APAtrap: identification and quantification of alternative polyadenylation sites from RNA-seq data
https://sourceforge.net/projects/apatrap/
Refine annotated 3’UTRs and identify novel 3’UTRs or 3’UTR extensions.
For genome having long 3’UTR
identifyDistal3UTR -i Sample1.bedgraph Sample2.bedgraph -m hg19.genemodel.bed -o novel.utr.bed
For genome having short 3’UTR
identifyDistal3UTR -i Sample1.bedgraph Sample2.bedgraph -m rice.genemodel.bed -o novel.utr.bed -w 50 -e 5000
Infer all potential APA sites and estimate their corresponding usages.
For genome having long 3’UTR
predictAPA -i Sample1.bedgraph Sample2.bedgraph -g 2 -n 1 1 -u hg19.utr.bed -o output.txt
For genome having short 3’UTR
predictAPA -i Sample1.bedgraph Sample2.bedgraph -g 2 -n 1 1 -u rice.utr.bed -o output.txt -a 50
Detect genes having significant changes in APA site usage between conditions.
deAPA(input_file, output_file, group1, group2, least_qualified_num_in_group1, least_qualified_num_in_group2, coverage_cutoff)
Dynamic analyses of alternative polyadenylation from RNA-seq reveal a 3’UTR landscape across seven tumour types
http://lilab.research.bcm.edu/dldcc-web/lilab/zheng/DaPars_Documentation/html/DaPars.html
DaPars will use the extracted distal polyadenylation sites to infer the proximal polyadenylation sites based on the alignment wiggle files of two samples. The output in this step will be used by the next step.
python DaPars_Extract_Anno.py -b gene.bed -s symbol_map.txt -o extracted_3UTR.bed
The configure file is the only parameter for DaPars_main.py, which stores all the parameters. The format of the configure is:
#The following file is the result of step 1.
Annotated_3UTR=hg19_refseq_extracted_3UTR.bed
#A comma-separated list of BedGraph files of samples from condition 1
Group1_Tophat_aligned_Wig=Condition_A_chrX.wig
#Group1_Tophat_aligned_Wig=Condition_A_chrX_r1.wig,Condition_A_chrX_r2.wig if multiple files in one group
#A comma-separated list of BedGraph files of samples from condition 2
Group2_Tophat_aligned_Wig=Condition_B_chrX.wig
Output_directory=DaPars_Test_data/
Output_result_file=DaPars_Test_data
#At least how many samples passing the coverage threshold in two conditions
Num_least_in_group1=1
Num_least_in_group2=1
Coverage_cutoff=30
#Cutoff for FDR of P-values from Fisher exact test.
FDR_cutoff=0.05
PDUI_cutoff=0.5
Fold_change_cutoff=0.59
Run this function to get the final result.
python DaPars_main.py configure_file