Biology Reference
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energy of 35, iso width of 1.0, activation Q of 0.250 and
activation time of 30 ms. Ions with unassigned charge or +1
were excluded for fragmentation. The minimum signal threshold
was set to 750.
3. Protein identifi cation: Raw-fi les are processed in Proteome
Discoverer software (Thermo, Germany) using in house protein
databases containing the latest available protein sequences and
six-frame translations using the SEQUEST algorithm (as is
available in Proteome Discoverer 1.3, Thermo, USA). The
following settings were used: precursor mass tolerance was set
to 10 ppm and fragment ion mass tolerance to 0.8 Da. Only
charge states +2 or greater were used. Identifi cation confi dence
was set to a 5 % FDR and the variable modifi cations were set to:
acetylation of N terminus, oxidation of methionine, and carb-
amidomethyl cysteine formation. No fi xed modifi cations were
set. A maximum of two missed cleavages were set for all searches
[ 6 ]. For fully sequenced organisms, like Chlamydomonas or
Arabidopsis we establish as a threshold for protein identifi ca-
tion one unique peptide (peptide that only appears once in the
entire database) with a X -Correlation value 0.5 greater than
the charge state (i.e., 2.5 for peptides with charge +2). For
non-sequenced organisms the use of two peptides are advised.
4. Quantifi cation: identifi ed proteins are quantifi ed by a standard
peptide count measurement using a NSAF approach [ 7 ]. This
measurement is limited to peptides that have been assigned to
proteins, and not those which are not present in the database
or those with posttranslational modifi cations (PTM´s) not
defi ned in the SEQUEST search step. A complete unbiased
approach for quantifi cation independent from database search
is the MAPA (mass accuracy precursor alignment) approach
[ 8 , 9 ]. Here the identifi cation relies on feature selection and
abundance. Using this method posttranslational modifi cations
(PTM´s) and non-sequenced organisms can be analyzed as
well [ 8 , 10 ]. Independently of the employed approach, a
power multivariate statistical analysis is paramount [ 11 ] ( see
Chapter 5 ). We routinely performed these analyses using R
statistical environment, but recently we have developed
COVAIN, a tool that allows a deep statistical analysis and data
pre-processing in a user-friendly environment [ 12 ].
4
Notes
1. The indicated protocol is intended for microalgae and bacteria,
for strains or plant organs with thicker cell wall the homog-
enization step probably should be stronger, or alternative
homogenization methods like French press or mortar and
pestle in liquid nitrogen used.
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