Trends in the development of miRNA bioinformatics tools

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  • Liang Chen
    Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China
  • Liisa Heikkinen
    Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China
  • Changliang Wang
    Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China
  • Yang Yang
    Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China
  • Huiyan Sun
    Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
  • Garry Wong
    Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R, China

Abstract

<jats:title>Abstract</jats:title><jats:p>MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression via recognition of cognate sequences and interference of transcriptional, translational or epigenetic processes. Bioinformatics tools developed for miRNA study include those for miRNA prediction and discovery, structure, analysis and target prediction. We manually curated 95 review papers and ∼1000 miRNA bioinformatics tools published since 2003. We classified and ranked them based on citation number or PageRank score, and then performed network analysis and text mining (TM) to study the miRNA tools development trends. Five key trends were observed: (1) miRNA identification and target prediction have been hot spots in the past decade; (2) manual curation and TM are the main methods for collecting miRNA knowledge from literature; (3) most early tools are well maintained and widely used; (4) classic machine learning methods retain their utility; however, novel ones have begun to emerge; (5) disease-associated miRNA tools are emerging. Our analysis yields significant insight into the past development and future directions of miRNA tools.</jats:p>

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