Selection of suitable bioinformatic tools in micro-RNA research

Abstract MicroRNAs (miRNAs) are essential components of gene regulatory networks. miRNAs participate in the regulation of biological processes and thereby have promising potentials to act as valuable diagnostic biomarkers and even therapeutic molecules in various diseases. Bioinformatics analysis is a pivotal initial step of microRNAs research. The main functions of bioinformatics are extensive biodata mining, processing, and analysis of raw data to provide precious and accurate results based on in-silico statistical and mathematical methods. This work aims to evaluate the most widely used bioinformatics tools in microRNAs research in a critical viewpoint and to suggest a helpful list of tools according to the significant features. For this purpose, a wide range of microRNA-related bioinformatics tools in the field of “microRNA search/targets/pathway/mutation/disease/and biomarker discovery” were searched and studied. To examine the advantages and disadvantages of every applied tool “miR-183”, “miR-182”, “miR-96”, “ATOH1 (Atonal BHLH Transcription Factor 1)”, and Hearing loss disorder were used as a search keyword. As a result, among 162 studied bioinformatics tools, a total of 38 were selected based on distinct features, including result reliability, service accessibility, usefulness, and user satisfaction. The presence of a large number of currently available microRNAs-bioinformatics tools and the continuing development of new tools makes confusion and complexity in the selection of proper tools for a particular study. Here we have introduced and discussed the most helpful Bioinformatics Tools.

[1]  Margarita Zachariou,et al.  Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches , 2017, Briefings Bioinform..

[2]  Anders Krogh,et al.  miRMaid: a unified programming interface for microRNA data resources , 2010, BMC Bioinformatics.

[3]  Sanghyuk Lee,et al.  miRGator v3.0: a microRNA portal for deep sequencing, expression profiling and mRNA targeting , 2012, Nucleic Acids Res..

[4]  Athanasios Fevgas,et al.  DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions , 2014, Nucleic Acids Res..

[5]  Angela Re,et al.  CircuitsDB: a database of mixed microRNA/transcription factor feed-forward regulatory circuits in human and mouse , 2010, BMC Bioinformatics.

[6]  C. García-Estrada,et al.  The inducers 1,3-diaminopropane and spermidine cause the reprogramming of metabolism in Penicillium chrysogenum, leading to multiple vesicles and penicillin overproduction. , 2013, Journal of proteomics.

[7]  Yves A. Lussier,et al.  Advantages of genomic complexity: bioinformatics opportunities in microRNA cancer signatures , 2011, J. Am. Medical Informatics Assoc..

[8]  Brad T. Sherman,et al.  DAVID-WS: a stateful web service to facilitate gene/protein list analysis , 2012, Bioinform..

[9]  Ralf Zimmer,et al.  miRSel: Automated extraction of associations between microRNAs and genes from the biomedical literature , 2010, BMC Bioinformatics.

[10]  John J Rossi,et al.  MicroRNAs in Disease and Potential Therapeutic Applications. , 2007, Molecular therapy : the journal of the American Society of Gene Therapy.

[11]  M. Primig.  The bioinformatics tool box for reproductive biology. , 2012, Biochimica et biophysica acta.

[12]  Yan Zhang,et al.  TMREC: A Database of Transcription Factor and MiRNA Regulatory Cascades in Human Diseases , 2015, PloS one.

[13]  Noam Shomron,et al.  Pharmaco-miR: linking microRNAs and drug effects , 2013, Briefings Bioinform..

[14]  Ivo Grosse,et al.  Functional microRNA targets in protein coding sequences , 2012, Bioinform..

[15]  M. Jami,et al.  In Vitro Differentiation of Human Bone Marrow Mesenchymal Stem Cells to Hair Cells Using Growth Factors. , 2017, The international tinnitus journal.

[16]  Yadong Wang,et al.  miR2Disease: a manually curated database for microRNA deregulation in human disease , 2008, Nucleic Acids Res..

[17]  S. Lawler,et al.  MicroRNAs in cancer: biomarkers, functions and therapy. , 2014, Trends in molecular medicine.

