This repository holds python code to detect head-to-tail spliced (back-spliced) sequencing reads, indicative of circular RNA (circRNA) in RNA-seq data. It is also used extensively by circbase.
The scripts in this package were designed by Nikolaus Rajewsky, Antigoni Elefsinioti and Marvin Jens. The current implementation was written by Marvin Jens.
This software is provided to you "as is", without any warranty. We used this code (v1) to produce the results presented in
Nature. 2013 Mar 21;495(7441):333-8.
doi: 10.1038/nature11928. Epub 2013 Feb 27.
Circular RNAs are a large class of animal RNAs with regulatory potency.
Memczak S, Jens M, Elefsinioti A, Torti F, Krueger J, Rybak A, Maier L,
Mackowiak SD, Gregersen LH, Munschauer M, Loewer A, Ziebold U,
Landthaler M, Kocks C, le Noble F, Rajewsky N.
Have a look at Memczak et al. 2013 and the supplementary material for additional information. Please don't contact the authors, who are very busy, regarding this software unless you have found a bug, wish to provide improvements to the code, propose a collaboration, or use our code for commercial purposes. Thank you very much for your understanding!
For non-commercial purposes the code is released under the General Public License (version 3). See the file LICENSE for more detail. If you are interested in commercial use of this software contact the authors.
The current code (v1.2) has some small bug fixes and improvements. Each version deemed stable is tagged and a brief summary of the improvements is given here:
-
v1 : as used in Memczak et al. 2013
-
v1.2 :
- fix the uniqueness handling. Occasionally reads would have either anchor align uniquely, but never both. These rare cases now get flagged as "NO_UNIQ_BRIDGES".
- support all chromosomes in one FASTA file
- store the size of each chromosome upon first access, pad violations of chromosome bounds by padding with 'N'
- category labels have been extended for clarity
("UNAMBIGUOUS_BP" instead of "UNAMBIGUOUS", etc.), in a manner which preserves
functionality of
grepcommands. - by default no longer report junctions with only one uniquely aligned anchor. Original behaviour can be restored using the
--halfuniqswitch. - by default no longer report junctions that lack a read where both anchors align uniquely (NO_UNIQ_BRIDGES keyword).
Original behaviour can be restored using the
--report_nobridgeswitch
-
v2 : (under development): produce multiple anchor lengths to potentially yield more junctions with unique anchor mappings.
The scripts run on Ubuntu 12.04.2 on a 64Bit machine with python 2.7. We do not know if it runs in different environments but other 64Bit unix versions should run if you can get the required 3rd party software installed.
You need to install the python packages numpy and pysam. If there are no packages in your linux distro's repositories, try the very useful python installer (building numpy requires many dependencies, so obtaining pre- compiled packages from a repository is a better option).
pip install --user pysam
Next you need the short read mapper bowtie2 and samtools up and running. samtools now is in the repositories of many distros, but here you can get the most fresh versions:
http://samtools.sourceforge.net/
http://bowtie-bio.sourceforge.net/bowtie2/index.shtml
At this point you should have everything to run a built-in test data set
cd test_data
make
If you get error messages here, first make sure the dependencies are really installed correctly and run on their own. The authors of this code can not give support bowtie2, samtools, python, or any other third-party packages! Sorry, but not enough time for this. If you are sure that the problem is with our code, just zip the test_data folder and e-mail it to us. MAYBE, we can help.
In case you are working with human data and have the hg19 genome and a bowtie2 index around, there is an additional test/sanity-check you can run:
cd test_data
make hek_test2 \
GENOME_HG19=/path_to/hg19.fa \
INDEX_HG19=/path_to/bowtie2_hg19_index
(obviously, the paths to genome and index will have to be changed for this to work)
This will push known spliced reads, belonging to previously identified junctions,
through find_circ.py, then take the found spliced reads and run them
through find_circ.py a second time. Ultimately, it compares the detected splice
sites and ensures the two sets are identical.
If everything goes well you can get started with your real data! :)
You need to have the reference genome and a bowtie2 index for it.
