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| Status |
Public on Mar 22, 2021 |
| Title |
BSPCR_ZFSssI_fwa_nrpd1 |
| Sample type |
SRA |
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| Source name |
BSPCR_ZFSssI_fwa_nrpd1
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| Organism |
Arabidopsis thaliana |
| Characteristics |
tissue: inflorescence
genotype: ZFSssI_in_fwa_nrpd1
ecotype: Columbia 0
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| Growth protocol |
Plant materials All plants in this study were grown under long-day conditions (16 h light/8 h dark). The fwa-4 mutant has been described previously (Johnson et al.,2014), as have the fwa nrpd1, fwa drm1 drm2 and fwa nrpe1 lines (Gallego-Bartolome et al., 2019). The pMDC123-ZF-3xFLAG-SssI plasmid was transformed into Agrobacterium AGLO and transformed into the different backgrounds by Agrobacterium-mediated floral dipping. T1 transgenic plants were grown on 1/2 MS medium + Glufosinate 50 μg/mL (Goldbio) in growth chambers under long-day conditions and transplanted to soil. The selection of transgenic lines for experiments was based on i) early flowering T1 plants for the lines in the fwa backgrounds and ii) protein expression by WB for the lines in the Col-0 background. Following transgenic generations were germinated directly on soil and presence of the transgene was done by genotyping. Plants were not selected for homozygocity except for the T4 populations used for the ChIP experiment. Controls in this study correspond to untransformed plants of the different backgrounds used coming from the same seed stock. Flowering time was scored by counting the total number rosette and caulinar leaves. In the flowering time dot plots, each dot represents the flowering time of individual plants. Plants with 20 or fewer leaves were considered early flowering. The samples used for all our genomics data correspond to inflorescence tissue collected during the day.
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| Extracted molecule |
genomic DNA |
| Extraction protocol |
BS-PCR-seq was performed as previously described (GallegoBartolome et al., 2019). Briefly, leaf tissues from adult plants of representative T2 lines containing the ZF-SssI transgene were collected and DNA was extracted following a CTAB-based method. Bisulfite conversion was done using the Epitect Bisulfite Conversion kit (QIAGEN). The following regions of FWA were analyzed: Region 1 (chr4: 13038143-13038272); Region 2 (chr4: 13038356-13038499); Region3 (chr4: 13038568-13038695), which cover fragments of the promoter and 5′ transcribed region of FWA. Pfu Turbo Cx (Agilent) was used to amplify bisulfite-treated DNA using primers containing Illumina adaptors. The primers used are listed in Table S1.
For each sample, individual PCR products from each of the three FWA regions were pooled and purified using AMPure beads (Beckman Coulter) before making the libraries. Libraries were made from purified PCR products using a TruSeq Nano DNA Library Prep kit for Neoprep automated library preparation machine (Illumina), a Kapa DNA hyper kit (Kapa Biosystems) with Illumina TruSeq DNA adapters or an Ovation Ultralow V2 kit (NuGEN). Libraries were sequenced on Illumina HiSeq 2000 or HiSeq 2500.
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| Library strategy |
OTHER |
| Library source |
genomic |
| Library selection |
other |
| Instrument model |
Illumina iSeq 100 |
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| Description |
PCR amplified DNA
BS-PCR over FWA in ZFSssI_in_fwa_nrpd1_region1to3
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| Data processing |
Library strategy: BSPCR-seq
BS-PCR-seq analysis was conducted as previously described (Gallego-Bartolome et al., 2019). Briefly, raw sequencing reads with designed BS-PCR primers were first filtered and trimmed based on the primer sequence with customized scripts. Trimmed reads were then mapped to the reference TAIR10 genome with BSMAP (Xi et al., 2009) (v.2.74) by allowing maximal two mismatches (-v 2), one best hit (-w 1) and to both strands (-n 1). Methylation level at each cytosine was then calculated with BSMAP (methratio.py) script by only keeping unique mapped reads (-u). Reads with more than three consecutive methylated CHH sites were removed as previously described 19. Methylation levels at each cytosine were calculated as #C/(#C+#T). Cytosines with less than 20 reads coverage were excluded for further analysis. To visualize the BS-PCR-seq data, only cytosines within amplified regions were kept and plotted with R (ggplot2 package, https://ggplot2.tidyverse.org/).
