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After demonstration of the accuracy of our pipeline prediction with the training set, the whole predicted proteome of L. interrogans serovar Lai was analyzed using three computational predictions for protein subcellular localization: PSORTb, ProtCompB, and Proteome analyst (PA).
The data was converted using PRIDE converter [80] (http://code.google.com/p/pride-converter). Feature predictions for protein sequences in the proteomic output were automated using local installations of several software packages and Perl scripts.
For methods that do not explicitly make predictions for protein pairs, orthology is defined based on clustering into the same groups (for KOG, OrthoMCL, TribeMCL) or sharing of at least one orthologous domain (for Orthostrapper, RIO).
Sequence analysis then easily results in wrong predictions for protein localization.
38 Predictions for protein interactions exploited the STRING tool, 39 structure analyses, and literature mining.
SUBA houses large scale proteomic and GFP localization sets from cellular compartments of Arabidopsis as well as precompiled bioinformatic predictions for protein subcellular localizations.
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The CSIDOP is shown above to produce highly accurate function predictions for proteins in H. sapiens.
Only three of the methods under investigation (KOG, OrthoMCL, TribeMCL) permit clustering proteins from multiple species, rather than simply making pairwise predictions for proteins from two species.
These data were used by AutoAnnotate to make functional predictions for proteins, which were then made available in the Manatee interface for manual evaluation of the predicted function.
These data used by AutoAnnotate to make functional predictions for proteins were then made available in the Manatee interface for manual evaluation of the predicted function.
Another contributing factor could be that some of the computational predictions for protein-coding genes are false positives and thus cannot be experimentally verified.
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