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• W3208163695 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." @default.
• W3208163695 created "2021-11-08" @default.
• W3208163695 creator A5001655498 @default.
• W3208163695 creator A5004799122 @default.
• W3208163695 creator A5009260868 @default.
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• W3208163695 creator A5060867127 @default.
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• W3208163695 creator A5082496274 @default.
• W3208163695 creator A5086474710 @default.
• W3208163695 creator A5089561935 @default.
• W3208163695 date "2021-10-04" @default.
• W3208163695 modified "2023-10-14" @default.
• W3208163695 title "Comprehensive understanding of Tn5 insertion preference improves transcription regulatory element identification" @default.
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• W3208163695 doi "https://doi.org/10.1093/nargab/lqab094" @default.

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