Biology Reference
In-Depth Information
class. We prefer to relax this parameter and usually select four
as the minimum number of replicates per class (i.e., the protein
was identified in four out of the five biological replicates).
5. Select a q -value and press the “Optimize F” button. This will
make the TFold adjust a variable fold-change cutoff to maxi-
mize the identifications for a given q -value. The user can also
highlight (and separate) proteins that pass the q -value and fold-
change cutoff by increasing the parameter found in the
L-stringency numeric box. We recommend using a value of 0.4.
6. After the parsing step is done, press “Calculate and Plot” to
generate a volcano plot on the right side of PatternLab's dis-
play. Proteins that met the statistical and fold-change cutoffs
as well as the L-stringency parameter are displayed as blue
dots. The ones that satisfy both criteria but do not meet the
L-stringency parameter will be highlighted (and separately
reported) in orange. These proteins deserve further experi-
mentation to assess their differential expression status. The
green dots are those that satisfy the fold change but not the
q -value statistical filter; and finally, the red dots are proteins
that do not pass any criteria.
7. Export results by choosing “Output” panel and selecting “Save
Plot” for all proteins (blue, red, orange, and green dots).
In order to gather more information about the proteins suggested
as differentially expressed, the protein primary sequence of these
identifications is extracted using the Musite application. This soft-
ware contains several useful features and was originally developed
for prediction of phosphorylation sites. For detailed information,
please refer to Gao and coworkers [ 30 ].
3.5 Retrieval of
Protein Sequences
1. Prepare a one-column file containing the accessions suggested
by the T-Fold test as differentially expressed ( see Note 9 ).
2. In Musite control panel, convert the FASTA database used by
SEQUEST search engine to Musite XML format. For that,
select “Tools” and then “File Processing”. For database con-
version, select “File Conversion” and “Convert FASTA to
Musite XML”. Finally, indicate the file to be converted and
the regular expression to parse the FASTA database header.
3. After Musite XML file is created, select “Tools” and then “File
Processing”. For filtering the proteins of interest, select “File
Filtering” and “Filter Proteins by Accessions”.
4. Indicate the converted Musite XML database and paste the list
of proteins of interest in the “Accessions” section.
5. After filtering is done, the retrieved sequences should be con-
verted back to FASTA file by following step 2 , but converting
Musite XML file to FASTA.
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