NOTICE: Bedtools version 2.25 has bug cause DCC fail at a step involve function groupby. I'm working on removing the dependence on Bedtools, by now the solution is as follows:

• Install Bedtools version 2.24.
• Make sure version 2.24 is the default bedtools version in your environment before running DCC.

DCC is a python package intended to detect and quantify circRNAs with high specificity. DCC works with the STAR (Dobin et al., 2013) chimeric.out.junction files which contains chimerically aligned reads including circRNA junction spanning reads.

DCC dependes on pysam, pybedtools, numpy and HTSeq The installation process of DCC will automatically check for the dependencies, if any dependence is missing from path, it will be automatically installed.

To download DCC from github, you can:

1. Download DCC release: https://github.com/dieterich-lab/DCC/releases

$ tar -zxvf DCC-<version>.tar.gz

$ cd DCC-<version>

$ python setup.py install

2. From git clone

$ git clone git@github.com:dieterich-lab/DCC.git

$ cd DCC

$ python setup.py install

3. To check for installation, do:

# Get help
$ DCC -h

4. If somehow DCC is not included in your path, you also run DCC by:

$ python <DCC directory>/scripts/DCC <Options>
# If this gives installation error, you can always run DCC as a script by:
$ python <DCC directory>/DCC/main.py <Options>

The detection of circRNAs from RNAseq data through DCC can be summarised by three steps:

1. Map RNAseq data from quality checked fastq files. For paired end data, it is recommended to map with two pairs jointly, and also separately. This is because STAR does not output reads or read pairs which have more than one chimeric junctions.
2. Prepare files needed by DCC. In summary, only one file is mandatory: 'samplesheet', which specifies where your Chimeric.out.junction files are stored, with one sample per line. Three other files are recommended: 1). 'Repetitive_regions.gtf', a GTF format annotation of repetitive regions, which is used to filter out circRNA candidates from repetitive regions. 2). For paired end sequencing, 'mate1' and 'mate2', which specify where your Chimeric.out.junction files from mate separate mapping are.
3. Run DCC. DCC can be run for different purpose with different modes. In summary, 1) Run DCC to detect circRNAs and host gene expression (use -D and -G option ) 2). Run DCC only to detect circRNAs (use -D option only).

In this tutorial, we use Westholm et al. 2014 data as an example. The data are paired end, stranded RiboMinus RNAseq data from Drosophila.melanogaster, consisting of samples of 3 developmental stages (1days, 4days and 20days) collected from heads. You can download the data as fastq files with NCBI SRA accession number: SRP001696.

1. Map RNA-seq data with STAR (Dobin et al., 2013). Note that --alignSJoverhangMin and --chimJunctionOverhangMin should use the same value, to make the circRNA expression and linear gene expression level comparable.

• Do pairs joined mapping first. If your data are paired end, do additional mates separate mapping (not mandatory, but will increase the sensitivity of DCC detection, because it collect small circRNAs which appear with one chimeric junction point at each read mate). If the data is single end, only one mapping step is needed. In this case, we have PE sequencing data.

$ mkdir Sample1
$ cd Sample1
$ STAR --runThreadN 10   --genomeDir [genome]  --outSAMtype BAM Unsorted --readFilesIn Sample1_1.fastq.gz  Sample1_2.fastq.gz   --readFilesCommand zcat  --outFileNamePrefix [sample prefix] --outReadsUnmapped Fastx  --outSJfilterOverhangMin 15 15 15 15 --alignSJoverhangMin 15 --alignSJDBoverhangMin 15 --outFilterMultimapNmax 20   --outFilterScoreMin 1   --outFilterMatchNmin 1   --outFilterMismatchNmax 2  --chimSegmentMin 15    --chimScoreMin 15   --chimScoreSeparation 10  --chimJunctionOverhangMin 15

• (Skip when you have single end data). Mates separate mapping. Be careful that, what you define as first mate (mate1) should also appears the first in the joined mapping. In this case, SamplePairedRead_1.fastq.gz is the first mate which came first above.

# Make a directory for mate1
$ mkdir mate1
$ STAR --runThreadN 10   --genomeDir [genome]  --outSAMtype None --readFilesIn Sample1_1.fastq.gz  --readFilesCommand zcat   --outFileNamePrefix [sample prefix] --outReadsUnmapped Fastx  --outSJfilterOverhangMin 15 15 15 15 --alignSJoverhangMin 15 --alignSJDBoverhangMin 15 --seedSearchStartLmax 30  --outFilterMultimapNmax 20   --outFilterScoreMin 1   --outFilterMatchNmin 1   --outFilterMismatchNmax 2  --chimSegmentMin 15    --chimScoreMin 15   --chimScoreSeparation 10  --chimJunctionOverhangMin 15

$ cd ..
$ mkdir mate2
# Do the same mapping as mate1 for mate2

2. Detect circRNAs from chimeric.out.junction files with DCC

• It is strongly recommended to specify a repetitive region file in GTF format for filtering. You can obtain this file through UCSC table browser: http://genome.ucsc.edu/cgi-bin/hgTables. Select your genome, select group as "Repeats" or "Variation and Repeats". For the track, I recommend choose RepeatMasker and Simple Repeats and combine the results. NOTE: the output file needs to comply with GTF format specification. Also note the name of chromosomes from different databases differs, e.g. "1" for chromosome 1 from ensembl, whereas "chr1" for chromosome 1 from UCSC. You need to have the same chromosome names for your gtf annotation file and repeats file. An example to convert UCSC chromosome to ensembl would be sed -i 's/^chr//g' your_repeatfile.gtf.

