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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:
Rootnode
level, hEIDI will then group proteins from all the identification results, orContext1
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 the very trivial example above, you filter, when grouping proteins in each leaf context, the proteins with a number of peptides lower than 2. This will remove ProtA and then, when grouping proteins in the Rootnode
context, ProtA will not appear in the final result.
But in case 1, when grouping/filtering proteins at the Rootnode
level, ProtA will gain one peptide more (ProtA will be identified by 3 peptides instead of 2) and so will appear in the final result.