title: Automatically clustering large-scale miRNA sequences: methods and experiments.
abstract: BACKGROUND
  Since the initial annotation of microRNAs (miRNAs) in 2001, many studies have sought to identify additional miRNAs experimentally or computationally in various species. MiRNAs act with the Argonaut family of proteins to regulate target messenger RNAs (mRNAs) post-transcriptionally. Currently, researches mainly focus on single miRNA function study. Considering that members in the same miRNA family might participate in the same pathway or regulate the same target(s) and thus share similar biological functions, people can explore useful knowledge from high quality miRNA family architecture.
  
  
  RESULTS
  In this article, we developed an unsupervised clustering-based method miRCluster to automatically group miRNAs. In order to evaluate this method, several data sets were constructed from the online database miRBase. Results showed that miRCluster can efficiently arrange miRNAs (e.g identify 354 families in miRBase16 with an accuracy of 92.08%, and can recognize 9 of all 10 newly-added families in miRBase 17). By far, ~30% mature miRNAs registered in miRBase are unclassified. With miRCluster, over 85% unclassified miRNAs can be assigned to certain families, while ~44% of these miRNAs distributed in ~300novel families.
  
  
  CONCLUSIONS
  In short, miRCluster is an automatic and efficient miRNA family identification method, which does not require any prior knowledge. It can be helpful in real use, especially when exploring functions of novel miRNAs. All relevant materials could be freely accessed online (http://admis.fudan.edu.cn/projects/miRCluster).
authors.affiliation: School of Computer Science, Fudan University, Shanghai 200433, China.
authors.forename: Linxia
authors.initials: L
authors.lastname: Wan
authors.forename: Jiandong
authors.initials: J
authors.lastname: Ding
authors.forename: Ting
authors.initials: T
authors.lastname: Jin
authors.forename: Jihong
authors.initials: J
authors.lastname: Guan
authors.forename: Shuigeng
authors.initials: S
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D000818:Animals; D001331:Automation; D016000:Cluster Analysis; D030541:Databases, Genetic; D020407:Internet; D035683:MicroRNAs / Q000737:chemistry / Q000145:classification / Q000378:metabolism*; D010944:Plants / Q000235:genetics; D014584:User-Computer Interface; D014780:Viruses / Q000235:genetics
chemical_list: D035683:MicroRNAs