Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Time-series ChIP-seq Peak Calling× | ChIP-seq Peak Calling× | |
|---|---|---|
| Vakgebied | Bio-informatica | Bio-informatica |
| Familie | Process / pipeline | Process / pipeline |
| Jaar van ontstaan≠ | 2008–2012 (ChIP-seq); time-series extensions ~2015–2020 | 2007–2008 |
| Grondlegger≠ | ENCODE Consortium; extended by Haiminen et al. and broader epigenomics community | Johnson et al. (ChIP-seq concept, 2007); Zhang et al. (MACS algorithm, 2008) |
| Type≠ | Computational epigenomics pipeline | Computational genomics pipeline |
| Oorspronkelijke bron≠ | Landt, S. G., Marinov, G. K., Kundaje, A., Kheradpour, P., Pauli, F., Batzoglou, S., ... & Snyder, M. (2012). ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Research, 22(9), 1813–1831. 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 ↗ |
| Aliassen | longitudinal ChIP-seq analysis, dynamic ChIP-seq peak calling, time-course ChIP-seq, temporal chromatin profiling | ChIP-seq analysis, peak detection, MACS peak calling, ChIP peak identification |
| Verwant≠ | 5 | 6 |
| Samenvatting≠ | Time-series ChIP-seq peak calling extends standard chromatin immunoprecipitation sequencing analysis to samples collected at multiple time points. By identifying and comparing protein-DNA binding peaks across a temporal dimension, the method reveals how transcription factor occupancy, histone modifications, or chromatin remodeler binding evolve during biological processes such as differentiation, circadian cycles, or stimulus response. | 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. |
| ScholarGateGegevensset ↗ |
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