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 classifiedmore » 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. Conclusion: 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.« less

Authors:

Zhao, Weizhong ^[1]; Chen, James J. ^[2]; Perkins, Roger ^[2]; Wang, Yuping ^[2]; Liu, Zhichao ^[2]; Hong, Huixiao ^[2]; Tong, Weida ^[2]; Zou, Wen ^[2]

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+ Show Author Affiliations

1. U.S. Food and Drug Administration (FDA), Jefferson, AR (United States). Division of Bioinformatics and Biostatistics, National Center for Toxicological Research; Xiangtan Univ. Xiangtan (China). College of Information Engineering
2. U.S. Food and Drug Administration (FDA), Jefferson, AR (United States). Division of Bioinformatics and Biostatistics, National Center for Toxicological Research

Publication Date:

Fri May 13 00:00:00 EDT 2016

Research Org.:

Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN (United States)

Sponsoring Org.:

USDOE Office of Science (SC)

OSTI Identifier:

1626761

Grant/Contract Number:  

SC0014664

Resource Type:

Accepted Manuscript

Journal Name:

BMC Bioinformatics

Additional Journal Information:

Journal Volume: 17; Journal Issue: 1; Journal ID: ISSN 1471-2105

Publisher:

BioMed Central

Country of Publication:

United States

Language:

English

Subject:

97 MATHEMATICS AND COMPUTING; 59 BASIC BIOLOGICAL SCIENCES; Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology; Data mining; Topic modeling; Next-generation sequencing (NGS); Genetic diversity; Biomarker