I normally put that up when we do the release. The RC4 email wasn't meant for the mailing list -- I meant to send it to someone that was going to do one final release before putting it out. If you want I can put a source tarball up on the development folder.
-Joe On Wed, Feb 19, 2014 at 3:30 PM, Brian Hampton <[email protected]> wrote: > 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. > -- 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. 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