circRIP

circRIP: An accurate tool for identifying circRNA-RBP interaction

1. requisites

pysam (0.14.1)
pandas (1.0.3)
featureCounts (v1.6.1)
gtfToGenePred
bedtools (v2.26.0)
STAR (2.7.5a)
bowtie2 (2.4.2)

2. Identify circRNA from IP/Input

2.1 Identify circRNA using built-in method in circRIP:

    circRIP identify circRNA by parsing STAR mapping results and re-mapping the unmapped reads to known circRNA back-splice junctions(BSJs)

    requisited input files:

        test_ip_1.fq.gz,test_ip_2.fq.gz,test_input_1.fq.gz,test_input_2.fq.gz  fastq files of IP/Input
        ~/starindex/    the STAR index
        ~/bowtie2index/circBase the bowtie2 index of circRNA junctions 
        gencode.v32.annotation.gtf  the GTF format gene annotation
        GRCh38.p10.genome.fa    the genome fasta file
        
    command lines:

	# IP
	python circRIP.py  circRNAIdentify -fq1 test_ip_1.fq.gz -fq2 test_ip_2.fq.gz \
	                   -star_index ~/starindex/ -bowtie2_index ~/bowtie2index/circBase \
	                   -p 30 -gtf gencode.v32.annotation.gtf -genome GRCh38.p10.genome.fa -prefix test_ip.circ -junLen 100
	# Input
	python circRIP.py  circRNAIdentify -fq1 test_input_1.fq.gz -fq2 test_input_2.fq.gz \
	                    -star_index ~/starindex/ -bowtie2_index ~/bowtie2index/circBase \
	                    -p 30 -gtf gencode.v32.annotation.gtf -genome GRCh38.p10.genome.fa -prefix test_input.circ -junLen 100


    
results:

    the column of test_ip/input.circ is:

        1, Chromosome
        2, circRNA start coordinates
        3, circRNA end coordinates
        4, circRNA BSJs reads counts

2.2 Identify circRNA using other softwares:

 Users are free to use the circRNA recognition software, as long as the following conditions are met:
 
    1, transform the results to four columns format as described above.
    2, bam files of linear reads(to detecte forward-splice junctions, splice-aware aligners such as STAR should be used to perform reads mapping)

3. Identify IP-enriched circRNA:

circRIP identify IP-enriched circRNA by c-test:

requisited input files: 

    test_ip.circ/test_input.circ    circRNAs in four columns format
    test_ip.circAligned.out.bam/test_input.circAligned.out.bam    bam files of linear reads
    gencode.v32.annotation.gtf    the GTF format gene annotation
    GRCh38.p10.genome.fa    the genome fasta file
    
command lines:
   

python circRIP.py EnrichedcircRNA -ip_circ test_ip.circ -input_circ test_input.circ -gtf gencode.v32.annotation.gtf \
                  -ip_bam test_ip.circAligned.out.bam -input_bam test_input.circAligned.out.bam -prefix test.final.out \
		  -G GRCh38.p10.genome.fa


results:
    circRIP generates two output files test.final.out and test.final.out.html, test.final.out is the txt format results and test.final.out.html is a web report page
    the column of test.final.out is:
        1, circRNA ID(Chromosome:start|end)
        2, circRNA host gene symbol
        3, circRNA BSJs in IP
        4, circRNA BSJs in Input
        5, circRNA CPM in IP
        6, circRNA CPM in Input
        7, IP/Input ratio
        8, P value
        9, circRNA status (enriched or non-enriched)
    
    test.final.out.html: