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| Gọi đỉnh ChIP-seq Bayes× | Phân tích biểu hiện gen vi sai RNA-seq theo phương pháp Bayes× | |
|---|---|---|
| Lĩnh vực | Tin sinh học | Tin sinh học |
| Họ | Process / pipeline | Process / pipeline |
| Năm ra đời≠ | 2008–2009 | 2010–2013 |
| Người khởi xướng≠ | Spyrou et al. (BayesPeak, 2009); broader Bayesian ChIP-seq framework developed across multiple groups ~2008–2012 | Kendziorski et al. (EBSeq); Hardcastle & Kelly (baySeq) |
| Loại≠ | Probabilistic signal detection pipeline | Bayesian statistical inference pipeline |
| Công trình gốc≠ | Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., & Liu, X. S. (2008). Model-based analysis of ChIP-Seq (MACS). Genome Biology, 9(9), R137. DOI ↗ | 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 ↗ |
| Tên gọi khác | Bayesian ChIP-seq analysis, probabilistic peak detection, Bayesian peak caller, ChIP-seq Bayesian enrichment calling | Bayesian DE analysis, Bayesian RNA-seq DE, baySeq, EBSeq |
| Liên quan | 6 | 6 |
| Tóm tắt≠ | Bayesian ChIP-seq peak calling applies probabilistic models — typically Poisson, negative binomial, or hidden Markov models with Bayesian inference — to detect genomic regions enriched for a protein of interest in chromatin immunoprecipitation followed by sequencing experiments. By explicitly modelling read-count noise and incorporating prior distributions, Bayesian callers yield posterior probabilities of enrichment rather than simple p-values, providing a principled framework for uncertainty quantification across the genome. | 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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