EFICAz2.5

THIS WEBSERVICE HAS BEEN DISCONTINUE. YOU CAN DOWNLOAD EFICAz2.5 BELOW.

EFICAz2.5 (Enzyme Function Inference by a Combined Approach) is an automatic engine for large-scale enzyme function inference that combines predictions from six different methods developed and optimized to achieve high prediction accuracy: (i) recognition of functionally discriminating residues (FDRs) in enzyme families obtained by a Conservation-controlled HMM Iterative procedure for Enzyme Family classification (CHIEFc), (ii) pairwise sequence comparison using a family specific Sequence Identity Threshold, (iii) recognition of FDRs in Multiple Pfam enzyme families, (iv) recognition of multiple Prosite patterns of high specificity, (v) SVM evaluation of CHIEFc families, and (vi) SVM evaluation of Multiple Pfam enzyme families.

NOTE:

  • This web service is freely available to all academic users and not-for-profit institutions.
  • Commercial users wishing an evaluation copy should contact skolnick@gatech.edu .
  • Commercial users may license the EFICAz2.5 software after completing the license agreement and sending it to skolnick@gatech.edu Download the license agreement.


If you find use EFICAz2.5 useful, please cite the following papers:

  • Narendra Kumar and Jeffrey Skolnick (2012) EFICAz2.5: Application of a high-precision enzyme function predictor to 396 proteomes. Bioinformatics (in press).
  • Arakaki A, Huang Y and Skolnick J (2009) EFICAz2: enzyme function inference by a combined approach enhanced by machine learning. BMC Bioinformatics 10:107 PDF
  • Tian W, Arakaki AK, Skolnick J (2004) EFICAz: a comprehensive approach for accurate genome-scale enzyme function inference. Nucleic Acids Research 32(21):6226-39. PDF
  • Download EFICAz


    latest
    release 1.3

    Instructions for installation and usage of EFICAz2.5 are included in the package. To run EFICAz2 (release 1.3), use the following command:
    $> python $EFICAZ_PATH/EFICAz_latest.py input_sequence_fasta

    This service is developed and maintained by Narendra Kumar