๐Ÿงฉ Fig 1: CHIMERYS solves the puzzle of chimeric spectra as mixtures of peptides. Using AI-based predictions and regularized linear regression, it identifies multiple peptides per MS2 spectrum. With rigorous #FDR control, it outperforms 8 search engines in #DDA ID rates. 2/7
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๐Ÿ“ˆ Fig 2: Challenging data? CHIMERYS shines! Extract more IDs from any DDA data โ€” good for revisiting legacy data, even low-res. โ†’ 4x more throughput w/o loss in IDs โ†’ 2x more IDs at the same throughput โ†’ Cloud-based for speed & scalability Unveil secrets older data hold! 3/7
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๐Ÿš€ Fig 3: In DIA, complexity skyrockets. No problem for CHIMERYS! โ†’ CHIMERYS delivers high ID rates with better FDR control than other solutions โ†’ More informative fragment utilization yields more reliable #quantification It handles #DIA data in a spectrum-centric fashion. 4/7
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๐Ÿ“ Fig 4: CHIMERYS quantifies peptides directly from chimeric #MS2 spectra. How? Deconvolution! โ†’ #PRM-level precision with r = 0.99 vs Skyline โ†’ CV performance on par with established DIA algorithms No libraries. No manual curation. Straight from data to insights. 5/7
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๐Ÿ’ก Fig 5: CHIMERYS unlocks #directInfusion proteomics (DISPA) โ€” library-free! โ†’ Up to 3ร— more IDs result in richer functional insights โ†’ Same #deepLearning model and data processing as for DDA & DIA Any mass spec-data, no special tuning, just deeper results. 6/7
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๐Ÿ”„ Fig 6: CHIMERYS enables fair DDA vs DIA comparisons. โ†’ Same algorithm, consistent FDR โ†’ IDs vs data completeness: tune your workflow to your needs Unify your data analysis workflow with #CHIMERYS: choose the mode, not the model! 7/7
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Replying to @msaid_de @naturemethods
Following the preprint of this work, I let Martin Frejno know that DIA-NN output was processed incorrectly, resulting in nonsense data and plots. Mistakes happen, but puzzling to see these weird plots still in the final version (see below). Make your conclusions :)
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