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<?xml version="1.0"?> |
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<html xmlns="http://www.w3.org/1999/xhtml" |
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xmlns:v="http://localhost" xml:lang="en" lang="en"> |
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<head> |
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<link rel="stylesheet" type="text/css" href="../css/exercise.css" /> |
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<link rel="stylesheet" type="text/css" |
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href="../css/default.css" /> |
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<title>Proteios Software Environment Exercise</title> |
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</head> |
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<body> |
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<h1>Generating a quantitative report from LC-MS runs with |
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isobaric labels</h1> |
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<h3>Background</h3> |
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<p>The protein levels of yeast (Saccharomyces cerevisiae) grown |
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under two different conditions will be compared. For this analysis the |
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samples were reduced and alkylated using iodoacetamide, |
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digested with trypsin and labelled using the isobaric label |
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TMT. Labels 126,128 and 130 were used for one cell state, and |
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127,129 and 131 for the other. The labelled peptides were |
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loaded onto a nano LC system and analysed on-line using an |
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LTQ-Orbitrap. To get many peptide identifications, CID |
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fragmentation and analysis in the linear trap was used. |
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However, the reporter ions are not visible in most of the |
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spectra, since they are found at lower masses than what can be |
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analysed in the ion trap using CID fragmentation. To overcome |
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this, each MS/MS scan in the linear trap was followed by an |
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MS/MS scan in the Orbitrap of the same precursor ion using |
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high-energy collision- induced dissociation (HCD) fragmentation |
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.</p> |
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<h3>Proteios Software Environment (ProSE)</h3> |
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<p>The major reason for using ProSE in this project was to get |
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a large number of confident peptide identifications at a |
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controlled error rate and to automatically get the reporter |
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ion ratios included in the report. The reporter ion quantities |
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need to be retained from adjacent scans for CID-based |
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identifications. Here we have chosen not to enter sample |
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tracking information into ProSE, but rather to generate a |
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report as quickly as possible. The following steps are then |
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used:</p> |
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<ol> |
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<li>Login to the ProSE server at |
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<a target="proteios" |
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href="http://proteios.immunoprot.lth.se:8080/proteios/app"> |
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http://proteios.immunoprot.lth.se:8080/proteios/app</a> |
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<br /> |
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<b>Accounts:</b>user1,user2,user3 (passwords are |
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user1,user2,user3)</li> |
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<li>Create a new project |
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<ul> |
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<li> |
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<ul class="clickstream"> |
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<li>File</li> |
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<li>New</li> |
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<li>Project</li> |
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</ul> |
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</li> |
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<li>Select non-gel project as type</li> |
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<li class="click">Save</li> |
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</ul></li> |
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<li>The sample data, consisting of raw data from LC-MS/MS |
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runs have been converted to mzData and has to be uploaded to |
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ProSE. We've used Proteome Discoverer (Thermo Scientific) for |
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the conversion, and the two centroid mzData peak lists files |
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should be uploaded. Files can be uploaded one by one using |
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the web browser, but for batch upload the normal way would be |
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to use ftp. To upload through the browser |
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<ul> |
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<li> |
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<ul class="clickstream"> |
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<li>project_name</li> |
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<li>files</li> |
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</ul> |
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</li> |
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<li class="click">Upload file</li> |
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</ul> |
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<b>The files are located:</b>X:\ProSE_TMT_exercise |
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<br />For future ftp usage, the fireFTP plugin for Mozilla |
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Firefox can be used, or the standalone ftp-client Filezilla. |
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Other ftp-clients may work aswell. The ftp server address is |
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<b>proteios.immunoprot.lth.se:8021</b></li> |
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<li>Typically the MS/MS peptide identification searches are |
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performed directly from ProSE. |
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<ul> |
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<li>Configure search parameters |
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<ul> |
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<li> |
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<ul class="clickstream"> |
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<li>View</li> |
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<li>Search Setup</li> |
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<li>Mascot</li> |
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</ul> |
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</li> |
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<li class="click">New Mascot Parameter Set</li> |
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<li>Fill out form</li> |
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<li class="click">Save</li> |
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<li class="click">Your saved parameter set in the |
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list</li> |
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<li>Fill out form</li> |
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<li class="click">Save</li> |
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</ul></li> |
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<li>Start search |
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<ul> |
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<li> |
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<ul class="clickstream"> |
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<li>Your Project</li> |
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<li>Spectrum Search</li> |
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<li>Mascot</li> |
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</ul> |
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</li> |
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<li class="click">Select spectrum files</li> |
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<li class="click">Next - Select Mascot Search User |
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Data</li> |
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<li>Fill out form</li> |
