title: CSSSCL: a python package that uses combined sequence similarity scores for accurate taxonomic classification of long and short sequence reads.
abstract: CONCLUSIONS
  Sequence comparison of genetic material between known and unknown organisms plays a crucial role in genomics, metagenomics and phylogenetic analysis. The emerging long-read sequencing technologies can now produce reads of tens of kilobases in length that promise a more accurate assessment of their origin. To facilitate the classification of long and short DNA sequences, we have developed a Python package that implements a new sequence classification model that we have demonstrated to improve the classification accuracy when compared with other state of the art classification methods. For the purpose of validation, and to demonstrate its usefulness, we test the combined sequence similarity score classifier (CSSSCL) using three different datasets, including a metagenomic dataset composed of short reads.
  
  
  METHODS
  Package's source code and test datasets are available under the GPLv3 license at https://github.com/oicr-ibc/cssscl.
  
  
  BACKGROUND
  ivan.borozan@oicr.on.ca
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Informatics and Bio-computing, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario, Canada.
authors.forename: Ivan
authors.initials: I
authors.lastname: Borozan
authors.affiliation: Informatics and Bio-computing, Ontario Institute for Cancer Research, MaRS Centre, 661 University Avenue, Suite 510, Toronto, Ontario, Canada.
authors.forename: Vincent
authors.initials: V
authors.lastname: Ferretti
mesh_terms: D000465:Algorithms; D001419:Bacteria / Q000145:classification* / Q000235:genetics; D056186:Metagenomics / Q000379:methods*; D008962:Models, Theoretical; D010802:Phylogeny; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA; D012984:Software; D014780:Viruses / Q000145:classification* / Q000235:genetics