De novo robustness to noise and missing cleavages
benchmark
De novo robustness to noise and missing cleavages: benchmark. Evaluation of how de novo sequencing algorithms degrade as spectra lose fragmentation cleavages and gain noise, run over eight published datasets with Novor and DeepNovo.
Evaluation of how de novo sequencing algorithms degrade as spectra lose fragmentation cleavages and gain noise, run over eight published datasets with Novor and DeepNovo.
| Kind | benchmark |
| Acquisition | DDA |
Data
What the papers describing this method trained it on and reported its results on, as each paper states it. A version is named only where the paper names one.
Also used, purpose not recorded
- Aggregating sequences that occur in many proteins constitute weak spots of bacterial proteostasis (as deposited)
- Identification and quantification of small proteins and peptides in Staphylococcus aureus (as deposited)
- NlpI scaffolds hydrolases within peptidoglycan multi-enzyme complexes in Escherichia coli (as deposited)
- Optimized data analysis avoiding trypsin artefacts (as deposited)
- PIA - Mouse Benchmark Dataset - PIA - An intuitive protein inference engine with a web-based user interface (as deposited)
- PeptideShaker - A complex standard for protein identification, designed by evolution (as deposited)
- PeptideShaker - PeptideShaker is a user friendly tool for shotgun proteomic data interpretation and reprocessing. (as deposited)
- Staphylococcus aureus responds to physiologically relevant temperature changes by altering its global transcript and pro (as deposited)
Paper describing it
- The impact of noise and missing fragmentation cleavages on de novo peptide identification algorithms (2022, Computational and Structural Biotechnology Journal, peer-reviewed)