title: Using growing self-organising maps to improve the binning process in environmental whole-genome shotgun sequencing.
abstract: Metagenomic projects using whole-genome shotgun (WGS) sequencing produces many unassembled DNA sequences and small contigs. The step of clustering these sequences, based on biological and molecular features, is called binning. A reported strategy for binning that combines oligonucleotide frequency and self-organising maps (SOM) shows high potential. We improve this strategy by identifying suitable training features, implementing a better clustering algorithm, and defining quantitative measures for assessing results. We investigated the suitability of each of di-, tri-, tetra-, and pentanucleotide frequencies. The results show that dinucleotide frequency is not a sufficiently strong signature for binning 10 kb long DNA sequences, compared to the other three. Furthermore, we observed that increased order of oligonucleotide frequency may deteriorate the assignment result in some cases, which indicates the possible existence of optimal species-specific oligonucleotide frequency. We replaced SOM with growing self-organising map (GSOM) where comparable results are obtained while gaining 7%-15% speed improvement.
authors.affiliation: Dynamic Systems & Control Group, Department of Mechanical Engineering, University of Melbourne, VIC 3010, Australia.
authors.forename: Chon-Kit Kenneth
authors.initials: CK
authors.lastname: Chan
authors.forename: Arthur L
authors.initials: AL
authors.lastname: Hsu
authors.forename: Sen-Lin
authors.initials: SL
authors.lastname: Tang
authors.forename: Saman K
authors.initials: SK
authors.lastname: Halgamuge
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002874:Chromosome Mapping / Q000379:methods*; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*