Protein grouping mechanism is detailled beneath the following image.

Peptides from different identifications - attached directly or indirectly to a context - are grouped.
Peptides must have same sequence and same experimental mass to be grouped.
Peptide grouping results in new peptides attached to the parent context and having child peptides

Peptides from a same protein or a same protein set are grouped.
Protein grouping results in new groups of proteins and peptides, attached to the parent context.

You need to be carefull when grouping proteins within a tree of contexts. Let's take the following example:

Rootnode
  |_ Context1
     |_ F085255.dat
     |_ F085256.dat
     |_ F085257.dat
  |_ Context2
     |_ F085258.dat        
     |_ F085259.dat

It's possible:

• case 1 - to group proteins at the Rootnode level, hEIDI will then group proteins from all the identification results, or
• case 2 - to group proteins starting from the leaf contexts (Context1 and Context2), then ending with the Rootnode.

At present, when launching the protein group algorithm, you can tell hEIDI to filter some proteins and/or peptides. For example, if you decide to filter proteins with a number of peptides lower than 2, it is important to understand that doing this may give different results in cases 1 & 2.

Rootnode
  |_ Context1
     ProtA (pep1, pep2)
  |_ Context2
     ProtA (pep1, pep5)

In case 2, ProtA will be filtered at an early stage (when grouping proteins in Context1 and Context2), and will not appear in the final result.

But in case 1, when grouping proteins at the Rootnode level, ProtA will 'gain' one peptide more (ProtA will be identified by 3 peptides instead of 2). So, ProtA will not be filtered and will appear in the final result.