title: Nonlinear dimensionality reduction methods for synthetic biology biobricks' visualization.
abstract: BACKGROUND
  Visualizing data by dimensionality reduction is an important strategy in Bioinformatics, which could help to discover hidden data properties and detect data quality issues, e.g. data noise, inappropriately labeled data, etc. As crowdsourcing-based synthetic biology databases face similar data quality issues, we propose to visualize biobricks to tackle them. However, existing dimensionality reduction methods could not be directly applied on biobricks datasets. Hereby, we use normalized edit distance to enhance dimensionality reduction methods, including Isomap and Laplacian Eigenmaps.
  
  
  RESULTS
  By extracting biobricks from synthetic biology database Registry of Standard Biological Parts, six combinations of various types of biobricks are tested. The visualization graphs illustrate discriminated biobricks and inappropriately labeled biobricks. Clustering algorithm K-means is adopted to quantify the reduction results. The average clustering accuracy for Isomap and Laplacian Eigenmaps are 0.857 and 0.844, respectively. Besides, Laplacian Eigenmaps is 5 times faster than Isomap, and its visualization graph is more concentrated to discriminate biobricks.
  
  
  CONCLUSIONS
  By combining normalized edit distance with Isomap and Laplacian Eigenmaps, synthetic biology biobircks are successfully visualized in two dimensional space. Various types of biobricks could be discriminated and inappropriately labeled biobricks could be determined, which could help to assess crowdsourcing-based synthetic biology databases' quality, and make biobricks selection.
authors.affiliation: School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei, 230009, China.
authors.forename: Jiaoyun
authors.initials: J
authors.lastname: Yang
authors.affiliation: School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei, 230009, China.
authors.forename: Haipeng
authors.initials: H
authors.lastname: Wang
authors.affiliation: School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei, 230009, China.
authors.forename: Huitong
authors.initials: H
authors.lastname: Ding
authors.affiliation: School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei, 230009, China. ning.g.an@acm.org.
authors.forename: Ning
authors.initials: N
authors.lastname: An
authors.affiliation: Harvard Medical School, Boston Children's Hospital, Boston, 02115, MA, USA.
authors.forename: Gil
authors.initials: G
authors.lastname: Alterovitz
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D015203:Reproducibility of Results; D017422:Sequence Analysis, DNA; D058615:Synthetic Biology
keywords: Biobricks; Dimensionality reduction; Edit distance; Synthetic biology; Visualization