Difference between revisions of "GO Terms A4 HUMAN"

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===GOPET===
  
===GOPET===
   
 
First, we used GOPET to predict the GO terms of the protein.
  
First, we used GOPET to predict the GO terms of the protein.
 
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[[Image:a4_human_gopet.png|center|Result of the GOPET prediction for A4_HUMAN]]
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[[Image:a4_human_gopet.png|center|800px|thumb|Figure 1: Result of the GOPET prediction for A4_HUMAN]]
   
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The method only predicts functional GO terms. A4_HUMAN has 11 annotated GO functions. The methods predicts 13 GO function terms. Therefore we decided to check if all predictions are correct.
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The method only predicts functional GO terms. A4_HUMAN has 11 annotated GO functions. The methods predicts 13 GO function terms, which can be seen in Figure 1. Therefore we decided to check if all predictions are correct.
   
 
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Graphical representation of the prediction result of Pfam:
 
Graphical representation of the prediction result of Pfam:
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[[Image:a4_human_pfam.png|center|Result of the Pfam prediction for A4_HUMAN]]
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[[Image:a4_human_pfam.png|center|800px|thumb|Figure 2: Result of the Pfam prediction for A4_HUMAN]]
   
 
Pfam found six significant Pfam-A matches:
  
Pfam found six significant Pfam-A matches:
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ProtFun 2.2 does not give clear predictions if the protein belongs to this class or not, instead it gives probabilities and odd scores.
 
ProtFun 2.2 does not give clear predictions if the protein belongs to this class or not, instead it gives probabilities and odd scores.
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We decided to make a cutoff by 2. So all classes with an odd score of 2 or higher are right results for us. You can also find a "=>" sign in the result file. This sign shows the result with the highest information content. We also take this line as result, although if the odd score is lower than 2. If we only have result with a odd score lower than 2, the line with this sign is our onlyest result.<br>
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We decided to make a cutoff by 2. So all classes with an odd score of 2 or higher are results for us. You can also find a "=>" sign in the result file. This sign shows the result with the highest information content. We also take this line as result, although if the odd score is lower than 2. If we only have result with a odd score lower than 2, the line with this sign is our onliest result.<br>
 
Because the prediction categories are very general, it was not possible to map the GOids. Therefore, we checked the known GO annotations. If there was a hint for a category and the protein was predicted to be in this category, we decided that the prediction is right, otherwise if the known GO annotations and the categories conflict, we count the prediction as wrong.
  
Because the prediction categories are very general, it was not possible to map the GOids. Therefore, we checked the known GO annotations. If there was a hint for a category and the protein was predicted to be in this category, we decided that the prediction is right, otherwise if the known GO annotations and the categories conflict, we count the prediction as wrong.
 
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Latest revision as of 22:36, 30 August 2011

GOPET

First, we used GOPET to predict the GO terms of the protein.

Figure 1: Result of the GOPET prediction for A4_HUMAN

The method only predicts functional GO terms. A4_HUMAN has 11 annotated GO functions. The methods predicts 13 GO function terms, which can be seen in Figure 1. Therefore we decided to check if all predictions are correct.

GO term confidence prediction term prediction GOid
endopeptidase inhibitor activity 87% right wrong
serine-type endopeptidase inhibitor activity 86% right right
plasmin inhibitor activity 83% wrong wrong
trypsin inhibitor activtiy 83% wrong wrong
peptidase inhibitor activity 82% right right
binding 79% right right
protein binding 74% right right
metal ion binding 73% right right
DNA binding 71% right right
heparin binding 70% wrong right
zinc ion binding 69% wrong wrong
copper ion binding 69% wrong wrong
iron ion binding 67% wrong wrong


Back to [sequence-based prediction]

Pfam

We used the webserver for our analysis. We decided to only trust the significant Pfam-A matches. To check if the predictions are correct we mapped the Pfam ids to the Go ids with help of a mapping website [[1]]. If a successful mapping was not possible, we compared the names of the predicted Pfam family with the names of the GO terms. If the names are similar or equal, we decided to trust the mapping.

Graphical representation of the prediction result of Pfam:

Figure 2: Result of the Pfam prediction for A4_HUMAN

Pfam found six significant Pfam-A matches:

Family E-Value GOid prediction
Amyloid A4 N-terminal heparin-binding 4e-42 none right
Copper-binding of amyloid precursor CuBD 2.3e-27 none right
Kunitz/Bovine pancreatic trypsin inhibitor domain 3e-19 GO:0004867 right
E2 domain of amyloid precursor protein 1.6e-74 none right
Beta-amyloid peptide (beta-APP) 4.3e-28 GO:0005488 right
GO:0016021 right
Beta-amyloid precursor protein C-terminus 1.1e-29 none right


Back to [sequence-based prediction]

ProtFun 2.2



ProtFun 2.2 does not give clear predictions if the protein belongs to this class or not, instead it gives probabilities and odd scores. We decided to make a cutoff by 2. So all classes with an odd score of 2 or higher are results for us. You can also find a "=>" sign in the result file. This sign shows the result with the highest information content. We also take this line as result, although if the odd score is lower than 2. If we only have result with a odd score lower than 2, the line with this sign is our onliest result.
Because the prediction categories are very general, it was not possible to map the GOids. Therefore, we checked the known GO annotations. If there was a hint for a category and the protein was predicted to be in this category, we decided that the prediction is right, otherwise if the known GO annotations and the categories conflict, we count the prediction as wrong.

The ProtFun Server calculated following prediction result for A4_HUMAN:

Functional category
Functional category Probabilty Odd score Prediction
Amino acid biosynthesis 0.020 0.921 right
Biosynthesis of cofactors 0.261 3.623 right
Cell envelope 0.804 => 13.186 => right
Cellular processes 0.053 0.070 right
Central intermediary metabolism 0.184 2.920 right
Engergy metabolism 0.023 0.259 right
Fatty acid metabolsim 0.016 1.265 right
Purines and Pyrimidines 0.417 1.716 right
Regulatory functions 0.013 0.084 wrong
Replication and transcription 0.029 0.109 right
Translation 0.027 0.613 right
Transport and binding 0.827 2.016 right
Enyzme/non-enzyme
Enzyme/non-enzyme Probability Odd score Prediction
Enzyme 0.392 => 1.368 => right
Nonenzyme 0.608 0.852 right
Enyzme class
Enzyme class Probability Odd score Prediction
Oxidoreductase (EC 1.-.-.-) 0.024 0.114 right
Transferase (EC 2.-.-.-) 0.208 0.603 right
Hydrolase (EC 3.-.-.-) 0.190 0.600 right
Lyase (EC 4.-.-.-) 0.020 0.430 right
Isomerase (EC 5.-.-.-) 0.010 0.324 right
Ligase (EC 6.-.-.-) 0.048 0.946 right
Gene ontology category
Gene ontology category Probability Odd score Prediction
Signal transducer 0.126 0.586 right
Receptor 0.036 0.211 right
Hormone 0.001 0.206 right
Structural protein 0.034 => 1.205 => right
Transporter 0.024 0.222 right
Ion channel 0.009 0.162 right
Volatge-gated ion channel 0.002 0.108 right
Cation channel 0.010 0.215 right
Transcription 0.043 0.335 right
Transcription regulation 0.018 0.143 right
Stress response 0.076 0.862 right
Immune response 0.016 0.183 right
Growth factor 0.005 0.372 right
Metal ion transport 0.009 0.020 right


Back to [sequence-based prediction]

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