Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Laika sēriju proteīmu analīze× | Proteīnu analīze× | |
|---|---|---|
| Nozare | Bioinformātika | Bioinformātika |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2000s (quantitative framework: Gygi et al. 1999; time-series designs: 2004–2010) | 1994–2003 (term coined 1994; shotgun proteomics established early 2000s) |
| Autors≠ | Multiple groups; Gygi et al. (1999) established quantitative proteomics; time-series designs emerged in the 2000s with LC-MS/MS workflows | Marc Wilkins, Matthias Mann, Ruedi Aebersold (proteome/mass spectrometry foundations) |
| Tips≠ | Quantitative longitudinal omics pipeline | Quantitative omics pipeline |
| Pirmavots≠ | Lemeer, S., & Heck, A. J. R. (2012). The phosphoproteomics data explosion. Current Opinion in Chemical Biology, 16(1–2), 1–8. link ↗ | Wilkins, M. R., Sanchez, J.-C., Gooley, A. A., Appel, R. D., Humphery-Smith, I., Hochstrasser, D. F., & Williams, K. L. (1996). Progress with proteome projects: Why all proteins expressed by a genome should be identified and how to do it. Biotechnology and Genetic Engineering Reviews, 13(1), 19–50. link ↗ |
| Citi nosaukumi | longitudinal proteomics, temporal proteomics, dynamic proteomics, time-course proteomics | proteomics, mass spectrometry-based proteomics, shotgun proteomics, quantitative proteomics |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | Time-series proteomics analysis quantifies protein abundance across two or more ordered time points to reveal how the proteome changes dynamically in response to stimuli, developmental stages, or disease progression. By combining mass spectrometry-based protein quantification with statistical models designed for temporal data, the method identifies proteins with significant expression trends, oscillatory patterns, or delayed responses that cannot be detected in single time-point studies. | Proteomics analysis is a systematic pipeline for identifying and quantifying proteins in biological samples using mass spectrometry. Starting from raw spectral data, the workflow searches protein sequence databases, estimates abundance across conditions, applies statistical tests for differential expression, and maps findings onto biological pathways. It complements transcriptomics by capturing post-translational regulation and actual protein abundance, and is central to biomarker discovery, drug-target identification, and systems biology. |
| ScholarGateDatu kopa ↗ |
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