Brief Introduction

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Traditional Chinese Medicine (TCM), with a history of thousands of years of clinical practice, plays an important role in maintaining the health of peoples of Asia, and is gaining more and more application all over the world. However, owing to the diversity of TCM’s ingredients and the complexity of TCM’s interaction with human body, it is still quite difficult to uncover the underlying molecular mechanism of TCM. The clarification of the TCM’s molecular mechanism has become a bottleneck in TCM modernization and internationalization. BATMAN-TCM (a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine) is the first online bioinformatics analysis tool specially designed for the research of molecular mechanism of TCM.

For user-submitted TCM, BATMAN-TCM will first predict potential targets for each query TCM’s ingredient, and then perform functional analyses on these targets including Gene Ontology (GO) term, KEGG pathway and OMIM/TTD disease enrichment analyses. TCM ingredient-target-pathway/disease association network and biological pathway with highlighted TCM’s targets will also be shown. These functions aim to contribute to the understanding of the “multi-component, multi-target and multi-pathway” combinational therapeutic mechanism of TCM and to provide clues for the following experimental validation. In addition, BATMAN-TCM also supports users to simultaneously input multiple TCMs, which is typically used to simultaneously analyze multiple compositive herbs of a formula, helping understand this combinational principle of a formula from molecular and systematic level.

Please see Tutorials for more information.


Every user’s submitted data will be kept private and not viewable by anyone other than the user or those given permission by the user.


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1. Please input the interested TCM



* Required Field.

Annotation
Three alternative input types: TCM formula (Pinyin name) (e.g. JIA WEI CAN XIA TANG), Herb or Herb list (Pinyin, English or Latin name) (e.g. ren shen, Ginseng or Panax ginseng) or User defined compositive compound list of the TCM (compounds are denoted by PubChem_CIDs or chemical structures of InChI format). For the second and third input types, multiple herbs/compounds are supported, one herb/compound per line.
For the third input type, currently only PubChem_CID or InChI format is supported. If you have compound lists represented by other types of format, you can use OpenBabel to transfer the format.
By clicking on “Add one cluster”, BATMAN-TCM also supports users to simultaneously input multiple TCM formulas/herbs/Compound lists. Please click on “Example 4” to obtain an example (4 clusters maximum).

2. Parameters setting

  • Target Prediction
  • For each compositive compound, the predicted candidate targets whose scores given by the target prediction method exceed a given cutoff "Score cutoff " (including known targets) will be considered as the potential targets, and will be presented and further analyzed.

    Input parameter Score cutoff:


  • Target Analyses
  • The significantly enriched Gene Ontology functional terms, KEGG biological pathways and OMIM/TTD diseases among the potential targets of the query TCM are analyzed. (During the enrichment analyses, we only consider the predicted candidate targets (including known targets) with scores no smaller than 20 as you set above.)

    Please set the cutoff of P-value after Benjamini-Hochberg multiple testing correction (Adjusted P-value) :

    3. Now start to predict and analyze


    Attention: When the user selects the input type of “compound list”, the E-mail notification function below is highly recommended, because the analyses of the submitted “compound list” generally consume much time. Conservatively a compound averagely consumes about 5 mins.


    Please cite: Liu Z, Guo F, Wang Y, Li C, Zhang X, Li H, Diao L, Gu J, Wang W, Li D, He F. BATMAN-TCM: a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine. Sci Rep. 2016, 6:21146.  [Full text]


    Contact: Zhongyang Liu, liuzy1984@163.com, Beijing Proteome Research Center, Beijing, China.