leverage the power of sequencing data, three major features are developed and implemented in PlantMirnaT. First, we implemented a plant-specific short read mapping tool based on recent discoveries on miRNA target relationship in plant. Second, we designed and implemented an algorithm considering miRNA targets in the full intragenic region, not just 3' UTR. Lastly but most importantly, our algorithm is designed to consider quantity of miRNA expression and its distribution on target mRNAs. The new algorithm was used to characterize rice under drought condition using our proprietary data. Our algorithm successfully discovered that two miRNAs, miRNA1425-5p, miRNA 398b, that are involved in suppression of glucose pathway in a naturally drought resistant rice, Vandana. The system can be downloaded at https://sites.google.com/site/biohealthinformaticslab/resources.

[1]  Martin Reczko,et al.  Accurate microRNA Target Prediction Using Detailed Binding Site Accessibility and Machine Learning on Proteomics Data , 2011, Front. Gene..

[2]  R. Giegerich,et al.  Fast and effective prediction of microRNA/target duplexes. , 2004, RNA.

[3]  Javier F. Palatnik,et al.  Control of leaf morphogenesis by microRNAs , 2003, Nature.

[4]  Dirk Inzé,et al.  The Agony of Choice: How Plants Balance Growth and Survival under Water-Limiting Conditions1 , 2013, Plant Physiology.

[5]  D. Bartel,et al.  MicroRNAS and their regulatory roles in plants. , 2006, Annual review of plant biology.

[6]  Enrico Macii,et al.  miREE: miRNA recognition elements ensemble , 2011, BMC Bioinformatics.

[7]  Knut Reinert,et al.  SeqAn An efficient, generic C++ library for sequence analysis , 2008, BMC Bioinformatics.

[8]  Yves Van de Peer,et al.  TAPIR, a web server for the prediction of plant microRNA targets, including target mimics , 2010, Bioinform..

[9]  Michael Kertesz,et al.  The role of site accessibility in microRNA target recognition , 2007, Nature Genetics.

[10]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[11]  H. K. Park,et al.  Overexpression of the Transcription Factor AP37 in Rice Improves Grain Yield under Drought Conditions1[W][OA] , 2009, Plant Physiology.

[12]  Baohong Zhang,et al.  Bioinformatics Applications Note Data and Text Mining Target-align: a Tool for Plant Microrna Target Identification , 2022 .

[13]  F Remacle,et al.  miRNA and mRNA cancer signatures determined by analysis of expression levels in large cohorts of patients , 2013, Proceedings of the National Academy of Sciences.

[14]  Meng Wang,et al.  PsRobot: a web-based plant small RNA meta-analysis toolbox , 2012, Nucleic Acids Res..

[15]  Grace X. Y. Zheng,et al.  MicroRNAs can generate thresholds in target gene expression , 2011, Nature Genetics.

[16]  S. Ghosh,et al.  Mitochondria in innate immune responses , 2011, Nature Reviews Immunology.

[17]  Sungroh Yoon,et al.  Ensemble learning can significantly improve human microRNA target prediction. , 2014, Methods.

[18]  M. Ludlow,et al.  Carbohydrate Metabolism in Drought-Stressed Leaves of Pigeonpea (Cajanus cajan) , 1993 .

[19]  Anton J. Enright,et al.  MicroRNA targets in Drosophila , 2003, Genome Biology.

[20]  C. Burge,et al.  Most mammalian mRNAs are conserved targets of microRNAs. , 2008, Genome research.

[21]  Brad T. Sherman,et al.  Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists , 2008, Nucleic acids research.

[22]  B. Frey,et al.  Using expression profiling data to identify human microRNA targets , 2007, Nature Methods.

[23]  Yunlong Liu,et al.  Computational analysis of microRNA profiles and their target genes suggests significant involvement in breast cancer antiestrogen resistance , 2009, Bioinform..

[24]  Stijn van Dongen,et al.  miRBase: microRNA sequences, targets and gene nomenclature , 2005, Nucleic Acids Res..

[25]  A. Luttun,et al.  Quantification of miRNA-mRNA Interactions , 2012, PloS one.

[26]  Kenneth P. Nephew,et al.  BioVLAB-MMIA-NGS: microRNA-mRNA integrated analysis using high-throughput sequencing data , 2015, Bioinform..

[27]  Wanchen Li,et al.  Differential Expression of MicroRNAs in Response to Drought Stress in Maize , 2013 .

[28]  Lin Shi,et al.  Plant microRNA-Target Interaction Identification Model Based on the Integration of Prediction Tools and Support Vector Machine , 2014, PloS one.

[29]  Yun Zheng,et al.  Transcriptome-wide identification of microRNA targets in rice. , 2010, The Plant journal : for cell and molecular biology.

[30]  G. Xue,et al.  Use of expression analysis to dissect alterations in carbohydrate metabolism in wheat leaves during drought stress , 2008, Plant Molecular Biology.

[31]  L. Sieburth,et al.  Widespread Translational Inhibition by Plant miRNAs and siRNAs , 2008, Science.

[32]  B. Engelmann.  Plasmalogens: targets for oxidants and major lipophilic antioxidants. , 2004, Biochemical Society transactions.

[33]  C. Burge,et al.  Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets , 2005, Cell.

[34]  Cole Trapnell,et al.  Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. , 2010, Nature biotechnology.

[35]  R. Martienssen,et al.  miRNAs trigger widespread epigenetically-activated siRNAs from transposons in Arabidopsis , 2014, Nature.

[36]  Zhangjun Fei,et al.  Transcriptional profiles of drought-responsive genes in modulating transcription signal transduction, and biochemical pathways in tomato , 2010, Journal of experimental botany.

[37]  T. Dalmay,et al.  miR398 and miR408 are up-regulated in response to water deficit in Medicago truncatula , 2010, Planta.

[38]  D. Tollervey,et al.  Mapping the Human miRNA Interactome by CLASH Reveals Frequent Noncanonical Binding , 2013, Cell.

[39]  Curtis Balch,et al.  MicroRNA and mRNA integrated analysis (MMIA): a web tool for examining biological functions of microRNA expression , 2009, Nucleic Acids Res..

[40]  Jorge Nocedal,et al.  A Limited Memory Algorithm for Bound Constrained Optimization , 1995, SIAM J. Sci. Comput..

[41]  H. Budak,et al.  miRNA expression patterns of Triticum dicoccoides in response to shock drought stress , 2011, Planta.

[42]  Ziv Bar-Joseph,et al.  Integrating sequence, expression and interaction data to determine condition-specific miRNA regulation , 2013, Bioinform..

[43]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[44]  Edwards Allen,et al.  P1/HC-Pro, a viral suppressor of RNA silencing, interferes with Arabidopsis development and miRNA unction. , 2003, Developmental cell.

[45]  Louise C. Showe,et al.  Naïve Bayes for microRNA target predictions - machine learning for microRNA targets , 2007, Bioinform..

[46]  A. de Daruvar,et al.  Mapping the proteome of poplar and application to the discovery of drought‐stress responsive proteins , 2006, Proteomics.