We are analyzing https://link.springer.com/article/10.1007/s13361-011-0139-3.

Title:
Target-Decoy Approach and False Discovery Rate: When Things May Go Wrong | Journal of The American Society for Mass Spectrometry
Description:
The target-decoy approach (TDA) has done the field of proteomics a great service by filling in the need to estimate the false discovery rates (FDR) of peptide identifications. While TDA is often viewed as a universal solution to the problem of FDR evaluation, we argue that the time has come to critically re-examine TDA and to acknowledge not only its merits but also its demerits. We demonstrate that some popular MS/MS search tools are not TDA-compliant and that it is easy to develop a non-TDA compliant tool that outperforms all TDA-compliant tools. Since the distinction between TDA-compliant and non-TDA compliant tools remains elusive, we are concerned about a possible proliferation of non-TDA-compliant tools in the future (developed with the best intentions). We are also concerned that estimation of the FDR by TDA awkwardly depends on a virtual coin toss and argue that it is important to take the coin toss factor out of our estimation of the FDR. Since computing FDR via TDA suffers from various restrictions, we argue that TDA is not needed when accurate p-values of individual Peptide-Spectrum Matches are available.
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Keywords {🔍}

sigma, tda, database, fdr, peptide, tools, search, scoring, peptides, decoy, oplus, article, scoreleft, mass, identifications, google, scholar, target, tool, fprs, databases, random, proteomics, fpr, cas, false, statistical, spectra, functions, tdacompliant, protein, left, function, msms, spectrum, sequest, ddleft, spectrometry, discovery, compute, computing, mascot, score, number, psms, approach, argue, xtandem, footnote, psm,

Topics {✒️}

{\mathop{{\widehat{{fdr}}}}\nolimits_{{tda}} } false-positive rate determination false positive rate health grant 1-p41-rr024851 semi-supervised learning tda-compliant remains open ms/ms search algorithms typical ms/ms searches target-decoy search strategy popular ms/ms tools high-throughput mass spectrometry ms/ms search tool molecular sequence features ms-based peptide sequencing mass spectrometry-based proteomics large-scale proteomics experiments database-dependent scoring functions multi-stage search tools false discovery rate newly found n-myristoylated database-dependent scoring function 6th n-acetylated protein fpr-noncompliant scoring functions $$ \widehat{{fdr}}_{{etda}} high-throughput proteomics experiment large-scale protein identifications combined target-decoy database compute accurate p-values perfect peptide-spectrum match privacy choices/manage cookies ms/ms algorithms ms/ms searches ms-gf outperforms sequest ms/ms tools tda-compliant scoring functions ms-gf scoring {\left[ {1_{{score{\left fpr-compliant scoring function mass spectrometry literature precision mass spectrometry false discovery rates cid+etd spectral datasets related statistical concepts related subjects words multi-stage option individual peptide-spectrum matches {\left[ {{\sum\limits_{\sigma target-decoy approach scoring functions employed

Questions {❓}

, lead to new biological discoveries), should they be switched off?
1 Can TDA be Exploited to Disguise Bogus Peptide Identifications as Good Ones?
2 Can Tools That Do Not Comply with TDA be Useful?
2 False Positive Rate and eTDA:Is TDA Needed if Accurate FPRs are Available?
3 Can the Decoy and Target Databases Contain Shared Peptides?
3 What are the Disadvantages of TDA?
4 Can Computing FDR via FPR Substitute Approximating FDR via TDA?
5 Are Individual FPRs Useful in the Context of High-Throughput Mass Spectrometry?
Does it mean that (i) Sequest is not TDA compliant, (ii) TDA only works correctly for a 50–50 split between the sizes of the target and decoy databases, or (iii) TDA does not provide a reliable estimate of FDR in the case of 50–50 split and should be practiced with decoy databases that are larger than the target database?
However, since such tests are difficult to do in case of commercial software (without access to the source code), multi-stage search tools, or complex tools like InsPecT, should TDA be avoided in conjunction with such tools?
Should Mascot (a useful tool whose source code is not available for a test of TDA compliance) be excluded from TDA studies?
Should TDA be run with decoy databases that are much larger than target databases to accurately measure FDRs of highly reliable peptide identifications?
Should we therefore declare eTDA as impractical?
What are the limits of TDA applicability when it comes to lowering the size of the database?
What are the limits of TDA applicability when it comes to lowering the size of the spectral dataset?
Would it be better to setup an FPR (rather than FDR) threshold for individual PSMs (that would clearly separate reliable and unreliable identifications) and avoid contaminating the output of the TDA approach with ≈ 30 bogus peptide identifications?
Coli with 1% FDR, would it be a good reason to write a paper about a new N-acetylated protein and discuss its biological function?

Schema {🗺️}

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         headline:Target-Decoy Approach and False Discovery Rate: When Things May Go Wrong
         description:The target-decoy approach (TDA) has done the field of proteomics a great service by filling in the need to estimate the false discovery rates (FDR) of peptide identifications. While TDA is often viewed as a universal solution to the problem of FDR evaluation, we argue that the time has come to critically re-examine TDA and to acknowledge not only its merits but also its demerits. We demonstrate that some popular MS/MS search tools are not TDA-compliant and that it is easy to develop a non-TDA compliant tool that outperforms all TDA-compliant tools. Since the distinction between TDA-compliant and non-TDA compliant tools remains elusive, we are concerned about a possible proliferation of non-TDA-compliant tools in the future (developed with the best intentions). We are also concerned that estimation of the FDR by TDA awkwardly depends on a virtual coin toss and argue that it is important to take the coin toss factor out of our estimation of the FDR. Since computing FDR via TDA suffers from various restrictions, we argue that TDA is not needed when accurate p-values of individual Peptide-Spectrum Matches are available.
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            Proteomics
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      headline:Target-Decoy Approach and False Discovery Rate: When Things May Go Wrong
      description:The target-decoy approach (TDA) has done the field of proteomics a great service by filling in the need to estimate the false discovery rates (FDR) of peptide identifications. While TDA is often viewed as a universal solution to the problem of FDR evaluation, we argue that the time has come to critically re-examine TDA and to acknowledge not only its merits but also its demerits. We demonstrate that some popular MS/MS search tools are not TDA-compliant and that it is easy to develop a non-TDA compliant tool that outperforms all TDA-compliant tools. Since the distinction between TDA-compliant and non-TDA compliant tools remains elusive, we are concerned about a possible proliferation of non-TDA-compliant tools in the future (developed with the best intentions). We are also concerned that estimation of the FDR by TDA awkwardly depends on a virtual coin toss and argue that it is important to take the coin toss factor out of our estimation of the FDR. Since computing FDR via TDA suffers from various restrictions, we argue that TDA is not needed when accurate p-values of individual Peptide-Spectrum Matches are available.
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      dateModified:2011-05-05T00:00:00Z
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         Computational proteomics
         Target-decoy approach
         False discovery rate
         False positive rate
         Database search
         Decoy database
         P-value
         Analytical Chemistry
         Biotechnology
         Organic Chemistry
         Proteomics
         Bioinformatics
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            address:
               name:Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, USA
               type:PostalAddress
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      affiliation:
            name:University of Sydney
            address:
               name:School of Mathematics and Statistics, University of Sydney, Sydney, Australia
               type:PostalAddress
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      affiliation:
            name:University of California San Diego
            address:
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      name:School of Mathematics and Statistics, University of Sydney, Sydney, Australia
      name:Bioinformatics Program, University of California San Diego, La Jolla, USA
      name:Department of Computer Science and Engineering, University of California San Diego, La Jolla, USA

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