CDKAM: a taxonomic classification tool using discriminative k-mers and approximate matching strategies
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CDKAM: a taxonomic classification tool using discriminative k‑mers and approximate matching strategies Van‑Kien Bui1 and Chaochun Wei1,2*
*Correspondence: [email protected] 1 Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China Full list of author information is available at the end of the article
Abstract Background: Current taxonomic classification tools use exact string matching algorithms that are effective to tackle the data from the next generation sequencing technology. However, the unique error patterns in the third generation sequencing (TGS) technologies could reduce the accuracy of these programs. Results: We developed a Classification tool using Discriminative K-mers and Approxi‑ mate Matching algorithm (CDKAM). This approximate matching method was used for searching k-mers, which included two phases, a quick mapping phase and a dynamic programming phase. Simulated datasets as well as real TGS datasets have been tested to compare the performance of CDKAM with existing methods. We showed that CDKAM performed better in many aspects, especially when classifying TGS data with average length 1000–1500 bases. Conclusions: CDKAM is an effective program with higher accuracy and lower memory requirement for TGS metagenome sequence classification. It produces a high species-level accuracy. Keywords: Third generation sequencing, Taxonomic classification, Discriminative k-mer, Approximate matching
Background Metagenome sequencing is a powerful approach to study microbial communities in natural environments [1]. In a pipeline for the metagenomics project, taxonomic classification aims to accurately assign each fragment to its corresponding host organism and is one of the most important initial steps. With the progress of sequencing technology, modern metagenomics methods need to deal with vast sequence datasets. Identifying taxa for billions of reads according to a reference database with many thousands of microbial genomes available today is becoming a time-consuming process. As the database from NCBI is continuously growing and being more complete, we have to consider the trade-off between the size of the reference database and the classification accuracy as well as the computational cost.
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