33. If possible, locate these folders on a faster hard drive as an SSD
to save time of analysis. The combined folder will receive all the
output files of the analysis.
34. By default the parameters file is saved beside the raw files from
LC-MS.
35. The proteinGroup.txt file is located in the output folder “combined/txt/proteinGroups.txt”.
36. If one of your interactors belongs to the contaminant list of
MaxQuant, you will lose this protein. To check the list of
contaminant goes to the MaxQuant Folder and open the following fasta file “MaxQuant/bin/conf/contaminants.fasta”.
Alternatively, you can keep the annotated contaminants proteins from your dataset.
37. Set the same name to samples belonging to the same group.
38. Alternatively you can use a different percentage to decrease
imputed values in the next steps.
39. It is a good practice to avoid to destroy the Gaussian distribution to have a better statistical analysis. You can increase the
filtering step 16 to decrease imputed values. However, for
pulldown analysis, it is not always possible because imputed
values can be important in control LC-MS.
40. Alternatively, you can use the “Hawaii plot” to display two class
of permutation FDR calculation.
41. Alternatively the number of random can be increased (e.g.,
2500 to better estimate the permutation FDR threshold).
42. Some interactors with low LFQ intensities can be found not
significant because of the imputation process. It is advisable to
inspect LFQ intensities using an Excel spreadsheet and eventually to recover some proteins with no detected values in Control conditions and valid values in pulldown conditions.
Acknowledgments
J-P.B.’s laboratory is funded by La Ligue Nationale Contre le
Cancer (Label Ligue J.P.B. 2019), Institut National du Cancer,
Fondation ARC pour la Recherche sur le Cancer, and Ruban
Rose. Through the European PDZnet consortium, JPB’s lab has
received funding from the EU Horizon 2020 RIA under the Marie
Skłodowska-Curie grant agreement No. 675341. The Marseille
Proteomic platform is funded by Institut Paoli-Calmettes, IBiSA
(Infrastructures en Biologie, Sante ´ et Agronomie) and FEDER
(Fonds Europe ´en de De ´veloppement Re ´gional). J-P.B. is a scholar
of Institut Universitaire de France.
Identification of Associated PDZ Proteins
39
to save time of analysis. The combined folder will receive all the
output files of the analysis.
34. By default the parameters file is saved beside the raw files from
LC-MS.
35. The proteinGroup.txt file is located in the output folder “combined/txt/proteinGroups.txt”.
36. If one of your interactors belongs to the contaminant list of
MaxQuant, you will lose this protein. To check the list of
contaminant goes to the MaxQuant Folder and open the following fasta file “MaxQuant/bin/conf/contaminants.fasta”.
Alternatively, you can keep the annotated contaminants proteins from your dataset.
37. Set the same name to samples belonging to the same group.
38. Alternatively you can use a different percentage to decrease
imputed values in the next steps.
39. It is a good practice to avoid to destroy the Gaussian distribution to have a better statistical analysis. You can increase the
filtering step 16 to decrease imputed values. However, for
pulldown analysis, it is not always possible because imputed
values can be important in control LC-MS.
40. Alternatively, you can use the “Hawaii plot” to display two class
of permutation FDR calculation.
41. Alternatively the number of random can be increased (e.g.,
2500 to better estimate the permutation FDR threshold).
42. Some interactors with low LFQ intensities can be found not
significant because of the imputation process. It is advisable to
inspect LFQ intensities using an Excel spreadsheet and eventually to recover some proteins with no detected values in Control conditions and valid values in pulldown conditions.
Acknowledgments
J-P.B.’s laboratory is funded by La Ligue Nationale Contre le
Cancer (Label Ligue J.P.B. 2019), Institut National du Cancer,
Fondation ARC pour la Recherche sur le Cancer, and Ruban
Rose. Through the European PDZnet consortium, JPB’s lab has
received funding from the EU Horizon 2020 RIA under the Marie
Skłodowska-Curie grant agreement No. 675341. The Marseille
Proteomic platform is funded by Institut Paoli-Calmettes, IBiSA
(Infrastructures en Biologie, Sante ´ et Agronomie) and FEDER
(Fonds Europe ´en de De ´veloppement Re ´gional). J-P.B. is a scholar
of Institut Universitaire de France.
Identification of Associated PDZ Proteins
39