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ChinaMu is the largest sequence‐indexed Mutator (Mu) transposon insertional library in maize (Zea mays). In this study, we made significant improvements to the size and quality of the ChinaMu library. We developed a new Mu‐tag isolation method Mu‐Tn5‐seq (MuT‐seq). Compared to the previous method used by ChinaMu, MuT‐seq recovered 1/3 more germinal insertions, while requiring only about 1/14 of the sequencing volume and 1/5 of the experimental time. Using MuT‐seq, we identified 113,879 germinal insertions from 3,168 Mu‐active F1 families. We also assembled a high‐quality genome for the Mu‐active line Mu‐starter, which harbors the initial active MuDR element and was used as the pollen donor for the mutation population. Using the Mu‐starter genome, we recovered 33,662 (15.6%) additional germinal insertions in 3,244 (7.4%) genes in the Mu‐starter line. The Mu‐starter genome also improved the assignment of 117,689 (54.5%) germinal insertions. The newly upgraded ChinaMu dataset currently contains 215,889 high‐quality germinal insertions. These insertions cover 32,224 pan‐genes in the Mu‐starter and B73Ref5 genomes, including 23,006 (80.4%) core genes shared by the two genomes. As a test model, we investigated Mu insertions in the pentatricopeptide repeat (PPR) superfamily, discovering insertions for 92% (449/487) of PPR genes in ChinaMu, demonstrating the usefulness of ChinaMu as a functional genomics resource for maize. [ABSTRACT FROM AUTHOR]

Knowledge of the transcription factor binding landscape (TFBL) is necessary to analyze gene regulatory networks for important agronomic traits. However, a low‐cost and high‐throughput in vivo chromatin profiling method is still lacking in plants. Here, we developed a transient and simplified cleavage under targets and tagmentation (tsCUT&Tag) that combines transient expression of transcription factor proteins in protoplasts with a simplified CUT&Tag without nucleus extraction. Our tsCUT&Tag method provided higher data quality and signal resolution with lower sequencing depth compared with traditional ChIP‐seq. Furthermore, we developed a strategy combining tsCUT&Tag with machine learning, which has great potential for profiling the TFBL across plant development. [ABSTRACT FROM AUTHOR]