Hi Joe, I only see the Win binaries. Is the source available yet?
Brian On Wed, Feb 19, 2014 at 4:19 PM, Joseph Slagel < [email protected]> wrote: > You'll find the latest release candidate (RC4) in: > > > https://sourceforge.net/projects/sashimi/files/Trans-Proteomic%20Pipeline%20%28TPP%29/TPP%20v0.0%20%28Development%29/ > > Since I don't expect any more changes this should be effectively 4.7. > > -Joe > > > > On Tue, Feb 18, 2014 at 10:28 PM, Robert Jones > <[email protected]>wrote: > >> Bjorn, >> >> Thank you so very much that was extremely helpful! I'll be well on my way >> to finishing my analysis. >> >> -Robert M Jones >> >> >> On Tuesday, February 18, 2014 6:45:24 AM UTC-7, Bjorn wrote: >>> >>> Hi, >>> As far as I understand it, the TPP uses a mixture model-based approach >>> to determine posterior probabilities and it then uses these probabilities >>> to estimate the FDR. This can be done with or without the use of a decoy >>> database. However, when using some of the more sophisticated options (like >>> non-parametric modelling), you will need a decoy database to help the >>> modelling algorithms pin down the negative distribution. Also, when the >>> data is of not so excellent quality, the decoys can help make a better >>> distinction between good and bad identifications. >>> >>> After the analysis with ProteinProphet, you get something like this in >>> the ProteinProphet window: >>> >>> Prob Sens FPER Corr Incorr >>> 0.00 1.000 0.930 217 2874 >>> 0.10 1.000 0.337 217 110 >>> 0.20 1.000 0.337 217 110 >>> 0.30 0.915 0.216 198 55 >>> 0.40 0.865 0.152 187 34 >>> 0.50 0.816 0.102 177 20 >>> 0.60 0.777 0.074 168 14 >>> 0.70 0.747 0.058 162 10 >>> 0.80 0.647 0.019 140 3 >>> 0.90 0.608 0.010 132 1 >>> 0.95 0.569 0.005 123 1 >>> 0.96 0.547 0.004 119 0 >>> 0.97 0.529 0.003 115 0 >>> 0.98 0.507 0.002 110 0 >>> 0.99 0.470 0.001 102 0 >>> 1.00 0.152 0.000 33 0 >>> >>> The FPER is your FDR, so if you decide to set it at 1%, you notice that >>> this corresponds with a probability cut off of 0.90. Now, in your protein >>> list, you accept all proteins with a prob. of 0.09 or higher, which is >>> estimated to be 132 correct ones and 1 incorrect protein. Everything below >>> is discarded. >>> >>> I found lots of info in the following papers (esp. the last paper). >>> Hope this helps! >>> Cheers, >>> Bjorn >>> >>> [1] Choi, H., Fermin, D., and Nesvizhskii, A. I. Significance analysis >>> of spectral count data in label-free shotgun proteomics. Mol. Cell. >>> Proteomics 7, 12 (2008), 2373-2385. >>> >>> [2] Choi, H., Ghosh, D., and Nesvizhskii, A. I. Statistical validation >>> of peptide identifications in large-scale proteomics using the target-decoy >>> database search strategy and flexible mixture modeling. J. Proteome Res. 7, >>> 1 (2008), 286-292. >>> >>> [3] Choi, H., and Nesvizhskii, A. I. False discovery rates and related >>> statistical concepts in mass spectrometry-based proteomics. J. Proteome >>> Res. 7, 1 (2008), 47-50. >>> >>> [4] Choi, H., and Nesvizhskii, A. I. Semisupervised model-based >>> validation of peptide identifications in mass spectrometry-based >>> proteomics. J. Proteome Res. 7, 1 (2008), 254-265. >>> >>> [5] Deutsch, E. W., Mendoza, L., Shteynberg, D., Farrah, T., Lam, H., >>> Tasman, N., Sun, Z., Nilsson, E., Pratt, B., Prazen, B., Eng, J. K., >>> Martin, D. B., Nesvizhskii, A. I., and Aebersold, R. A guided tour of the >>> Trans-Proteomic Pipeline. Proteomics 10, 6 (2010), 1150-1159. >>> >>> [6] Keller, A., Nesvizhskii, A. I., Kolker, E., and Aebersold, R. >>> Empirical statistical model to estimate the accuracy of peptide >>> identifications made by MS/MS and database search. Anal. Chem. 74, 20 >>> (2002), 5383-5392. >>> >>> [7] Nesvizhskii, A. I. A survey of computational methods and error rate >>> estimation procedures for peptide and protein identification in shotgun >>> proteomics. J. Proteomics 73, 11 (2010), 2092-2123. >>> >> -- >> You received this message because you are subscribed to the Google Groups >> "spctools-discuss" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to [email protected]. >> To post to this group, send email to [email protected]. >> Visit this group at http://groups.google.com/group/spctools-discuss. >> For more options, visit https://groups.google.com/groups/opt_out. >> > > > > -- > Joe Slagel > Institute for Systems Biology > [email protected] > (206) 732-1362 > > -- > You received this message because you are subscribed to the Google Groups > "spctools-discuss" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > To post to this group, send email to [email protected]. > Visit this group at http://groups.google.com/group/spctools-discuss. > For more options, visit https://groups.google.com/groups/opt_out. > -- You received this message because you are subscribed to the Google Groups "spctools-discuss" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at http://groups.google.com/group/spctools-discuss. For more options, visit https://groups.google.com/groups/opt_out.
