MetaP: The ensemble algorithm for predicting protein subcellular locations

see Manual

Citation note:If you use metaP in your publications, please cite the following publication:
(1) Jianjun Hu. MetaP:Ensemble prediction of Protein subcellular Locations. (Submitted to Bioinformatics)
Step 1: If your sequence file contains very messy sequenceIDs, we suggest u to convert the seqID to numbered seqID before running individual prediction algorithms. convert here

Step 2: submit your sequence file to individual protein location prediction algorithms. click on the links below to go to their websites. (Algorithm author: If you are interested in putting your algorithm here,please Email us).
CELLO    LocTree    Proteome Analyst   PClassifier   SLP   SubLoc   PSortB

Step 3: Paste your sequences or upload input sequence file along with output files from standalone algorithms. (Pasted sequences have preference over sequence file). The output files are the original output files in text format from prediction algorithms. If it is a html file, save it as text file. metaP will parse the text file rather than html file. Download sample datafile here to verify the format of your data files. If one of your prediction algorithm is not listed below, you can convert its output to standard format to feed to metaP.

Paste the query sequences in FASTA format below

Input Sequence Fasta File:
CELLO output file:
LocTree output file:
Proteome Analyst output file:
PClassifier output file:
SLP output file:
SubLoc output file:
PSortB output file:
Standard output file1:
Standard output file2:
Standard output file3:
Standard output file4:
Standard output file5:

Considersub-optimal predictions only top prediction
Consider Effect of closest neighbor location Effect of all locations No effect of neighbor locations

Todo tasks:
  • automatically submit the sequence file to other algorithm servers and get the results back and then generate the consensus predictions.
  • upload files in one tar.gz file if standard suffix is used for the algorithm output files.

This material is based upon work supported by the National Science Foundation under Grant No. 0845381.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Last Modified: April 16, 2010