Computational Core for Plant Metabolomics (CCPM v4.4)

Welcome to CCPM!

CCPM provides a secure repository and analysis pipeline for metabolomics data and associated information. Separate modules have been designed for preprocessing, Pre-treatment and statistical analysis integrated with visualization tools. To access this database, one must register and get an account & password. There are published projects for registered users to view, analyze and download.

To create a new project and deposit data (unpublished) one needs to get access as Principal Investigator (PI). For this direct request to be sent to system Owner/PI of CCPM. Please note that only PI has the right to create new project, delete/modify and publish existing projects. Please check “User Manual” to learn how to use CCPM. We regularly organize workshops to familiarize users with CCPM.

Please check “Events” for upcoming workshops.

Citing CCPM:

If CCPM was useful to you, please acknowledge by directly citing the web address: http://metabolomics.iiit.ac.in/

Our team is working on integration of other omics-based datasets (genomics, transcriptomics and proteomics) with CCPM to facilitate biological interpretation of the data and pathway analysis. Stay tuned for the updates!

Computational Core for Plant Metabolomics (CCPM) is a multi – institutional collaborative project, funded by the Department of Biotechnology, Govt. of India, being carried out at two institutes: IIIT Hyderabad and JNU, New Delhi. The objectives are the following:

• Develop a data repository which can capture, secure and serve metabolomic data
• Provide tools and metabolomics web resources for analysis of the data
• Provide connectivity for integration of metabolomics data with genomic, transcriptomic and proteomic databases

Portal Overview

The functionalities of this portal is organized using a modular architecture.

• Module I is for data uploading. This module enables capture of raw instrumental data and associated metadata about the sample source and handling and about the instruments used along with their specifications and parameter settings. (Completed and functional)

• Module II is for data preprocessing and has two functionalities:

  • Preprocessing of raw instrumental data to remove noise and redundancies and to extract relevant information to enable visualization of the preprocessed data in terms of quantitative groupwise comparison, generation of peak list, identification of significant peaks and linking peak list data to metabolite databases. (Mostly completed and functional)
  • Identification of metabolites from peak list data. This includes connectivity to other database resources. (Ongoing)

• Module III is for data analysis. The functionalities may be summarized as follows:

  • Data Pre-treatment and transformation (partially completed)
  • Statistical analysis of metabolite data. (ongoing).
  • Connectivity to Genomics, Transcriptomics, Proteomics and Pathway related resources using metabolomics analysis results.(To be initiated soon).

Privileges for Registered Users

• A registered user may browse data and analysis results reported in projects that have been published in this portal. Currently the published projects mainly contain data from published papers available in the net.
• Registered users, after appropriate approval, may also create their own projects and carry out operations corresponding to the currently completed and functional modules. Such projects will be stored as ‘Unpublished’ projects and will be visible only to the ‘Owner’ of the project, or to those who have been explicitly permitted by the ‘Owner’. Please register and browse ‘Help’ for further details.

Release Notes for CCPM v4.4

New functionalities of CCPM v4.4 are as follows:

• NMR Module

  NMR Metabolite identification tool uses the open source metabohunter scripts for semi-automatic assignment of 1D NMR spectra of metabolites. The metabolite identification interface search two major publicly available NMR database (HMDB: Human Metabolome Database and MMCD: Madison Metabolomics Consortium Database) in background and display the result output from the selected database based on user input parameters. This tool for metabolite identification is based on spectra or peak lists with different search methods and with possibility for peak drift in a user defined spectral range.
  
  Link: New NMR Pre-processing Task button on Preprocessing page for the selected project

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• Metabolite Correlation Edgelist

  This feature can be used to export metabolite correlation matrix as an edge list with user-defined cut-offs for correlation values. The edge list can be used as an input to network visualization softwares, such as Cytoscape.
  
  Link: Download Cytoscape Input File button on the results page of Statistical Analysis (using correlation)

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• Multigroup

  Multigroup comparison allows user for the identification of differentially expressed metabolite features across multiple classes of data. multigroup analysis aims to identify differences between groups and reveal the diversity of metabolic patterns across different groups.
  
  Multiple sets can now be selected while adding a new Preprocessing task

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• Bulk Upload

  Meta data for multiple groups and its samples in a project can be uploaded as a single .csv file by the user. The corresponding file can be uploaded by clicking the button Choose File followed by clicking Upload Bulk Data. User can also upload Multiple Raw files with a group simultaneously using this Bulk Upload option.
  
  Link: Upload Bulk Data button on group details page

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• KEGG Module Integration

  The KEGG module can be used for mapping and visualizing a given set of metabolites on to KEGG pathways. The user can upload a list of metabolites along with fold-change/p values (optional) to retrieve the associated KEGG pathways and modules. In case no information is available for the given species, mapping can be done on the KEGG Reference Pathway. The fold-change/p values can be used for rendering the visualization of pathway maps.
  
  On preprocessing results page, user can download filtered diffreport, fill in KEGG IDs and upload the file directly to KEGG module