As an example, let's assume you use C.elegans genome ce6 (WS190):
wget -c http://hgdownload.cse.ucsc.edu/goldenPath/ce6/bigZips/chromFa.tar.gz \
-O - | gzip -dc | tar -xO > ce6.fa
This will retrieve the genome from the UCSC website, unpack it into a single fasta file with all chromosomes to build the index:
bowtie2-build ce6.fa bt2_ce6
It is recommended to map your RNA-seq reads against the genome first and keep the part that can not be mapped contiguously to look for splice- junctions afterwards. The genome alignments can be used for gene expression analysis and the unmapped reads will represent a fraction of the input, thus downstream analysis will be faster.
bowtie2 -p16 --very-sensitive --score-min=C,-15,0 --mm \
-x bt2_ce6 -q -U <your_reads.fastq.gz> 2> bowtie2.log \
| samtools view -hbuS - | samtools sort - test_vs_ce6
single out the unaligned reads and split those with good quality into anchors for independent mapping (used to identify splice junctions)
# get the unmapped and pipe through unmapped2anchors.py
samtools view -hf 4 test_vs_ce6.bam | samtools view -Sb - | \
./unmapped2anchors.py unmapped_ce6.bam | gzip \
> ce6_anchors.fastq.gz
Now we have everything to screen for spliced reads, from either linear or head-to-tail (circular) splicing:
mkdir -p <run_folder>
bowtie2 -p 16 --score-min=C,-15,0 --reorder --mm \
-q -U ce6_anchors.fastq.gz -x bt2_ce6 |\
./find_circ.py \
--genome=ce6.fa \
--prefix=ce6_test_ \
--name=my_test_sample \
--stats=<run_folder>/stats.txt \
--reads=<run_folder>/spliced_reads.fa \
> <run_folder>/splice_sites.bed
The prefix ce6_test_ is arbitrary, and pre-pended to every identified splice
junction. You may consider setting it to tmp or similar for single samples out of a
larger set. Note that find_circ.py outputs both, circRNA splice junctions (containing the keyword CIRCULAR) linear splice junctions (containing the keyword LINEAR).
You may want to grep CIRCULAR <run_folder>/splice_sites.bed > circs_sample1.bed or similar, to sort out the circRNAs.
The detected linear and circular candidate splice sites are printed to stdout. The first 6 columns are standard BED. The rest hold various quality metrics about the junction. Here is an overview:
| column | name | description |
|---|---|---|
| 1 | chrom | chromosome/contig name |
| 2 | start | left splice site (zero-based) |
| 3 | end | right splice site (zero-based). |
| (Always: end > start. 5' 3' depends on strand) | ||
| 4 | name | (provisional) running number/name assigned to junction |
| 5 | n_reads | number of reads supporting the junction (BED 'score') |
| 6 | strand | genomic strand (+ or -) |
| 7 | n_uniq | number of distinct read sequences supporting the junction |
| 8 | uniq_bridges | number of reads with both anchors aligning uniquely |
| 9 | best_qual_left | alignment score margin of the best anchor alignment |
supporting the left splice junction (max=2 * anchor_length) |
||
| 10 | best_qual_right | same for the right splice site |
| 11 | tissues | comma-separated, alphabetically sorted list of |
| tissues/samples with this junction | ||
| 12 | tiss_counts | comma-separated list of corresponding read-counts |
| 13 | edits | number of mismatches in the anchor extension process |
| 14 | anchor_overlap | number of nucleotides the breakpoint resides within one anchor |
| 15 | breakpoints | number of alternative ways to break the read with flanking GT/AG |
| 16 | signal | flanking dinucleotide splice signal (normally GT/AG) |
| 17 | strandmatch | 'MATCH', 'MISMATCH' or 'NA' for non-stranded analysis |
| 18 | category | list of keywords describing the junction. Useful for quick grep filtering |
The following list of keywords is assigned to splice sites by find_circ.py for easy filtering:
| keyword | description |
|---|---|
| LINEAR | linear (mRNA) splice site, joining consecutive exons |
| CIRCULAR | potential circRNA splice site. Exons are joint in reverse order. |
| UNAMBIGUOUS_BP | demanding flanking GT/GA, only one way of splitting the spliced |
| read was found (only one possible breakpoint within the read) | |
| PERFECT_EXT | The read sequence between the anchors aligned perfectly during the |
| extension process. | |
| GOOD_EXT | The extension (see above) required not more than one mismatch or one |
| nucleotide overlap with an anchor | |
| OK_EXT | The extension (see above) required not more than two mismatches or two |
| nucleotides overlap with an anchor | |
| ANCHOR_UNIQUE | Unique anchor alignments have been found, supporting both sides |
of the junction. Unless --halfunique is used, this should be |
|
| true for all reported results. | |
| CANONICAL | splice sites are flanked by GT/AG. Unless --noncanonical is |
| used, this should be true for all reported results. | |
| NO_UNIQ_BRIDGES | While both sides of the junction are individually supported by |
| unique anchor alignments, there is not a single read, where both | |
| anchors align uniquely at the same time (bridging the junction). | |
Unless --report_nobridge is used, this should never appear. |
|
| STRANDMATCH | Only appears when --stranded is used and GT/AG were found in the |
| correct orientation |
It is usually a good idea to demand at least 2 reads supporting the junction, unambiguous breakpoint detection and some sane mapping quality:
To get a reasonable set of circRNA candidates try:
grep CIRCULAR <run_folder>/splice_sites.bed | \
grep -v chrM | \
awk '$5>=2' | \
grep UNAMBIGUOUS_BP | grep ANCHOR_UNIQUE | \
./maxlength.py 100000 \
> <run_folder>/circ_candidates.bed
This selects the circular splice sites supported by at least 2 reads with unambiguous detection of the breakpoint (i.e. the exact nucleotides at which splicing occurred), and unique anchor alignments on both sides of the junction. The last part subtracts start from end coordinates to compute the genomic length, and removes splice sites that are more than 100 kb apart. These are perhaps trans-splicing events, but for sure they are so huge they can seriously slow down any downstream scripts you may want to run on this output.
