| Nr |
3 (Review) |
| Authors |
Reker, Daniel; Malmström, Lars |
| Title |
Bioinformatic challenges in targeted proteomics. |
| Journal |
J Proteome Res (2012) 11 4393-402 |
| DOI |
10.1021/pr300276f |
| Citations |
23 citations (journal impact: 5.06) |
| Abstract |
Selected reaction monitoring mass spectrometry is an emerging targeted proteomics tech- nology that allows for the investigation of complex protein samples with high sensitivity and efficiency. It requires extensive knowledge about the sample for the many parameters needed to carry out the experiment to be set appropriately. Most studies today rely on parameter es- timation from prior studies public databases or from measuring synthetic peptides. This is efficient and sound but in absence of prior data de novo parameter estimation is necessary. Computational methods can be used to create an automated framework to address this prob- lem. However the number of available applications is still small. This review aims at giving an orientation on the various bioinformatical challenges. To this end we state the problems in classical machine learning and data mining terms give examples of implemented solutions and provide some room for alternatives. This will hopefully lead to an increased momentum for the development of algorithms and serve the needs of the community for computational methods. We note that the combination of such methods in an assisted workflow will ease both the usage of targeted proteomics in experimental studies as well as the further development of computational approaches. |
| Synopsis |
This review presents some computational challenges in Selected Reaction Monitoring. |