title: Effect of k-tuple length on sample-comparison with high-throughput sequencing data.
abstract: The high-throughput metagenomic sequencing offers a powerful technique to compare the microbial communities. Without requiring extra reference sequences, alignment-free models with short k-tuple (k = 2-10 bp) yielded promising results. Short k-tuples describe the overall statistical distribution, but is hard to capture the specific characteristics inside one microbial community. Longer k-tuple contains more abundant information. However, because the frequency vector of long k-tuple(k ≥ 30 bp) is sparse, the statistical measures designed for short k-tuples are not applicable. In our study, we considered each tuple as a meaningful word and then each sequencing data as a document composed of the words. Therefore, the comparison between two sequencing data is processed as "topic analysis of documents" in text mining. We designed a pipeline with long k-tuple features to compare metagenomic samples combined using algorithms from text mining and pattern recognition. The pipeline is available at http://culotuple.codeplex.com/. Experiments show that our pipeline with long k-tuple features: ①separates genomes with high similarity; ②outperforms short k-tuple models in all experiments. When k ≥ 12, the short k-tuple measures are not applicable anymore. When k is between 20 and 40, long k-tuple pipeline obtains much better grouping results; ③is free from the effect of sequencing platforms/protocols. ③We obtained meaningful and supported biological results on the 40-tuples selected for comparison.
authors.affiliation: Department of Automation, Xiamen University, Fujian 361005, China. Electronic address: wangying@xmu.edu.cn.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Department of Automation, Xiamen University, Fujian 361005, China.
authors.forename: Xiaoye
authors.initials: X
authors.lastname: Lei
authors.affiliation: Department of Automation, Xiamen University, Fujian 361005, China.
authors.forename: Shun
authors.initials: S
authors.lastname: Wang
authors.affiliation: Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing 100084, China.
authors.forename: Zicheng
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Department of Automation, Xiamen University, Fujian 361005, China.
authors.forename: Nianfeng
authors.initials: N
authors.lastname: Song
authors.affiliation: Department of Automation, Xiamen University, Fujian 361005, China.
authors.forename: Feng
authors.initials: F
authors.lastname: Zeng
authors.affiliation: Bioinformatics Division, TNLIST and Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China; Program in Computational Biology and Bioinformatics, University of Southern California, Los Angeles, CA 90089, USA.
authors.forename: Ting
authors.initials: T
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001419:Bacteria / Q000145:classification / Q000235:genetics* / Q000302:isolation & purification; D001483:Base Sequence; D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D054892:Metagenome / Q000235:genetics*; D064307:Microbiota / Q000235:genetics*; D008969:Molecular Sequence Data; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods
keywords: Alignment-free; Clustering; High throughput sequencing; Long k-tuple; Metagenomics; Text mining