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Abstract The canonical non-homologous end joining (c-NHEJ) repair pathway, generally viewed as stochastic, has recently been shown to produce predictable outcomes in CRISPR-Cas9 mutagenesis. This predictability, mainly in 1-bp insertions and small deletions, has led to the development of in-silico prediction programs for various animal species. However, the predictability of CRISPR-induced mutation profiles across species remained elusive. Comparing CRISPR-Cas9 repair outcomes between human and plant species reveals significant differences in 1-bp insertion profiles. The high predictability observed in human cells links to the template-dependent activity of human Polλ. Yet plant Polλ exhibits dual activities, generating 1-bp insertions through both templated and non-templated manners. Polλ knockout in plants leads to deletion-only mutations, while its overexpression enhances 1-bp insertion rates. Two conserved motifs are identified to modulate plant Polλ‘s dual activities. These findings unveil the mechanism behind species-specific CRISPR-Cas9-induced insertion profiles and offer strategies for predictable, precise genome editing through c-NHEJ. 

Path-based solutions have been shown to be useful for various graph analysis tasks, such as link prediction and graph clustering. However, they are no longer adequate for handling complex and gigantic graphs. Recently, motif-based analysis has attracted a lot of attention. A motif, or a small graph with a few nodes, is often considered as a fundamental unit of a graph. Motif-based analysis captures high-order structure between nodes, and performs better than traditional "edge-based" solutions. In this paper, we study motif-path , which is conceptually a concatenation of one or more motif instances. We examine how motif-paths can be used in three path-based mining tasks, namely link prediction, local graph clustering and node ranking. We further address the situation when two graph nodes are not connected through a motif-path, and develop a novel defragmentation method to enhance it. Experimental results on real graph datasets demonstrate the use of motif-paths and defragmentation techniques improves graph analysis effectiveness.