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| Bayesian Proteomics Analysis× | Bayesian RNA-seq Differential Expression× | |
|---|---|---|
| Valdkond | Bioinformaatika | Bioinformaatika |
| Perekond | Process / pipeline | Process / pipeline |
| Tekkeaasta≠ | 2000s (major developments 2003–2010) | 2010–2013 |
| Looja≠ | Multiple contributors; foundational statistical frameworks by Nesvizhskii, Kall, Choi, and colleagues | Kendziorski et al. (EBSeq); Hardcastle & Kelly (baySeq) |
| Tüüp≠ | Probabilistic inference pipeline | Bayesian statistical inference pipeline |
| Algallikas≠ | Kall, L., Canterbury, J. D., Weston, J., Noble, W. S., & MacCoss, M. J. (2008). Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nature Methods, 5(11), 923–925. link ↗ | Leng, N., Dawson, J. A., Thomson, J. A., Ruotti, V., Rissman, A. I., Smits, B. M., Haag, J. D., Gould, M. N., Stewart, R. M., & Kendziorski, C. (2013). EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics, 29(8), 1035–1043. link ↗ |
| Rööpnimetused | Bayesian protein quantification, Bayesian peptide inference, probabilistic proteomics, Bayesian mass spectrometry analysis | Bayesian DE analysis, Bayesian RNA-seq DE, baySeq, EBSeq |
| Seotud | 6 | 6 |
| Kokkuvõte≠ | Bayesian proteomics analysis applies probabilistic models to mass spectrometry data to identify peptides, infer protein presence, and quantify differential protein abundance across conditions. By encoding prior knowledge and propagating uncertainty through each step of the pipeline, Bayesian approaches produce calibrated posterior probabilities of identification and quantification rather than simple point estimates, enabling more principled control of false discovery rates and more honest reporting of uncertainty than purely frequentist alternatives. | Bayesian RNA-seq differential expression analysis applies hierarchical Bayesian models to RNA sequencing read-count data to identify genes whose expression levels differ significantly between biological conditions. Rather than relying solely on p-values, these methods quantify the posterior probability that a gene is differentially expressed, borrowing statistical strength across genes and naturally accommodating low sample sizes common in genomics experiments. |
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