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GoTest.drw
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GoTest.drw
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#
# A script to measures the similarity between the putative orthologs
# based on the code of the benchmark paper.
#
# Adrian Altenhoff, Sep 2006
# rewritten Adrian Altenhoff, Jul 13, 2007
# rewritten for BenchService Adrian Altenhoff, Dec 4, 2009
# rewritten for EBench Adrian Altenhoff, Feb 2019
#
# input arguments:
# refset_path path to reference dataset
# project_db path to predictions in darwin db format
# title name of the method which is evaluated
# assessment_fname filename where the assessment file should be written
# community_id community id
# measure similarity measure, e.g 'avg Schlicker'
# evidences evidence codes to consider, passed as a list/set, e.g. [EXP, IDA]
# out_dir directory where output is written to. must exist
printlevel := 2;
Set(printgc=false); Set(gc=5e6):
if not assigned(refset_path) then
error('refset_path not assigned');
fi:
if not assigned(community_id) or not assigned(out_dir) then
error('community_id and out_dir all must be defined');
fi:
existing_evidence_codes := {'EXP','IDA','IPI','IMP','IGI','IEP', # experiments
'HTP','HDA','HMP','HGI','HEP', # high throughput
'IBA','IBD','IKR','IRD', # phylogenetic
'ISS','ISO','ISA','ISM','IGC','RCA', # computational analysis
'TSA','NSA', # Author statement
'IC', 'ND', 'IEA'}: # curator / electronic
if not type(evidences, {string, list}) then
error('evidences not propperly assigned');
elif type(evidences, string) then
if lowercase(evidences) = 'all' then evidences := intersect();
elif lowercase(evidences) = 'exp' then
evidences := ['EXP','IDA','IPI','IMP','IGI','IEP'];
elif lowercase(evidences) = 'cur' then
evidences := [op(minus(existing_evidence_codes, {'ND','IEA'}))];
else
error('invalid evidence code class: '.evidences);
fi:
fi:
filter := {op(evidences)} intersect existing_evidence_codes;
if length(filter)=0 then error('no evidences provided that make sense') fi:
# hacky thing to make sure Ontolgy_name is loaded and can be accessed.
GOname(55):
allterms := Indices(Ontology_name):
GOparents := table({}):
for i in allterms do GOparents[i] := GOsuperclassR(i) od:
DB := GODB := ReadDb(refset_path.'/ServerIndexed.db'):
lprint('GO annotations from '.GODB['FileName']);
# extract important all GO annotations from ServerIndexed.db
LoadGOset := proc()
global DB, freqs, GOs, nProt;
nProt := GODB[TotEntries]:
GOs := CreateArray(1..nProt,[]):
DB := GODB;
for eNr to nProt do
gos := SearchTag('GO', Entry(eNr));
if gos='' then next fi;
gos := SearchDelim('; ',gos);
gos := [seq(SearchDelim('@',z), z=gos)];
for z in gos do
annotation := parse(z[2]);
evidence := {seq( x[1], x=annotation)};
if length( intersect(evidence, filter) )>0 then
GOs[eNr] := append(GOs[eNr], parse(z[1,4..-1]) );
fi;
od:
od:
# compute GO term frequencies of proteins in iSet;
freqs := table(0); cnts := 0;
for p in GOs do for go in p do
cnts := cnts+1;
freqs[go] := freqs[go]+1;
for parent in GOparents[go] do freqs[parent] := freqs[parent]+1; od:
od od:
if cnts>0 then for i in Indices(freqs) do freqs[i] := freqs[i]/cnts od fi:
end:
ComputeSimilarity := proc(data:list; 'method'=(method:string))
if method='avg Sim' then
return( avg(seq(z[2],z=data)) );
elif method='max Sim' then
return( max(seq(z[2],z=data)) );
elif method='avg Info' then
return( avg(seq(z[3], z=data)) );
elif method='max Info' then
return( max(seq(z[3], z=data)) );
elif method='avg Schlicker' then
