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Capítulos
- 00:05Título
- 02:43Preprocessing: Preparing Input Files for Bayesian Change-point (BCP) Analysis & ChIP Read Densities for Detection of Enriched Islands in Diffuse Data
- 04:48Estimating the Posterior Mean Read Density of Each Block using BCMIX Approximation
- 06:24Post-processing Posterior Means of Diffuse Read Profiles
- 07:06Results: Comparison of BCP and SICER in Analysis of Histone Modification Data
- 11:47Conclusion
Unsere Bayesian ändern Point (BCP)-Algorithmus baut auf state-of-the-art Fortschritte in der Modellierung change-Punkten über Hidden-Markov-Modelle und wendet sie auf Chromatinimmunpräzipitation Sequenzierung (ChIPseq) Datenanalyse. BCP funktioniert gut sowohl breit und punktförmige Datentypen, sondern zeichnet sich durch genaue Identifizierung robust, reproduzierbar Inseln diffuse Histon Bereicherung.
Tags
Bayesian Change-point AlgorithmGenome-wide AnalysisChIPseq Data TypesProtein-DNA InteractionsRead Density ProfilesPeaksTranscription FactorsHistone ModificationsStatistical ModelsHidden Markov Models (HMMs)Ad Hoc ParametersResolutionUsabilityParameter Estimation ProceduresFinite State Classifications
