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@ARTICLE{Holl:280903,
      author       = {Holl, Sonja and Mohammed, Yassene and Zimmermann, Olav and
                      Palmblad, Magnus},
      title        = {{S}cientific {W}orkflow {O}ptimization for {I}mproved
                      {P}eptide and {P}rotein {I}dentification},
      journal      = {BMC bioinformatics},
      volume       = {16},
      issn         = {1471-2105},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {FZJ-2016-00614},
      pages        = {284},
      year         = {2015},
      abstract     = {Background: Peptide-spectrum matching is a common step in
                      most data processing workflows for massspectrometry-based
                      proteomics. Many algorithms and software packages, both free
                      and commercial, have beendeveloped to address this task.
                      However, these algorithms typically require the user to
                      select instrument- andsample-dependent parameters, such as
                      mass measurement error tolerances and number of missed
                      enzymaticcleavages. In order to select the best algorithm
                      and parameter set for a particular dataset, in-depth
                      knowledgeabout the data as well as the algorithms themselves
                      is needed. Most researchers therefore tend to use
                      defaultparameters, which are not necessarily
                      optimal.Results: We have applied a new optimization
                      framework for the Taverna scientific workflow management
                      $system(http://ms-utils.org/Taverna_Optimization.pdf)$ to
                      find the best combination of parameters for a given
                      scientificworkflow to perform peptide-spectrum matching. The
                      optimizations themselves are non-trivial, as demonstrated
                      byseveral phenomena that can be observed when allowing for
                      larger mass measurement errors in sequence databasesearches.
                      On-the-fly parameter optimization embedded in scientific
                      workflow management systems enables expertsand non-experts
                      alike to extract the maximum amount of information from the
                      data. The same workflows could beused for exploring the
                      parameter space and compare algorithms, not only for
                      peptide-spectrum matching, but alsofor other tasks, such as
                      retention time prediction.Conclusion: Using the optimization
                      framework, we were able to learn about how the data was
                      acquired as well asthe explored algorithms. We observed a
                      phenomenon identifying many ammonia-loss b-ion spectra as
                      peptideswith N-terminal pyroglutamate and a large precursor
                      mass measurement error. These insights could only be
                      gainedwith the extension of the common range for the mass
                      measurement error tolerance parameters explored by
                      theoptimization framework.},
      cin          = {JSC},
      ddc          = {004},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / 512 - Data-Intensive Science and Federated
                      Computing (POF3-512)},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-HGF)POF3-512},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000360426000008},
      pubmed       = {pmid:26335531},
      doi          = {10.1186/s12859-015-0714-x},
      url          = {https://juser.fz-juelich.de/record/280903},
}