Comprehensive understanding of Tn5 insertion preference improves transcription regulatory element identification

Abstract

Tn5 transposase, which can efficiently tagment the genome, has been widely adopted as a molecular tool in next-generation sequencing, from short-read sequencing to more complex methods such as assay for transposase-accessible chromatin using sequencing (ATAC-seq). Here, we systematically map Tn5 insertion characteristics across several model organisms, finding critical parameters that affect its insertion. On naked genomic DNA, we found that Tn5 insertion is not uniformly distributed or random. To uncover drivers of these biases, we used a machine learning framework, which revealed that DNA shape cooperatively works with DNA motif to affect Tn5 insertion preference. These intrinsic insertion preferences can be modeled using nucleotide dependence information from DNA sequences, and we developed a computational pipeline to correct for these biases in ATAC-seq data. Using our pipeline, we show that bias correction improves the overall performance of ATAC-seq peak detection, recovering many potential false-negative peaks. Furthermore, we found that these peaks are bound by transcription factors, underscoring the biological relevance of capturing this additional information. These findings highlight the benefits of an improved understanding and precise correction of Tn5 insertion preference.

Major cell-types in multiomic single-nucleus datasets impact statistical modeling of links between regulatory sequences and target genes

“…The rationale behind the null distributions implemented in the Z-scores model comes from reports that the Tn5 transposase used in the ATACseq protocol has a GC bias 6 . However, a recent comprehensive study specifically addressing this issue did not detect such bias 20 . Furthermore, our own analyses found minimal (if any) effect of GC content the detection of links between ATACseq peaks and target genes, while the number of cells with non-zero counts in both the ATACseq peak and the gene is a major determinant.…”

Major cell-types in multiomic single-nucleus datasets impact statistical modeling of links between regulatory sequences and target genes

“…The rationale behind the null distributions implemented in the Z-scores model comes from reports that the Tn5 transposase used in the ATACseq protocol has a GC bias 15 . However, a recent comprehensive study specifically addressing this issue did not detect such bias 16 . Furthermore, our own analyses found minimal (if any) effect of GC content the detection of links between ATACseq peaks and target genes, while the number of cells with non-zero counts in both the ATACseq peak and the gene is a major determinant.…”

Investigating chromatin accessibility during development and differentiation by ATAC-sequencing to guide the identification of cis-regulatory elements

“…Indeed, ATAC-seq can be performed on single cells [ 17 ] (see below). Limitations of ATAC-seq include the potential contamination of data with mitochondrial DNA and potential sequence or structural biases of the Tn5 enzyme [ 18 , 19 ]. Overall however, the speed, scalability and ease of experiment makes ATAC-seq a popular approach for studying chromatin in dynamic systems.…”