GOAT - A simple LC-MS/MS gradient optimization tool

David C. Trudgian, Roman Fischer, Xiaofeng Guo, Benedikt M. Kessler, Hamid Mirzaei

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Modern nano-HPLC systems are capable of extremely precise control of solvent gradients, allowing high-resolution separation of peptides. Most proteomics laboratories use a simple linear analytical gradient for nano-LC-MS/MS experiments, though recent evidence indicates that optimized non-linear gradients result in increased peptide and protein identifications from cell lysates. In concurrent work, we examined non-linear gradients for the analysis of samples fractionated at the peptide level, where the distribution of peptide retention times often varies by fraction. We hypothesized that greater coverage of these samples could be achieved using per-fraction optimized gradients. We demonstrate that the optimized gradients improve the distribution of peptides throughout the analysis. Using previous generation MS instrumentation, a considerable gain in peptide and protein identifications can be realized. With current MS platforms that have faster electronics and achieve shorter duty cycle, the improvement in identifications is smaller. Our gradient optimization method has been implemented in a simple graphical tool (GOAT) that is MS-vendor independent, does not require peptide ID input, and is freely available for non-commercial use at http://proteomics.swmed.edu/goat/

Original languageEnglish (US)
Pages (from-to)1467-1471
Number of pages5
Issue number12
StatePublished - Jun 2014


  • Bioinformatics
  • Gradient
  • LC-MS/MS
  • Liquid chromatography
  • Optimization
  • Separation

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology


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