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<li class="click">Next - Select Mascot parameter set</li> |
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<li>Select the previously created parameter set</li> |
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<li class="click">Next - Create search job[s]</li> |
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</ul></li> |
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</ul>Depending number of files and configuration the jobs may |
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take a while to execute. |
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<br />However, in this example, the searches are already |
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done, so you may upload the 6 files with search results.</li> |
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<li>Now the report generation can begin. First the peak list |
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files need to be registered. To do this |
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<ul> |
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<li> |
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<ul class="clickstream"> |
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<li>project_name</li> |
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<li>Hits Import</li> |
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<li>Non gel based</li> |
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</ul> |
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</li> |
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<li>Enter a local sample id for step 1, for example |
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'pool'</li> |
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<li> |
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<ul class="clickstream"> |
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<li>Next</li> |
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<li>Select peak list files</li> |
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</ul> |
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</li> |
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<li>Check the check boxes in front of the peak list files |
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in the next step |
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<div class="note">Do not select the search result files |
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here.</div></li> |
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<li class="click">import</li> |
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</ul></li> |
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<li>When the jobs have finished you can check the results by |
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looking in |
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<ul class="clickstream in"> |
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<li>project_name</li> |
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<li>Reports</li> |
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<li>Hits</li> |
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</ul>. It should now just contain the peak list files, but no |
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identifications.</li> |
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<li>The next step is to import the search results. This is |
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done in the second step in the Non Gel Based hits import. |
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<ul> |
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<li> |
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<ul class="clickstream"> |
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<li>project_name</li> |
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<li>Hits Import</li> |
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<li>Non gel based</li> |
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</ul> |
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</li> |
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<li class="click">Next - Select Search Result File[s]</li> |
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<li>Check the check boxes in front of the search results |
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files</li> |
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<li class="click">Import</li> |
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</ul>Note that the search results files were uploaded in step |
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4. The import step serves to get the results into the |
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database tables.</li> |
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<li>When the jobs have finished, |
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<ul class="clickstream in"> |
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<li>Reports</li> |
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<li>Hits</li> |
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</ul>will contain a lot of results, which can be navigated |
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and filtered. However, there is still no validation and |
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consensus usage of the search results from different search |
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engines. Select columns and filter the report to show only |
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enolase 2. |
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<div class="question">How many entries are there?</div>Filter |
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the report to show all peptides from enolase 2 (P00925). |
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<div class="question">How many Mascot results are there for |
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peptides that are unique to P00925 and which exist in P00925 |
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and other proteins too respectively?</div></li> |
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<li>Combination of the search results from different search |
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engines is performed by selecting |
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<ul> |
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<li> |
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<ul class="clickstream in"> |
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<li>Reports</li> |
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<li>Combined Hits</li> |
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</ul> |
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</li> |
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<li>Select the local sample Id that you used in step 5</li> |
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</ul>and make sure that combination is performed on the |
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peptide level. If combination is performed on the Protein |
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level, no peptide score cut off will be used, which is |
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dangerous in experiments with complex peptide mixtures. Decoy |
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(random) identifications have the prefix 'r' and not the |
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default 'IPR'. Set the parameters and start the job. A |
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peptide report will now be built up, where the false |
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discovery rate for peptides will be calculated. The report |
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will be written to file, and in addition, the Hits report is |
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updated with Combined FDR values that can be used for |
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filtering. Open up the generated tab-separated file. |
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<div class="question">How many peptide identifications were |
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retained?</div></li> |
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<li>Generation of the protein report, including reporter |
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ratios. This is done by selecting |
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<ul class="clickstream in"> |
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<li>Reports</li> |
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<li>Protein Assembly</li> |
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</ul>. Select the local sample ID and select TMT label. The |
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generated report will contain a list of protein groups that |
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include the identified peptides that pass the FDR cut-off. |
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The ratios of the reporter ions will be displayed for all |
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peptides, and an average will be calculated for the protein |
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group. |
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<div class="question">How many protein groups were |
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there?</div> |
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<div class="question">What is a protein group?</div></li> |
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</ol> |
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</body> |
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</html> |