SANTON: Sequencing Analysis Toolkits for Off-target Nomination
Qiu, H.; Wang, R.; Wang, S.; Mulbagal, A. M.; Anderson, E.; Ge, H. G.
Show abstract
BackgroundGenome-wide off-target nomination and screening sequencing methods, such as synthetic oligonucleotide-based sequencing and whole genome-based sequencing, evaluate the precision and safety of gene-editing technologies. However, there remains a lack of comprehensive bioinformatics tools for analyzing the sequencing data generated by off-target nomination assays for various gene editors, including Cas9, Cas12a, and base editors. ResultsWe introduce a Sequencing Analysis Toolkits for Off-target Nomination (SANTON) for the identification and quantification of potential off-target sites using datasets generated from synthetic oligonucleotide-based sequencing and whole genome-based sequencing (e.g., Digenome-seq) methods. By applying SANTON to a Cas9 treated oligo synthesization-based sequencing data, a comprehensive set of potential off-target sites are evaluated in which the top off-target sites displayed highly constituency with a published study using in vivo method (e.g, GUIDE-seq). Utilizing SANTON to previously published Digenome-seq datasets, we identified more potential off-target regions than previous studies, including ones showing significantly higher cleavage level than the on-target site. To the best of our knowledge, SANTON is the first public package capable of analyzing synthetic oligonucleotide-based sequencing data and the first to specific optimized for the analysis of whole genome-based sequencing analysis for Cas12a and base editors. We demonstrated the capability of SANTON to effectively analyze complex cleavage patterns by a diverse editing system. ConclusionsSANTON provides a powerful computational solution to support genome editing research, facilitating more reliable and comprehensive off-target profiling.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- REPIC: A database for exploring N6-methyladenosine methylome 95%
- DENA: training an authentic neural network model using Nanopore sequencing data of Arabidopsis transcripts for detection and quantification of N6-methyladenosine on RNA 95%
- DNA methylation calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation 95%
Similar papers in this journal
- PtWAVE: A High-Sensitive deconvolution software of sequencing trace for the Detection of Large Indels in Genome Editing 97%
- Identification and Utilization of Copy Number Information for Correcting Hi-C Contact Map of Cancer Cell Line 95%
- Boosting variant-calling performance with multi-platform sequencing data using Clair3-MP 95%
Similar papers in this journal
"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.