1. CSSSCL: a python package that uses combined sequence similarity scores for accurate taxonomic classification of long and short sequence reads
- Author
-
Vincent Ferretti and Ivan Borozan
- Subjects
0301 basic medicine ,Statistics and Probability ,Source code ,media_common.quotation_subject ,Genomics ,Sequence alignment ,Biology ,computer.software_genre ,Biochemistry ,DNA sequencing ,03 medical and health sciences ,Software ,Molecular Biology ,Alignment-free sequence analysis ,Phylogeny ,media_common ,Bacteria ,business.industry ,Sequence Analysis, DNA ,Models, Theoretical ,Applications Notes ,Computer Science Applications ,Computational Mathematics ,030104 developmental biology ,Computational Theory and Mathematics ,Metagenomics ,Viruses ,Data mining ,business ,Classifier (UML) ,computer ,Sequence Analysis ,Sequence Alignment ,Algorithms - Abstract
Summary: 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. Availability and implementation: Package’s source code and test datasets are available under the GPLv3 license at https://github.com/oicr-ibc/cssscl. Contact: ivan.borozan@oicr.on.ca Supplementary information: Supplementary data are available at Bioinformatics online.
- Published
- 2015