title: Centroid based clustering of high throughput sequencing reads based on n-mer counts.
abstract: BACKGROUND
  Many problems in computational biology require alignment-free sequence comparisons. One of the common tasks involving sequence comparison is sequence clustering. Here we apply methods of alignment-free comparison (in particular, comparison using sequence composition) to the challenge of sequence clustering.
  
  
  RESULTS
  We study several centroid based algorithms for clustering sequences based on word counts. Study of their performance shows that using k-means algorithm with or without the data whitening is efficient from the computational point of view. A higher clustering accuracy can be achieved using the soft expectation maximization method, whereby each sequence is attributed to each cluster with a specific probability. We implement an open source tool for alignment-free clustering. It is publicly available from github: https://github.com/luscinius/afcluster.
  
  
  CONCLUSIONS
  We show the utility of alignment-free sequence clustering for high throughput sequencing analysis despite its limitations. In particular, it allows one to perform assembly with reduced resources and a minimal loss of quality. The major factor affecting performance of alignment-free read clustering is the length of the read.
authors.affiliation: Center for Infection and Immunity, Columbia University, New York, NY, 10032, USA. avs2132@columbia.edu.
authors.forename: Alexander
authors.initials: A
authors.lastname: Solovyov
authors.forename: W Ian
authors.initials: WI
authors.lastname: Lipkin
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D020407:Internet; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software