title: A novel procedure on next generation sequencing data analysis using text mining algorithm.
abstract: BACKGROUND
  Next-generation sequencing (NGS) technologies have provided researchers with vast possibilities in various biological and biomedical research areas. Efficient data mining strategies are in high demand for large scale comparative and evolutional studies to be performed on the large amounts of data derived from NGS projects. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining.
  
  
  METHODS
  We report a novel procedure to analyse NGS data using topic modeling. It consists of four major procedures: NGS data retrieval, preprocessing, topic modeling, and data mining using Latent Dirichlet Allocation (LDA) topic outputs. The NGS data set of the Salmonella enterica strains were used as a case study to show the workflow of this procedure. The perplexity measurement of the topic numbers and the convergence efficiencies of Gibbs sampling were calculated and discussed for achieving the best result from the proposed procedure.
  
  
  RESULTS
  The output topics by LDA algorithms could be treated as features of Salmonella strains to accurately describe the genetic diversity of fliC gene in various serotypes. The results of a two-way hierarchical clustering and data matrix analysis on LDA-derived matrices successfully classified Salmonella serotypes based on the NGS data. The implementation of topic modeling in NGS data analysis procedure provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data.
  
  
  CONCLUSIONS
  The implementation of topic modeling in NGS data analysis provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data.
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.|College of Information Engineering, Xiangtan University, Xiangtan, Hunan Province, China.
authors.forename: Weizhong
authors.initials: W
authors.lastname: Zhao
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.
authors.forename: James J
authors.initials: JJ
authors.lastname: Chen
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.
authors.forename: Roger
authors.initials: R
authors.lastname: Perkins
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.
authors.forename: Yuping
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.
authors.forename: Zhichao
authors.initials: Z
authors.lastname: Liu
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.
authors.forename: Huixiao
authors.initials: H
authors.lastname: Hong
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA.
authors.forename: Weida
authors.initials: W
authors.lastname: Tong
authors.affiliation: Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA. wen.zou@fda.hhs.gov.
authors.forename: Wen
authors.initials: W
authors.lastname: Zou
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000032:analysis; D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D008962:Models, Theoretical; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D012475:Salmonella / Q000145:classification / Q000235:genetics; D012703:Serotyping
chemical_list: D015415:Biomarkers
keywords: Biomarker; Data mining; Genetic diversity; Next-generation sequencing (NGS); Topic modeling