meRIP_count: Count aligned reads of meRIP-seq data
In ZhenWei10/meripQC: Quality control and inference for m6A-seq datasets.

Description Usage Arguments Details Value See Also Examples

View source: R/meRIP_count_annotation.R

meRIP_count_annotation is used to count bam files of the meRIP-seq data, the ranges of methylation is either user provided or comming from the regions derived from peak calling.

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meRIP_count(bam_IP, bam_input, bam_IP_treated = NULL,
  bam_input_treated = NULL, annotation_gr = NULL, bin_width = 100,
  sample_detail = NULL)

`bam_IP`	A character vector of the bam file directories for (control) IP samples.
`bam_input`	A character vector of the bam file directories for (control) input samples.
`bam_IP_treated`	A character vector of the bam file directories for treated IP samples. Default setting is NULL.
`bam_input_treated`	A character vector of the bam file directories for treated input samples. Default setting is NULL. The above 2 arguments should be filled only when conducting the differential methylation analysis.
`annotation_gr`	A `GenomicRanges` object containing the annotation of the methylation.
`bin_width`	The bin width used when count the reads, default setting is 100, the bin is only resized when using user provided annotation.
`sample_detail`	A character vector containing the details of the treatment or batch information, the length and order should correspond to the bam files input of the previous arguments. (Optional)
`GFF_dir`	A character vector containing the directory to GFF files.
`txdb`	A txdb object containing the genome's transcript annotation. If one of the above 2 arguments is filled, the count will based on the peaks generated by exomePeak under default setting.

This function will count reads of meRIP-seq against a user defined annotation of the modification; alternatively, the count can be conducted on de-novo generated regions reported by peak calling algorithm.

For m6A RNA modification, the recommended annotations are m6A sites collected by MeT-DB and RMBase; the annotations from the single based resolution data (miCLIP or m6A-CLIP) are also recommended.

This function will return a SummarizedExperiment object, which is the input of the downstream QC analysis by meRIP_QC_report.

meRIP_QC_report

#For methylation:
meRIP_count_annotation(
bam_IP = c("./Control_IP_rep1.bam","./Control_IP_rep2.bam"),
bam_input = c("./Control_input_rep1.bam","./Control_input_rep2.bam"),
annotation_gr = RMBase2_mm10_gr,
sample_detail = c("sh-control","sh-control","sh-control","sh-control"),
bin_width = 100
)

#For differential methylation:
meRIP_count_annotation(
bam_IP = c("./Control_IP_rep1.bam","./Control_IP_rep2.bam"),
bam_input = c("./Control_input_rep1.bam","./Control_input_rep2.bam"),
bam_IP_treated = c("./Treated_input_rep1.bam","./Treated_input_rep2.bam"),
bam_input_treated = c("./Treated_input_rep1.bam","./Treated_input_rep2.bam"),
annotation_gr = RMBase2_mm10_gr,
sample_detail = c("sh-control","sh-control","sh-control","sh-control","FTO-ko","FTO-ko","FTO-ko","FTO-ko"), #Label what ever you want by the order of the samples.
bin_width = 100
)