GO terms general
GOPET (Gene Ontology Term Prediction and Evaluation Tool)
Authors: Vinayagam A, König R, Moormann J, Schubert F, Eils R, Glatting KH, Suhai S
Source: [Applying Support Vector Machines for Gene Ontology based gene function prediction.]
GOPET is a homology-based GO term prediction methods. It tries to assign uncharacterised cDNA sequences to GO molecular function terms. Therefore, the method uses in the first step a Blast search against GO-mapped proteins in a database. The found GO terms and attributes are used as input for a Support Vector Machine, which makes the final classification.<br<>
We used the [Webserver] for our prediction. Therefore, it was only necessary to paste our sequences in FASTA-format and to sumbit the job.
GOPET returns a table with the predicted GOid, the Aspect (Molecular Function Ontology (F), Biological Process Ontology (P) and Cellular Component Ontology (C)), the confidence for the prediction and the GO term itself.
Authors: R.D. Finn, J. Mistry, J. Tate, P. Coggill, A. Heger, J.E. Pollington, O.L. Gavin, P. Gunesekaran, G. Ceric, K. Forslund, L. Holm, E.L. Sonnhammer, S.R. Eddy, A. Bateman
Source: [The Pfam protein families database]
Pfam is also a homology-based prediction method. The domains are saved as hidden markov models. The method uses a naive bayes classifactor and classify the proteins with the aid of the hidden markov models.
We used the [Webserver] for our predictions. Therefore, we chose the point "Sequence search", pasted the protein sequence in FASTA-format and sumbitted the job.
The webserver shows a graphical representation of the prediction and also the matches. There are two categories of matches, significant and insignificant Pfam A-family matches. These matches are listed with family name, a short description, the entry type, Clan, some information about the HMM and the E-Value.
Authors: L. Juhl Jensen, R. Gupta, N. Blom, D. Devos, J. Tamames, C. Kesmir, H. Nielsen, H. H. Stærfeldt, K. Rapacki, C. Workman, C. A. F. Andersen, S. Knudsen, A. Krogh, A. Valencia and S. Brunak.
Source: [Prediction of human protein function from post-translational modifications and localization features.]
ProtFun2.2 is an ab initio prediction method, which try to assign orphan proteins to functional classes. It integrates relevant features which are related to the linear amino acid sequence. Furthermore, it queries a large number of other feature prediction servers (PsiPred, TMHMM and so on). This explains why the prediction with ProtFun is very slow and you have to wait a long time for the prediction result. Techniqually, uses this method an ensemble of five different neuronal networks (which are three-layer feed-forward networks).
We used the [Webserver] in our prediction. The prediction takes a long time and your request is queued, so you have to wait some hours. For the prediciton it is only necessary to paste the sequence in FASTA-format to the input field.
As output, you get a list with different functional categories and with a probability and an odd score. The probability shows you how likely your protein belongs to this class. But the probability is influenced by the prior probability of the class. The second score is an odd score, which shows you if the sequence belongs to this class or not. We decided to make a cutoff by 2. Furthermore, it predicts if your protein is an enzyme and the probability and odd score that this protein belongs to different enzyme classes. The last prediction section of the result file is the prediction for the gene ontology category and also the probabilities and odd scores for that.