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| ChIP-seq Peak Calling× | ATAC-seq Analyse× | |
|---|---|---|
| Fagområde≠ | Bioinformatik | Genetik |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 2007–2008 | 2013 |
| Ophavsperson≠ | Johnson et al. (ChIP-seq concept, 2007); Zhang et al. (MACS algorithm, 2008) | Jason Buenrostro, Paul Giresi & William Greenleaf |
| Type≠ | Computational genomics pipeline | Chromatin profiling method |
| Oprindelig kilde≠ | 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 ↗ | Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013). Transposition of native chromatin for fast and sensitive epigenomic profiling of cell populations and tissues. Nature Methods, 10(12), 1213–1218. link ↗ |
| Aliasser≠ | ChIP-seq analysis, peak detection, MACS peak calling, ChIP peak identification | Chromatin accessibility, Open chromatin, Accessible chromatin analysis |
| Relaterede≠ | 6 | 2 |
| Resumé≠ | 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. | ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) is a method for profiling the landscape of chromatin accessibility genome-wide. Developed by Buenrostro and colleagues in 2013, ATAC-seq uses hyperactive transposase to tag open, accessible chromatin regions, enabling rapid and sensitive identification of regulatory DNA elements. ATAC-seq has become a standard technique for characterizing gene regulatory landscapes, discovering cell-type-specific regulatory elements, and inferring gene regulatory networks. |
| ScholarGateDatasæt ↗ |
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