If you intend to analyze multiple samples, it is now strongly advised to
run them individually through find_circ.py, and merge the separate outputs
later! Use the find_circ.py --name <sample_name> flag to assign sample IDs, tissue names, etc. to
each sample.
Merging should then be done with merge_bed.py:
./merge_bed.py sample1.bed sample2.bed [...] > combined.bed
This will deal properly with the various columns: quality scores will be assigned the maximum value of all samples, total read counts will be summed up, tissues column will contain a comma-separated list, etc..
./unmapped2anchors.py -h
Usage:
unmapped2anchors.py <alignments.bam> > unmapped_anchors.qfa
Extract anchor sequences from unmapped reads. Optionally permute.
Options:
-h, --help show this help message and exit
-a ASIZE, --anchor=ASIZE
anchor size
-q MINQUAL, --minqual=MINQUAL
min avg. qual along both anchors (default=5)
-r REV, --rev=REV permute read parts or reverse A,B,R,C,N for control
-R, --reads instead of unmapped reads from BAM, input is
sites.reads from find_circ.py
-F, --fasta instead of unmapped reads from BAM, input is FASTA
file
Usage:
bowtie2 [mapping options] anchors.fastq.gz | find_circ.py [options] > candidates.bed
Options:
-h, --help show this help message and exit
-v, --version get version information
-S SYSTEM, --system=SYSTEM
model system database (optional! Requires byo
library.)
-G GENOME, --genome=GENOME
path to genome (either a folder with chr*.fa or one
multichromosome FASTA file)
-n NAME, --name=NAME tissue/sample name to use (default='unknown')
-p PREFIX, --prefix=PREFIX
prefix to prepend to each junction name (default='')
-q MIN_UNIQ_QUAL, --min_uniq_qual=MIN_UNIQ_QUAL
minimal uniqness for anchor alignments (default=2)
-a ASIZE, --anchor=ASIZE
anchor size (default=20)
-m MARGIN, --margin=MARGIN
maximum nts the BP is allowed to reside inside an
anchor (default=2)
-d MAXDIST, --maxdist=MAXDIST
maximum mismatches (no indels) allowed in anchor
extensions (default=2)
--noncanonical relax the GU/AG constraint (will produce many more
ambiguous counts)
--randomize select randomly from tied, best, ambiguous hits
--allhits in case of ambiguities, report each hit
--stranded use if the reads are stranded. By default it will be
used as control only, use with --strandpref for
breakpoint disambiguation.
--strandpref prefer splice sites that match annotated direction of
transcription
--halfunique also report junctions where only one anchor aligns
uniquely (less likely to be true)
--report_nobridges also report junctions lacking at least a single read
where both anchors, jointly align uniquely (not
recommended. Much less likely to be true.)
-R READS, --reads=READS
write spliced reads to this file instead of stderr
(RECOMMENDED!)
-B BAM, --bam=BAM filename to store anchor alignments that were recorded
as linear or circular junction candidates
-r READS2SAMPLES, --reads2samples=READS2SAMPLES
path to tab-separated two-column file with read-name
prefix -> sample ID mapping
-s STATS, --stats=STATS
write numeric statistics on the run to this file
Usage:
merge_bed.py 1.bed 2.bed [3.bed] [4.bed] [...] > merged.bed
Merge BED or BED-like files on the genomic coordinates. Deals properly
with find_circ.py output and adds a few extra columns.
Options:
-h, --help show this help message and exit
-f FLANK, --flank=FLANK
add flanking nucleotides to define more fuzzy overlap
(default=0)
-s STATS, --stats=STATS
write statistics to this file (instead of stderr)
