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| Байесовско определяне на пикове при ChIP-seq× | Извикване на пикове в ChIP-seq× | |
|---|---|---|
| Област | Биоинформатика | Биоинформатика |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 2008–2009 | 2007–2008 |
| Създател≠ | Spyrou et al. (BayesPeak, 2009); broader Bayesian ChIP-seq framework developed across multiple groups ~2008–2012 | Johnson et al. (ChIP-seq concept, 2007); Zhang et al. (MACS algorithm, 2008) |
| Тип≠ | Probabilistic signal detection pipeline | Computational genomics pipeline |
| Основополагащ източник | 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 ↗ | 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 ↗ |
| Други названия | Bayesian ChIP-seq analysis, probabilistic peak detection, Bayesian peak caller, ChIP-seq Bayesian enrichment calling | ChIP-seq analysis, peak detection, MACS peak calling, ChIP peak identification |
| Свързани | 6 | 6 |
| Резюме≠ | 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. | ChIP-seq peak calling is a computational pipeline that identifies genomic regions where a protein of interest — a transcription factor or histone modification — is enriched, based on sequencing reads from chromatin immunoprecipitation experiments. It converts raw sequencing data into a set of high-confidence binding or modification sites across the genome, enabling downstream analysis of gene regulation, chromatin state, and epigenetic mechanisms. |
| ScholarGateНабор от данни ↗ |
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