:SELECT 
 term.*, motif.*, count(motif.id) AS c
:FROM
 term
 INNER JOIN graph_path ON (graph_path.term1_id = term.id)
 INNER JOIN association ON (association.term_id = graph_path.term2_id)
 INNER JOIN gene_product_seq ON (gene_product_seq.gene_product_id = association.gene_product_id)
 INNER JOIN seq_dbxref ON (gene_product_seq.seq_id = seq_dbxref.seq_id)
 INNER JOIN dbxref AS motif ON (motif.id = seq_dbxref.dbxref_id)
:WHERE
 [ term.acc = &term_acc& ]
 [ term.name = &term_name& ]
 [ motif_dbxref.xref_dbname = &motif_dbname& ]
 [ motif_dbxref.xref_key = &motif_acc& ]
 [ association.is_not = 0 ] 
:GROUP BY term.id, motif.id
:HAVING c > 1
:ORDER BY c DESC
:USE NESTING (set(term(motif)))
//
schema: go
desc: 

   gene_products have sequences attached to them, and these sequences
   have peptide motifs (eg from sources such as pfam and subsumed into
   interpro) attached to them. this query finds which protein motifs
   are correlated with which terms via gene product associations.

   requires seqdb or seqdblite

example_input: term_acc => SELECT acc FROM term
example_input: term_name => SELECT name FROM term ORDER BY name
example_input: motif_dbname => interpro, pfam, prints, prosite, smart