Genome_build: tair10
Supplementary_files_format_and_content: tab delimited text
WGBS analysis was performed as previously described (Gallego-Bartolome et al.,2019). Raw reads were first aligned to the reference TAIR10 genome using BSMAP (Xi et al., 2009) (v2.74) by allowing up to two mismatches (-v 2), one best hit (-w 1) and to both strands (-n 1). Methylation level at each cytosine was then calculated with BSMAP (methratio.py) script by only keeping unique mapped reads (-u). Reads with more than three consecutive methylated CHH sites were removed as previously described (Cokus et al.,2008). Methylation levels at each cytosine were calculated as #C/(#C+#T). DMRs between ZF-SssI and Col-0 were defined similar to before using the R package DMRcaller (Gallego-Bartolome et al.,2019). To increase coverage for DMR analysis, biological replicates were merged for each genotype (ZF-SssI + and ZF-SssI -), each generation (T2 to T5) and each transgenic lines (line 1 and line 2). In general, the whole TAIR10 genome was divided into 200 bp bins, and only bins with at least four cytosines, with each cytosine covered at least four times, more than 10% more methylation in ZF-SssI than Col-0, and a significance level of less than 0.05 were kept. To define hCG DMRs for T2 and T3, the intersect hCG DMRs of two transgenic lines in each generation were first calculated, then the union set of T2 and T3 in the same genotype (either ZF-SssI + or ZF-SssI -) were kept. DMRs overlapping with 200 bp bins in each cluster were considered as DMRs specific for certain clusters. Genomic location for DMRs and mCG equivalent control were annotated using the Homer (Heinz et al.,2010) ‘annotatePeaks’ function with default parameters. For T4 and T5, two transgenic lines were separated in order to trace the heritable hCG DMRs. To define heritable hCG DMRs, T2 ZF-SssI – were compared with T2 ZF-SssI + and the shared hCG DMRs were considered as heritable hCG DMRs in T2 ZF-SssI –. For T3 ZF-SssI + and ZF-SssI -, hCG DMRs were overlapped with T2 ZF-SssI +. For T4 ZF-SssI – heritable hCG sites, DMRs were first intersected with T3 ZF-SssI + and then intersected with T2 ZF-SssI + while T5 ZF-SssI – heritable hCG sites were further intersected with T4 ZF-SssI – hCG DMRs.
Genome_build: tair10
Supplementary_files_format_and_content: bigwig
ATAC-seq analysis was performed as previously described (Potok et al., 2019, Buenrostro et al., 2013). Briefly, paired-end reads were aligned to the TAIR10 reference genome with bowtie (Langmead et al., 2009) (v0.12.8) by allowing maximal two mismatches, uniquely mapped reads (-m 1) and the maximal 2 kb distance between pairs (-X 2000). PCR duplicated reads were removed using SAMTools (Li et al., 2009) (v1.19) ‘rmdup’ function and visualized with ngs.plot (Shen et al., 2014) or deepTools (Ramirez et al., 2014).
Genome_build: tair10
Supplementary_files_format_and_content: tab delimited text
For ChIP-seq data, raw reads were first mapped to the reference TAIR10 genome with Bowtie (Langmead et al.,2009) (v0.12.8) by allowing uniquely mapped reads and maximal two mismatches. PCR-duplicated reads were then filtered with SAMTools (Li et al.,2009) (v 1.19) . To call ZF-SssI FLAG peaks, the MACS2 calldiff function (Zhang et al.,2008) (v 2.1.2) was used to compare ZF-SssI FLAG ChIP-seq and Col-0 FLAG ChIP-seq data with default parameters. Genomic location and enriched motifs of ZF-SssI FLAG-specific peaks were then annotated with Homer (Heinz et al.,2010) ‘annotatePeaks’ and ‘findMotifGenome’ functions using 100 bp flanking the summit of the peaks. Promoter regions were defined as default in homer (upstream 1kb and downstream 100bp of TSS). ChIP-seq data visualizations were performed using ngs.plot (Shen et al.,2014) or deepTools (Ramirez et al.,2014).
Genome_build: tair10
Supplementary_files_format_and_content: bigwig
ATAC-seq analysis was performed as previously described (Potok et al.,2019, Buenrostro et al.,2013). Briefly, paired-end reads were aligned to the TAIR10 reference genome with bowtie (Langmeadet al.,2009) (v0.12.8) by allowing maximal two mismatches, uniquely mapped reads (-m 1) and the maximal 2 kb distance between pairs (-X 2000). PCR duplicated reads were removed using SAMTools (Li et al.,2009) (v1.19) ‘rmdup’ function and visualized with ngs.plot (Shen et al.,2014) or deepTools (Ramirez et al.,2014).
Genome_build: tair10
Supplementary_files_format_and_content: bigwig
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| Submission date |
Sep 16, 2020 |
| Last update date |
Mar 22, 2021 |
| Contact name |
Wanlu Liu |
| Organization name |
Zhejiang University
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| Department |
Zhejiang University - University of Edinburgh Institute
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| Lab |
Wanlu Liu
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| Street address |
718 East Haizhou Rd.,
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| City |
Haining |
| State/province |
Zhejiang |
| ZIP/Postal code |
314400 |
| Country |
China |
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| Platform ID |
GPL29156 |
| Series (1) |
| GSE158027 |
Ectopic targeting of CG DNA methylation in Arabidopsis with the bacterial SssI methyltransferase. |
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| Relations |
| BioSample |
SAMN16179494 |
| SRA |
SRX9132034 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM4785476_BSPCR_ZFSssI_fwa_nrpd1_methratio.txt.gz |
1.3 Kb |
(ftp)(http) |
TXT |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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