max1 := CreateArray(1..max(seq(z[4],z=data)),1..2,-DBL_MAX);
max2 := CreateArray(1..max(seq(z[5],z=data)),1..2,-DBL_MAX);
for i to length(data) do
z := data[i];
if z[2] > max1[z[4],1] then max1[z[4]] := [z[2],i]; fi;
if z[2] > max2[z[5],1] then max2[z[5]] := [z[2],i]; fi;
od:
s := c := 0;
for i in [op(max1),op(max2)] do
if i[1]>-DBL_MAX then s := s+i[1]; c:=c+1; fi:
od:
return( s/c );
else error('similarity not implemented') fi:
end:
ComputePerformance := proc(prjDB, title)
global DB;
# measure similarities between putative orthologs
Sim := Stat(title);
onts := [3674, 5575, 8150]:
rawData := []:
DB := prjDB;
last_timereport := time():
for eNr to nProt do
if length(GOs[eNr]) > 0 then
vps := ParseLongList(SearchTag('VP', Entry(eNr)));
for vp in vps do
if vp<eNr then next fi: # uni-directional
if length(GOs[vp]) = 0 then next fi:
pairs := [];
for ig1 to length(GOs[eNr]) do for ig2 to length(GOs[vp]) do
go1 := GOs[eNr,ig1]; go2 := GOs[vp,ig2];
ic := intersect( {go1, op(GOparents[go1])},
{go2, op(GOparents[go2])} );
if length(ic)=0 then next fi: # skip if not same ontology
ont := sum(SearchArray(z, onts), z=ic); # get ontology
if ont<1 or ont>3 then next fi:
# 2*ln(prob(interClass[eNr]))
# similarity sim = ---------------------------
# ln(prob(go1))+ln(prob(go2))
simRun := infoRun := -DBL_MAX;
for int in ic do
info := -2*ln(freqs[int]);
sim := If(info=0, 0, -info/(ln(freqs[go1])+ln(freqs[go2])));
if info > infoRun then infoRun := info; infoInt := int fi;
if sim > simRun then simRun := sim; simInt := int fi;
od:
pairs := append(pairs, [ont, simRun, infoRun, ig1, ig2] );
od od:
if length(pairs)>0 then
simPair := ComputeSimilarity(pairs, 'method'=measure);
Sim + simPair;
rawData := append(rawData, [eNr, vp, simPair]);
fi:
od:
fi;
if time() - last_timereport > 10 then
t := eNr/nProt;
printf('%.0f%% done. Estimated remaining time: %.0fsec\n', 100*t, (1-t)/t*time());
last_timereport := time();
fi:
od:
return(Sim, rawData):
end:
StoreRawData := proc(rawData, name, fname_)
fname := fname_;
if length(fname) > 4 and fname[-3..-1] = '.gz' then
fname := fname[1..-4];
do_gzip := true;
else do_gzip := false fi:
OpenWriting(fname);
printf('# GO Similarities between orthologs from %s\n', name);
printf('# Computing timestamp: %s\n', date());
printf('# Protein ID 1<tab>Protein ID 2<tab>GO Similarity\n');
for z in rawData do
id1 := ENr2XRef(z[1]);
id2 := ENr2XRef(z[2]);
printf('%s\t%s\t%f\n', id1, id2, z[3]);
od:
OpenWriting(previous);
if do_gzip then CallSystem('gzip -9f '.fname); fi:
end:
StoreResult := proc(fn:string, data)
OpenWriting(fn): prints(json(data)): OpenWriting(previous);
end:
projDB := ReadDb(project_db);
title_id := ReplaceString(' ','-', ReplaceString('_', '-', title));
raw_out_fn := sprintf('GO_%s_%s_raw.txt.gz', title_id, sha2(string([filter, measure, title, project_db]))[1..12]);
LoadGOset():
printf('loaded %d go annotations\n', sum(length(z), z=GOs));
result := table():
result['evidences'] := filter;
result['measure'] := measure;
t := ComputePerformance(projDB, title):
perf := t[1]; raw_data := t[2];
result['nr_orthologs'] := perf['Number'];
result['similarity'] := perf['Mean'];
result['stderr'] := perf['StdErr'];
result['raw_data_fn'] := raw_out_fn:
assessments := [AssessmentDataset(community_id, 'GO', title, 'NR_ORTHOLOGS', perf['Number'], 0),
AssessmentDataset(community_id, 'GO', title, measure, perf['Mean'], perf['StdErr'])];
StoreRawData(raw_data, title, out_dir.'/'.result['raw_data_fn']):
StoreResult(assessment_fname, assessments);
done;