ArticleBioinformatics (Oxford, England)2016
CSSSCL: a python package that uses combined sequence similarity scores for accurate taxonomic classification of long and short sequence reads.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Music of metagenomics-a review of its applications, analysis pipeline, and associated tools.Functional & integrative genomics · 2022Review
- The landscape of viral associations in human cancers.Nature genetics · 2020Article
- DisCVR: Rapid viral diagnosis from high-throughput sequencing data.Virus evolution · 2019Article
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Authors and funding
2 authors.
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Abstract
summarySequence 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.
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