PABST
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| The criteria used by PABST for selecting the list of most suitable peptides from a target protein are primarily based on the | The criteria used by PABST for selecting the list of most suitable peptides from a target protein are primarily based on the | ||
| - | likelihood of observation and the presence or absence of various sequence features. To address the former, we first query | + | likelihood of observation and the presence or absence of various sequence features. To address the former, we first query |
| - | the Peptide Atlas and find all peptides mapping to a subject protein that have been observed more than once. We also | + | the Peptide Atlas and find all peptides mapping to a subject protein that have been observed more than once. We also |
| - | generate a list of all possible trypic peptides from this same peptide, and apply two separate peptide detectability | + | generate a list of all possible trypic peptides from this same peptide, and apply two separate peptide detectability |
| - | algorithms, Peptide Sieve and Peptide Detectability Predictor. The information is then merged, with the empirical (EPS) | + | algorithms, Peptide Sieve and Peptide Detectability Predictor. The information is then merged, with the empirical (EPS) |
| - | proteotypic score being used if available, else the theoretical proteotypic score is used. | + | proteotypic score being used if available, else the theoretical proteotypic score is used. |
| - | The sequence is then evaluated based on length, amino acid composition, and redundancy (peptide maps to more than one | + | The sequence is then evaluated based on length, amino acid composition, and redundancy (peptide maps to more than one |
| - | protein and/or peptide maps to more than one region on genome), and a final score is calculated. This is done by | + | protein and/or peptide maps to more than one region on genome), and a final score is calculated. This is done by |
| applying weighting factors which can be modified by editing the configuration file fed to the command line peptide | applying weighting factors which can be modified by editing the configuration file fed to the command line peptide | ||
| selector. This is described in some detail on [http://tools.proteomecenter.org/wiki/index.php?title=PABST_peptide_examples [this page]]. | selector. This is described in some detail on [http://tools.proteomecenter.org/wiki/index.php?title=PABST_peptide_examples [this page]]. | ||
| + | |||
| + | The proteotypic score obtained from step one is then adjusted by the various weighting factors in step two, and a final | ||
| + | score from each peptide is determined. The list of peptides is sorted by this score in descending order, and the | ||
| + | desired number of peptides are selected. | ||
Revision as of 19:20, 28 September 2009
PABST overview
This page describes the Peptide Atlas Best SRM Transition tool or PABST, which is a Peptide Atlas functionality that uses various sources of information to produce lists of peptides and (optionally) fragment ions for use in selected reaction monitoring (SRM) assays. This information is compiled into 'builds' that allow for fast querying via a web interface, which can be found at following URL: https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/GetPABSTList
The PABST build process has 3 discrete steps which are described in more detail in the following paragraphs. First of all, a list of the the top N (currently 10) most suitable peptides is determined for each protein in the target organism. Secondly, a list of the top M (currently 8) potential fragment ions from each selected peptide is determined. Finally this information in loaded into the Peptide Atlas database and forms are provided to query and retrieve list of potential assays.
Peptide list selection
The criteria used by PABST for selecting the list of most suitable peptides from a target protein are primarily based on the likelihood of observation and the presence or absence of various sequence features. To address the former, we first query the Peptide Atlas and find all peptides mapping to a subject protein that have been observed more than once. We also generate a list of all possible trypic peptides from this same peptide, and apply two separate peptide detectability algorithms, Peptide Sieve and Peptide Detectability Predictor. The information is then merged, with the empirical (EPS) proteotypic score being used if available, else the theoretical proteotypic score is used.
The sequence is then evaluated based on length, amino acid composition, and redundancy (peptide maps to more than one protein and/or peptide maps to more than one region on genome), and a final score is calculated. This is done by applying weighting factors which can be modified by editing the configuration file fed to the command line peptide selector. This is described in some detail on [this page]. The proteotypic score obtained from step one is then adjusted by the various weighting factors in step two, and a final score from each peptide is determined. The list of peptides is sorted by this score in descending order, and the desired number of peptides are selected.
