Oh, no I'll wait till the final release.

thanks,

Brian

> On Feb 19, 2014, at 6:58 PM, Joseph Slagel <[email protected]> 
> wrote:
> 
> 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.
>>>> 
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>>> 
>>> 
>>> 
>>> -- 
>>> Joe Slagel
>>> Institute for Systems Biology
>>> [email protected]
>>> (206) 732-1362
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> 
> 
> 
> -- 
> Joe Slagel
> Institute for Systems Biology
> [email protected]
> (206) 732-1362
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