Tn5 transposase-based epigenomic profiling methods are prone to open chromatin bias
Wang, M.; Zhang, Y.
Show abstract
Epigenetic studies of rare biological samples like mammalian oocytes and preimplantation embryos require low input or even single cell epigenomic profiling methods. To reduce sample loss and avoid inefficient immunoprecipitation, several chromatin immuno-cleavage-based methods using Tn5 transposase fused with Protein A/G have been developed to profile histone modifications and transcription factor bindings using small number of cells. The Tn5 transposase-based epigenomic profiling methods are featured with simple library construction steps in the same tube, by taking advantage of Tn5 transposases capability of simultaneous DNA fragmentation and adaptor ligation. However, the Tn5 transposase prefers to cut open chromatin regions. Our comparative analysis shows that Tn5 transposase-based profiling methods are prone to open chromatin bias. The high false positive signals due to biased cleavage in open chromatin could cause misinterpretation of signal distributions and dynamics. Rigorous validation is needed when employing and interpreting results from Tn5 transposase-based epigenomic profiling methods.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods 95%
- EpiMethylTag simultaneously detects ATAC-seq or ChIP-seq signals with DNA methylation 94%
- Inferring transcriptional regulators through integrative modeling ofpublic chromatin accessibility and ChIP-seq data 94%
Similar papers in this journal
- Z-Flipons conserved between human and mouse are associated with increased transcription initiation rates 95%
- Transcriptome-wide high-throughput mapping of protein-RNA occupancy profiles using POP-seq 94%
- Major cell-types in multiomic single-nucleus datasets impact statistical modeling of links between regulatory sequences and target genes 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.