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Monday, July 24, between 18:00 CEST and 19:00 CEST
Tuesday, July 25, between 18:00 CEST and 19:00 CEST
Session A Poster Set-up and Dismantle
Session A Posters set up:
Monday, July 24, between 08:00 CEST and 08:45 CEST
Session A Posters dismantle:
Monday, July 24, at 19:00 CEST 		Session B Poster Set-up and Dismantle
Session B Posters set up:
Tuesday, July 25, between 08:00 CEST and 08:45 CEST
Session B Posters dismantle:
Tuesday, July 25, at 19:00 CEST
Wednesday, July 26, between 18:00 CEST and 19:00 CEST
Session C Poster Set-up and Dismantle
Session C Posters set up:
Wednesday, July 26,between 08:00 CEST and 08:45 CEST
Session C Posters dismantle:
Wednesday, July 26, at 19:00 CEST
Virtual
Bioinformatic Workflow to Increase Confidence in Single Amino Acid Variant Peptide Identifications
Track: CompMS
  • Beata Szeitz, Semmelweis University, Hungary
  • Nicole Woldmar, Lund University, Sweden
  • Zoltan G. Pahi, University of Szeged, Hungary
  • Zsolt Horvath, Lund University, Sweden
  • Fabio C. S. Nogueira, Federal University of Rio de Janeiro, Brazil
  • Lazaro H. Betancourt, Lund University, Sweden
  • Tibor Pankotai, University of Szeged, Hungary
  • David Fenyo, New York University, United States
  • Gyorgy Marko-Varga, Lund University, Sweden
  • A. Marcell Szasz, Semmelweis University, Hungary
  • Melinda Rezeli, Lund University, Sweden
  • Peter L. Horvatovich, University of Groningen, Netherlands


Presentation Overview: Show

In MS-based shotgun proteomics, confident identification of single amino acid variants (SAAVs) is challenging. To address this, we developed a workflow that efficiently reduces the number of false positive SAAV identifications. Our approach involves database search against a protein sequence database containing both reference sequences and SAAVs, followed by assessing SAAV identification confidence with PepQuery, SpectrumAI and MS2PIP tools. We tested the workflow using a proteomic dataset of 26 small cell lung cancer (SCLC) cell lines and searched for SAAVs previously described in SCLC cell lines’ genomes. Only 574 of 2828 SAAV PSMs obtained from database search were supported by genomics (true-positives, TPs). We employed Lasso-regularized logistic regression to identify tool outputs and spectral features that help distinguish true and false positive identifications. The model suggests that spectra passing PepQuery and SpectrumAI validation, correlating strongly with MS2PIP-predicted spectra and displaying less noise are all indicative of TP identifications. The model operated with sensitivity of 0.5690 and specificity of 0.9777 on the test data. We envision that our workflow will enable the reliable identification of cancer-related SAAVs in LC-MS/MS proteomic datasets even without prior genomic information. Grant support: UNKP-22-3-II, Semmelweis 250+ Excellence PhD Scholarship.