Database Reference
In-Depth Information
1:
chroList
←
list.files(pattern=”
∗
.chro”);
2:
cat (”Chro”, ”samSN”, ”refSN”, ”PPCSN”, ”HR”, ”PCA”, ”PCASN”,
file=”Pratio-Peptide.txt”);
3:
PE (
for
(
i
in 1:length(chroList))
4:
{
5:
currResult [
i
] = Pratio(filename=chroList[
i
]);
6:
}
)
7:
for
(
i
in 1:length(chroList))
{
8:
9:
cat (chroList[
i
], currResult$samSN, file=”Pratio-Peptide.txt”);
}
Algorithm 8.1:
Code fragment from ProRata with enabled task-parallelism
feature.
10:
pR
are not mandatory and are provided for better user-driven control in the
choice of parallel and serial routines. Since the overhead introduced by
pR
for
these kinds of function calls is less than 10% and reduces with the size of the
matrix, the scalability in terms of the number of processors is largely deter-
mined by the scalability of the underlying ScaLAPACK's routines (scalability
benchmarks are not shown here, but described elsewhere).
92
,
93
Dealing with the second challenge—the noise in the data—to improve both
quantification accuracy and quantification confidence requires optimization
of the core analysis steps described below: chromatographic peaks detection,
peptide relative abundance estimation, and protein relative abundance esti-
mation (Figure 8.12).
MS/MS Scans
SEQUEST+DTASelect
Full Scans (mzXML)
Peptide Identifications
Selected Ion Chromatograms
Peak Detection
ProRata
Peptide Abundance Ratios Estimation
Protein Abundance Ratios Estimation
Figure 8.12
Key data analysis steps in ProRata.
