GO Terms RET4 HUMAN

From Bioinformatikpedia
Revision as of 23:33, 30 August 2011 by Link (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)

GOPET

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

Figure 1: Result of the GOPET prediction for RET4_HUMAN

The method only predicts functional GO terms. RET4_HUMAN has 7 annotated GO functions. The methods predicts 8 GO function terms, which can be seen on Figure 1. Therefore, we decided to check if all predictions are correct.

GO term confidence prediction term prediction GOid
binding 90% right right
retiniod binding 81& right right
lipid binding 80% wrong wrong
retional binding 78% right right
transporter activity 78% right right
retinal binding 78% right right
lipid transport activity 69% wrong wrong
high-density lipoprotein particle binding 60% 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 RET4_HUMAN

Pfam found one significant Pfam-A matches:

Family E-Value GOid prediction
Lipocalin/cytosolic fatty-acid binding protein family 1.7e-22 GO:0005488 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 RET4_HUMAN:

Functional category
Functional category Probability Odd score Prediction
Amino acid biosynthesis 0.017 0.751 right
Biosynthesis of cofactors 0.044 0.610 right
Cell envelope 0.804 => 13.186 => right
Cellular processes 0.075 1.021 wrong
Central intermediary metabolism 0.197 3.128 right
Engergy metabolism 0.043 0.475 right
Fatty acid metabolsim 0.016 1.265 right
Purines and Pyrimidines 0.275 1.131 right
Regulatory functions 0.013 0.080 right
Replication and transcription 0.022 0.084 right
Translation 0.032 0.721 right
Transport and binding 0.800 1.951 wrong
Enyzme/non-enzyme
Enzyme/non-enzyme Probabilty Odd score Prediction
Enzyme 0.544 => 1.900 => right
Nonenzyme 0.456 0.639 right
Enyzme class
Enzyme class Probabilty Odd score Prediction
Oxidoreductase (EC 1.-.-.-) 0.095 0.458 right
Transferase (EC 2.-.-.-) 0.038 0.109 right
Hydrolase (EC 3.-.-.-) 0.235 0.742 right
Lyase (EC 4.-.-.-) 0.059 => 1.264 => wrong
Isomerase (EC 5.-.-.-) 0.010 0.321 right
Ligase (EC 6.-.-.-) 0.017 0.326 right
Gene ontology category
Gene ontology category Probability Odd score Prediction
Signal transducer 0.202 0.942 right
Receptor 0.147 0.862 right
Hormone 0.004 0.667 right
Structural protein 0.002 0.058 right
Transporter 0.025 0.232 right
Ion channel 0.016 0.288 right
Volatge-gated ion channel 0.003 0.148 right
Cation channel 0.010 0.215 right
Transcription 0.027 0.207 right
Transcription regulation 0.025 0.196 right
Stress response 0.161 1.829 right
Immune response 0.239 => 2.813 => wrong
Growth factor 0.023 1.617 right
Metal ion transport 0.009 0.020 right


Back to [Sequence-based prediction]