[18]  Xiaowei Wang,et al.  miRDB: an online resource for microRNA target prediction and functional annotations , 2014, Nucleic Acids Res..

[19]  A. Sahu.  Host-Virus Interaction: Role of miRNA and Bioinformatics Tools for miRNA Target Prediction , 2015 .

[20]  Ana M. Aransay,et al.  miRanalyzer: an update on the detection and analysis of microRNAs in high-throughput sequencing experiments , 2011, Nucleic Acids Res..

[21]  Kenneth W Witwer,et al.  Circulating microRNA biomarker studies: pitfalls and potential solutions. , 2015, Clinical chemistry.

[22]  Tsai-Tien Tseng,et al.  Bioinformatics Resources for MicroRNA Discovery , 2015, Biomarker insights.

[23]  Yan Cui,et al.  PolymiRTS Database 3.0: linking polymorphisms in microRNAs and their target sites with human diseases and biological pathways , 2013, Nucleic Acids Res..

[24]  Xing-Ming Zhao,et al.  mTD: A database of microRNAs affecting therapeutic effects of drugs. , 2017, Journal of genetics and genomics = Yi chuan xue bao.

[25]  M. Jami,et al.  Comparison of Three Types of Mesenchymal Stem Cells (Bone Marrow, Adipose Tissue, and Umbilical Cord-Derived) as Potential Sources for Inner Ear Regeneration. , 2017, The international tinnitus journal.

[26]  H. Dweep,et al.  miRWalk2.0: a comprehensive atlas of microRNA-target interactions , 2015, Nature Methods.

[27]  Fabian J Theis,et al.  PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes , 2010, Genome Biology.

[28]  V. Beneš,et al.  miRNA gene counts in chromosomes vary widely in a species and biogenesis of miRNA largely depends on transcription or post-transcriptional processing of coding genes , 2014, Front. Genet..

[29]  Piotr Zielenkiewicz,et al.  Tools4miRs – one place to gather all the tools for miRNA analysis , 2016, Bioinform..

[30]  C. Lawrie,et al.  New Concepts in Cancer Biomarkers: Circulating miRNAs in Liquid Biopsies , 2016, International journal of molecular sciences.

[31]  Tongbin Li,et al.  miRecords: an integrated resource for microRNA–target interactions , 2008, Nucleic Acids Res..

[32]  D. Bartel.  MicroRNAs: Target Recognition and Regulatory Functions , 2009, Cell.

[33]  Andreas Keller,et al.  A comprehensive, cell specific microRNA catalogue of human peripheral blood , 2017, Nucleic acids research.

[34]  L. Papa,et al.  A Panel of Serum MiRNA Biomarkers for the Diagnosis of Severe to Mild Traumatic Brain Injury in Humans , 2016, Scientific Reports.

[35]  Norbert Gretz,et al.  miRWalk - Database: Prediction of possible miRNA binding sites by "walking" the genes of three genomes , 2011, J. Biomed. Informatics.

[36]  Zheng Guo,et al.  Systematic review regulatory principles of non‐coding RNAs in cardiovascular diseases , 2019, Briefings Bioinform..

[37]  Ana Kozomara,et al.  miRBase: annotating high confidence microRNAs using deep sequencing data , 2013, Nucleic Acids Res..

[38]  Yao Guo,et al.  Bioinformatics-Based Identification of MicroRNA-Regulated and Rheumatoid Arthritis-Associated Genes , 2015, PloS one.

[39]  R. Shankar,et al.  miReader: Discovering Novel miRNAs in Species without Sequenced Genome , 2013, PloS one.

[40]  Gholamreza Farnoosh,et al.  The Effect of the MicroRNA-183 Family on Hair Cell-Specific Markers of Human Bone Marrow-Derived Mesenchymal Stem Cells , 2018, Audiology and Neurotology.

[41]  Yan Cui,et al.  miR2GO: comparative functional analysis for microRNAs , 2015, Bioinform..

[42]  Subrata Sen,et al.  MicroRNA as Biomarkers and Diagnostics , 2016, Journal of cellular physiology.

[43]  Malik Yousef,et al.  A study of microRNAs in silico and in vivo: bioinformatics approaches to microRNA discovery and target identification , 2009, The FEBS journal.

