PFP

A MATLAB library for protein function prediction.

Matlab functions in this package uses "pfp_" (aka. Protein Function Prediction) as the file name prefix.

Commonly used data structures

1. ONT, the ontology structure, see pfp_ontbuild.m. This structure has the following fields:

   • required fields:
     • term: a list of term structures (id, name).
     • rel_code: a list of relationship codes, e.g. {is_a, part_of}.
     • DAG: the relationship matrix, DAG(i, j) = k (k>0) means term(i) has k-th relationship of rel_code with term(j).
     • ont_type: the ontology type, e.g. molecular_function.
     • date: the date that this structure is built.
   • optional fields:
     • alt_list: the alternate term ID list.

2. OA, the ontology annotation structure, see pfp_oabuild.m. This structure has the following fields:

   • required fields:
     • object: the object (sequence) list.
     • ontology: the associated ontology structure.
     • annotation: the annotation matrix, annotation(i, j) = 1 means object(i) is annotated with term(j).
     • date: the date that this structure is built.

3. PRED, the prediction structure, see (built by any prediction methods, similar to OA). This structure has the following fields:

   • required fields:
     • object: the object (sequence) list.
     • ontology: the associated ontology structure.
     • score: the predicted score matrix, score(i,j) is the predicted score for the association of object(i) and term(j).
     • date: the date that this prediction is made.
   • optional fields: (see CAFA rules for details of these fields)
     • author: the author of this predictor.
     • model: the model number.
     • keywords: the keywords of this predictor.
     • tag: some additional information.

4. NET, the network structure, see pfp_netbuild.m. This structure has the following fields:

   • required fields:
     • object: the object (node) list.
     • ADJ: the adjacency matrix.
     • date: the date that this network is built.

How to build baseline predictors (BLAST, GOtcha, Naive)

BLAST predictor

Requirements

• "Training" data

  • Sequences in FASTA format.

  • Annotations (MFO terms for exmaple) for each of these sequences. This data needs to be prepared ahead of time as a two-column CSV file (delimited by TAB)

    <sequence ID>\t<GO term ID>
    

    where <sequence ID> would be of any system (e.g., UniProt accession number), as long as they are consistant with those used in the FASTA file.

• NCBI BLAST tool (used 2.2.29+ for this document)
• Query sequences in FASTA format.

Step-by-step

• STEP 1: Load annotations of training sequences.

  oa = pfp_oabuild(ont, 'annotation.dat');

  where ont is a MATLAB structure of ontology which can be built from and OBO file (say, 'ontology.obo') as

  ont = pfp_ontbuild('ontology.obo');

  Note that a typical gene ontology OBO file contains all three GO ontologies (i.e., MFO, BPO, and CCO), therefore, pfp_ontbuild returns a cell of THREE ontology strcutures instead:

  onts = pfp_ontbuild('go.obo')

  By default, they are ordered as BPO, CCO, MFO, alphabetically. You can also double check the .ont_type field of each returning structure.

• STEP 2: Prepare BLAST results

  • Run blastp on the query sequences against the "training" sequences by setting output format to be the following:

    blastp ... -outfmt "6 qseqid sseqid evalue length pident nident" -out blastp.out

  • Load the tabular output file (blastp.out as shown above) into MATLAB:

    B = pfp_importblastp('blastp.out');

• STEP 3: Build the BLAST predictor

  blast = pfp_blast(qseqid, B, oa);

  where qseqid is a cell list of query sequences on which you need scores. Note that it can be just a subset of all those you BLAST'ed. B is the structure imported step 2, while oa is the ontology annotation structure loaded in step 1.

  Also, extra options can be specified as additional arguments to this function. See the documentation of pfp_blast.m for more details. Thus, blast will be the BLAST predictor in MATLAB for evaluation.

GOtcha predictor

GOtcha predictor can be build in the similar way of BLAST predictor.

gotcha = pfp_gotcha(qseqid, B, oa);

Naive predictor

To build a naive predictor, all you need is the ontology annotation structure oa that you have as in the step 1 of making a BLAST predictor. Then run the following in MATLAB:

naive = pfp_naive(qseqid, oa);