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Biological ingredient analysis of traditional Chinese medicine preparation based on high-throughput sequencing: the story for Liuwei Dihuang Wan

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      Abstract

      Although Traditional Chinese Medicine (TCM) preparations have long history with successful applications, the scientific and systematic quality assessment of TCM preparations mainly focuses on chemical constituents and is far from comprehensive. There are currently only few primitive studies on assessment of biological ingredients in TCM preparations. Here, we have proposed a method, M-TCM, for biological assessment of the quality of TCM preparations based on high-throughput sequencing and metagenomic analysis. We have tested this method on Liuwei Dihuang Wan (LDW), a TCM whose ingredients have been well-defined. Our results have shown that firstly, this method could determine the biological ingredients of LDW preparations. Secondly, the quality and stability of LDW varies significantly among different manufacturers. Thirdly, the overall quality of LDW samples is significantly affected by their biological contaminations. This novel strategy has the potential to achieve comprehensive ingredient profiling of TCM preparations.

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      Cytoscape: a software environment for integrated models of biomolecular interaction networks.

      Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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        GenBank

        GenBank® (http://www.ncbi.nlm.nih.gov) is a comprehensive database that contains publicly available nucleotide sequences for almost 260 000 formally described species. These sequences are obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects, including whole-genome shotgun (WGS) and environmental sampling projects. Most submissions are made using the web-based BankIt or standalone Sequin programs, and GenBank staff assigns accession numbers upon data receipt. Daily data exchange with the European Nucleotide Archive (ENA) and the DNA Data Bank of Japan (DDBJ) ensures worldwide coverage. GenBank is accessible through the NCBI Entrez retrieval system, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, begin at the NCBI home page: www.ncbi.nlm.nih.gov.
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          Reducing the Effects of PCR Amplification and Sequencing Artifacts on 16S rRNA-Based Studies

          The advent of next generation sequencing has coincided with a growth in interest in using these approaches to better understand the role of the structure and function of the microbial communities in human, animal, and environmental health. Yet, use of next generation sequencing to perform 16S rRNA gene sequence surveys has resulted in considerable controversy surrounding the effects of sequencing errors on downstream analyses. We analyzed 2.7×106 reads distributed among 90 identical mock community samples, which were collections of genomic DNA from 21 different species with known 16S rRNA gene sequences; we observed an average error rate of 0.0060. To improve this error rate, we evaluated numerous methods of identifying bad sequence reads, identifying regions within reads of poor quality, and correcting base calls and were able to reduce the overall error rate to 0.0002. Implementation of the PyroNoise algorithm provided the best combination of error rate, sequence length, and number of sequences. Perhaps more problematic than sequencing errors was the presence of chimeras generated during PCR. Because we knew the true sequences within the mock community and the chimeras they could form, we identified 8% of the raw sequence reads as chimeric. After quality filtering the raw sequences and using the Uchime chimera detection program, the overall chimera rate decreased to 1%. The chimeras that could not be detected were largely responsible for the identification of spurious operational taxonomic units (OTUs) and genus-level phylotypes. The number of spurious OTUs and phylotypes increased with sequencing effort indicating that comparison of communities should be made using an equal number of sequences. Finally, we applied our improved quality-filtering pipeline to several benchmarking studies and observed that even with our stringent data curation pipeline, biases in the data generation pipeline and batch effects were observed that could potentially confound the interpretation of microbial community data.
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            Author and article information

            Affiliations
            [1 ]Institute of Materia Medica, Shandong Academy of Medical Sciences , Jinan, Shandong, 250062, China
            [2 ]Shandong Key Laboratory of Energy Genetics, CAS Key Laboratory of Biofuels and Bioinformatics Group of SingleCell Center, Qingdao Institute of Bioenergy and Bioprocess Technology , Chinese Academy of Sciences, Qingdao266101, China
            [3 ]School of Medicine and Life Sciences, , University of Jinan-Shandong Academy of Medical Sciences, Jinan 250062, China
            [4 ]These authors contributed equally to this work.
            Author notes
            Journal
            Sci Rep
            Sci Rep
            Scientific Reports
            Nature Publishing Group
            2045-2322
            03 June 2014
            2014
            : 4
            24888649
            4042125
            srep05147
            10.1038/srep05147
            Copyright © 2014, Macmillan Publishers Limited. All rights reserved

            This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. The images in this article are included in the article's Creative Commons license, unless indicated otherwise in the image credit; if the image is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the image. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/

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