Release-Notes

Welcome to MMIP Server Release-Notes.

MMIP Server Transfer, OS, and Package Update 15/June/2024

  • Note: We have installed MMIP on the new server, and operating system, many packages have been updated. As a result, some outcomes might differ due to changes in the seed and algorithms of these packages. Please let us know if you see high changes in results.

MMIP has been published 28/September/2023

  • MMIP is now published in Briefings in Bioinformatics(28/September/2023). Kindly read the full-length article here Link.

v.1.0.2: 12/August/2023

  • We have incorporated Generalized UniFrac or GUniFrac calculations, encompassing weighted, unweighted, and Alpha 0.5 variations, for beta diversity analysis. Furthermore, the main result page now offers the Bray-Curtis index, along with a link for investigating UniFrac (weighted, unweighted), generalized UniFrac, or GUniFrac (weighted, unweighted UniFrac, Alpha 0.5) with ordination using PCoA and NMDS.

v.1.0.1: 15/July/2023

  • Version 1.0.1 of MMIP has been released
  • The module name has been updated from MMIP to Module-I, and MMIP-MASS has been changed to Module-II.
  • Support for user-defined databases has been added, making the system independent of specific databases.
  • Users now have the option to upload representative sequence and taxonomy files if they prefer to use a database other than Greengenes. This allows for flexibility in choosing any database or using a database-independent approach
  • A model selection module has been incorporated in the submission page, allowing users to choose their preferred machine learning model. Additional models, such as Random Forest and Decision Tree, have been included to offer more choices.
  • An abundance-based filtering module has been introduced and included in the submission page.
  • Effective number calculations for alpha diversity have been implemented. Users can now view effective number conversions for Shannon and Simpson indices. The result page provides links to pages displaying the effective number calculations.
  • UniFrac calculations, including both weighted and unweighted variants, have been integrated for beta diversity analysis. Additionally, on the result page, users can find Bray Curtis calculations along with a link to access UniFrac calculations.
  • NMDS-based measures have been integrated to calculate beta diversity.
  • The machine learning method has been enhanced for improved robustness. We encourage you to read our publication for more details. Additionally, we have provided additional results to users on the server.
  • We have implemented a robust quality control pipeline that includes error checking for user-uploaded data. If any errors are detected, users will be notified via email and provided with an error log for reference.
  • The job queue system has been updated to process three jobs in parallel.

v.1.0.0: 04/March/2023

  • Version 1.0.0 of MMIP has been released