• Prepare path files to specify where is your chimeric.junction.out files are.

  First, "samplesheet" file, in which you specify your chimeric.out.junction file's absolute paths (mates joined mapping chimeric.out.junction files, for paired end data), one line per sample.

  Second (only if you have paired end sequencing data), "mate1" and "mate2" files. As with the "samplesheet" file, you specify where your mate1 and mate2 separately mapped chimeric.junction.out files are.

  You can find a example of this files for Westholm et al. data at:

$ <DCC directory>/DCC/data/samplesheet # Mates jointly mapped chimeric.junction.out files
$ <DCC directory>/DCC/data/mate1 # Mate1 independently mapped chimeric.junction.out files
$ <DCC directory>/DCC/data/mate1 # Mate2 independently mapped chimeric.junction.out files

• After all the preparation steps, you can now run DCC for circRNA detection.

# Call DCC to detect circRNAs, using Westholm data as example.
$ DCC @samplesheet -mt1 @mate1 -mt2 @mate2 -D -R [Repeats].gtf -an [Annotation].gtf -Pi -F -M -Nr 5 6 -fg -G -A [Reference].fa

# For single end, non-strand data:
$ DCC @samplesheet -D -R [Repeats].gtf -an [Annotation].gtf -F -M -Nr 5 6 -fg -G -A [Reference].fa

$ DCC @samplesheet -mt1 @mate1 -mt2 @mate2 -D -S -R [Repeats].gtf -an [Annotation].gtf -Pi -F -M -Nr 5 6 -fg

# Details of parameters please refer to the help page of DCC:
$ DCC -h

By default, DCC assume the data are stranded, for non-stranded data, use -N flag. NOTE: -F flag is mandatory, if you want to filter on the results. All filtering steps are not mandatory, but strongly recommended.

Finished!!!

The output of DCC include: CircRNACount, CircCoordinates, LinearCount and CircSkipJunctions.

CircRNACount: a table containing read counts for circRNAs detected. First three columns are chr, circRNA start, circRNA end. From fourth column on are the circRNA read counts, one sample per column, shown in the order given in your samplesheet.

CircCoordinates: CircRNA annotation in BED format. The columns are chr, start, end, genename, junctiontype (come from STAR, 1 for GT/AG, 2 for CT/AC), strand, circRNA region (startregion-endregion), overall regions (the genomic features circRNA coordinates interval covers).

LinearCount: host gene expression count table, same setup with CircRNACount file.

CircSkipJunctions: CircSkip junctions. First three columns are the same with LinearCount/CircRNACount, the rest columns are circSkip junctions found for each sample. circSkip junction shows in the format: chr:start-end:count (chr1:1787-6949:10 for example. It's possible that for one circRNA multiple circSkip junctions are found, because circRNA possible come from multiple RNA isoforms. In this case, multiple circSkip junctions are delimited with semicolon). 0 implies no circSkip junction found for this circRNA.

1. Install CircTest package as described: https://github.com/dieterich-lab/CircTest
2. Read and load DCC output into R

library(CircTest)

CircRNACount <- read.delim('CircRNACount',header=T)
LinearCount <- read.delim('LinearCount',header=T)
CircCoordinates <- read.delim('CircCoordinates',header=T)

CircRNACount_filtered <- Circ.filter(circ = CircRNACount, linear = LinearCount, Nreplicates = 6, filter.sample = 6, filter.count = 5, percentage = 0.1)
CircCoordinates_filtered <- CircCoordinates[rownames(CircRNACount_filtered),]
LinearCount_filtered <- LinearCount[rownames(CircRNACount_filtered),]

Alternatively, load the processed Westholm et al. data from CircTest package.

library(CircTest)

data(Circ)
CircRNACount_filtered <- Circ
data(Coordinates)
CircCoordinates_filtered <- Coordinates
data(Linear)
LinearCount_filtered <- Linear

3. Test for host-independently regulated circRNAs

test=Circ.test(CircRNACount_filtered,LinearCount_filtered,CircCoordinates_filtered,group=c(rep(1,6),rep(2,6),rep(3,6)))
# Significant result show in a summary table
View(test$summary_table)

4. Visuallize the significantly host-independently regulated circRNAs

for (i in rownames(test$summary_table))  {
 Circ.ratioplot( CircRNACount_filtered, LinearCount_filtered, CircCoordinates_filtered, plotrow=i, 
                 groupindicator1=c(rep('1days',6),rep('4days',6),rep('20days',6)), 
                 lab_legend='Ages' )
}

• ERROR: File <file> has inconsistent naming convention for record: CHR_MG132_PATCH 124291803 124294101 ENSMUSG00000098810 . - protein_coding exon CAAA01180111.1

Please update your bedtools at least to 2.24.0, and make sure the new version is included in your path.