[44]  Martin Reczko,et al.  DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows , 2013, Nucleic Acids Res..

[45]  Artemis G. Hatzigeorgiou,et al.  DIANA-mirExTra v2.0: Uncovering microRNAs and transcription factors with crucial roles in NGS expression data , 2016, Nucleic Acids Res..

[46]  C. Burge,et al.  The microRNAs of Caenorhabditis elegans. , 2003, Genes & development.

[47]  A. O. Chiromatzo,et al.  miRNApath: a database of miRNAs, target genes and metabolic pathways. , 2007, Genetics and molecular research : GMR.

[48]  Sanghamitra Bandyopadhyay,et al.  PuTmiR: A database for extracting neighboring transcription factors of human microRNAs , 2010, BMC Bioinformatics.

[49]  Most Mauluda Akhtar,et al.  Bioinformatic tools for microRNA dissection , 2015, Nucleic acids research.

[50]  Martin M Matzuk,et al.  A bioinformatics tool for linking gene expression profiling results with public databases of microRNA target predictions. , 2008, RNA.

[51]  M. Jami,et al.  MicroRNAs: effective elements in ear-related diseases and hearing loss , 2017, European Archives of Oto-Rhino-Laryngology.

[52]  Bairong Shen,et al.  Identification of candidate miRNA biomarkers from miRNA regulatory network with application to prostate cancer , 2014, Journal of Translational Medicine.

[53]  Tao Jiang,et al.  Circulating microRNAs in cancer: origin, function and application , 2012, Journal of Experimental & Clinical Cancer Research.

[54]  M. Gerstein,et al.  What is bioinformatics ? An introduction and overview , 2001 .

[55]  Di Wu,et al.  miRCancer: a microRNA-cancer association database constructed by text mining on literature , 2013, Bioinform..

[56]  Filip Pattyn,et al.  The microRNA body map: dissecting microRNA function through integrative genomics , 2011, Nucleic acids research.

[57]  M. Jami,et al.  Proteome analysis reveals roles of L-DOPA in response to oxidative stress in neurons , 2014, BMC Neuroscience.

[58]  Vincent J. Henry,et al.  OMICtools: an informative directory for multi-omic data analysis , 2014, Database J. Biol. Databases Curation.

[59]  M. Jami,et al.  MicroRNA-183 Family in Inner Ear: Hair Cell Development and Deafness , 2016, Journal of audiology & otology.

[60]  Matthias Blum,et al.  miRmap web: comprehensive microRNA target prediction online , 2013, Nucleic Acids Res..

[61]  M. Jami,et al.  Increased levels of miR-124 in human dental pulp stem cells alter the expression of neural markers , 2019, Journal of otology.

[62]  Pasquale Caianiello,et al.  Bioinformatics approach to predict target genes for dysregulated microRNAs in hepatocellular carcinoma: study on a chemically-induced HCC mouse model , 2015, BMC Bioinformatics.

[63]  Hsien-Da Huang,et al.  ViTa: prediction of host microRNAs targets on viruses , 2006, Nucleic Acids Res..

[64]  Kwong-Sak Leung,et al.  ViRBase: a resource for virus–host ncRNA-associated interactions , 2014, Nucleic Acids Res..

[65]  R. Knight,et al.  MIRUMIR: an online tool to test microRNAs as biomarkers to predict survival in cancer using multiple clinical data sets , 2012, Cell Death and Differentiation.

[66]  B. Davis-Dusenbery,et al.  Mechanisms of control of microRNA biogenesis. , 2010, Journal of biochemistry.

[67]  Yuanfang Guan,et al.  miRmine: a database of human miRNA expression profiles , 2017, Bioinform..

[68]  Michael Hackenberg,et al.  sRNAtoolbox: an integrated collection of small RNA research tools , 2015, Nucleic Acids Res..

[69]  Hsien-Da Huang,et al.  miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions , 2017, Nucleic Acids Res..

[70]  Olaf Wolkenhauer,et al.  Cooperative gene regulation by microRNA pairs and their identification using a computational workflow , 2014, Nucleic acids research.