Biology Reference
In-Depth Information
8. From all available methods, we recommend the use of the Row
Sigma normalization to deal with samples presenting high
spectral counting dynamic range. For a detailed description on
the method, please refer to Carvalho and coworkers [
29
].
9. This can be easily achieved by pasting the saved flexible report
in a blank excel file and breaking the first column (use
“comma” signal for that) to isolate the first described protein
from the others contained in the same protein group. By
default, the first described protein in a protein group is the
longest one and, thus, has the highest probability of retrieving
functional annotations during the sequence alignment step.
10. Selecting the level of depth is an important parameter for the
creation of the pie charts. The deeper the analysis, the more
detailed information is achieved but less coverage is obtained.
11. As a complement, Fischer's Exact Test can be applied to the
analysis if a background/reference dataset is provided.
12. We recommend replacing the zero values by the mean value if
a protein attends to the same parameters used in PatternLab
(i.e., protein found in
n
replicates −1). Otherwise, protein
should not be used in the analysis.
Acknowledgements
The authors would like to thank Dr. Paulo C. Carvalho for his
critical suggestions to this chapter.
References
1. Elliott MH, Smith DS, Parker CE et al (2009)
Current trends in quantitative proteomics.
J Mass Spectrom 44:1637-1660
2. Neilson KA, Ali NA, Muralidharan S et al
(2011) Less label, more free: approaches in
label-free quantitative mass spectrometry.
Proteomics 11:535-553
3. Nanjo Y, Nouri MZ, Komatsu S (2011)
Quantitative proteomic analyses of crop seed-
lings subjected to stress conditions; a commen-
tary. Phytochemistry 72:1263-1272
4. Evans C, Noirel J, Ow S et al (2012) An insight
into iTRAQ: where do we stand now? Anal
Bioanal Chem 404:1011-1027
5. Ross PL, Huang YN, Marchese JN et al (2004)
Multiplexed protein quantitation in
Saccharomyces
cerevisiae
using amine-reactive isobaric tagging
reagents. Mol Cell Proteomics 3:1154-1169
6. Gygi SP, Rist B, Gerber SA et al (1999)
Quantitative analysis of complex protein mixtures
using isotope-coded affinity tags. Nat
Biotechnol 17:994-999
7. Ong SE, Blagoev B, Kratchmarova I et al
(2002) Stable isotope labeling by amino acids
in cell culture, SILAC, as a simple and accurate
approach to expression proteomics. Mol Cell
Proteomics 1:376-386
8. Wiktorowicz JE, English RD, Wu Z et al
(2012) Model studies on iTRAQ modification
of peptides: sequence-dependent reaction spec-
ificity. J Proteome Res 11:1512-1520
9. Zhang Y, Wen Z, Washburn MP et al (2009)
Effect of dynamic exclusion duration on spec-
tral count based quantitative proteomics. Anal
Chem 81:6317-6326
10. Thelen JJ, Peck SC (2007) Quantitative pro-
teomics in plants: choices in abundance. Plant
Cell 19:3339-3346
11. Zhang B, VerBerkmoes NC, Langston MA et al
(2006) Detecting differential and correlated
