TIDDIT: Is a tool to used to identify chromosomal rearrangements using Mate Pair or Paired End sequencing data. TIDDIT identifies intra and inter-chromosomal translocations, deletions, tandem-duplications and inversions, using supplementary alignments as well as discordant pairs.
TIDDIT has two analysis modules. The sv mode, which is used to search for structural variants. And the cov mode that analyse the read depth of a bam file and generates a coverage report.
TIDDIT requires standard c++/c libraries, python 2.7 or 3.6, cython, and Numpy. To compile TIDDIT, cmake must be installed. samtools is reuquired for reading cram files (but not for reading bam).
git clone https://github.com/SciLifeLab/TIDDIT.git
To install TIDDIT:
cd TIDDIT
./INSTALL.sh
The install script will compile python and use pip to install the python dependencies TIDDIT is run via the TIDDIT.py script:
python TIDDIT.py --help
python TIDDIT.py --sv --help
python TIDDIT.py --cov --help
TIDDIT may be installed using bioconda:
conda install tiddit
Next, you may run TIDDIT like this:
tiddit --help
tiddit --sv
tiddit --cov
TIDDIT is also distributed with a Singularity environment (http://singularity.lbl.gov/index.html). Type the following command to download the container:
singularity pull --name TIDDIT.simg shub://J35P312/TIDDIT:latest
Type the following to run tiddit:
singularity exec TIDDIT.simg TIDDIT.py
You may also build it yourself (if you have sudo permisions)
sudo singularity build TIDDIT.simg Singularity
The singularity container will download and install the latest commit on the scilifelab branch of TIDDIT. The "versioned_singularity" folder contains singularity recipes for installing certain releases of TIDDIT. These releases may also be downloaded through singularity hub
singularity pull --name TIDDIT.simg shub://J35P312/TIDDIT:2.7.1
The main TIDDIT module, detects structural variant using discordant pairs, split reads and coverage information
python TIDDIT.py --sv [Options] --bam in.bam
TIDDIT support streaming of the bam file:
samtools view -buh in.bam | python TIDDIT.py --sv [Options] --bam /dev/stdin
Optionally, TIDDIT acccepts a reference fasta for GC correction:
python TIDDIT.py --sv [Options] --bam bam --ref reference.fasta
Reference is required for analysing cram files:
python TIDDIT.py --sv [Options] --bam in.cram --ref reference.fasta
Where bam is the input bam or cran file. And reference.fasta is the reference fasta used to align the sequencing data: TIDDIT will crash if the reference fasta is different from the one used to align the reads. The reads of the input bam file must be sorted on genome position.
The reference is required for analysing cram files.
NOTE: It is important that you use the TIDDIT.py wrapper for SV detection. The TIDDIT binary in the TIDDIT/bin folder does not perform any clustering, it simply extract SV signatures into a tab file.
TIDDIT may be fine-tuned by altering these optional parameters:
-o output prefix(default=output)
-i paired reads maximum allowed insert size. Pairs aligning
on the same chr at a distance higher than this are
considered candidates for SV (default= 99.9th percentile of insert size)
-d expected reads orientations, possible values "innie" (-> <-) or "outtie" (<- ->).
Default: major orientation within the dataset
-p Minimum number of supporting pairs in order to call a variation event (default 3)
-r Minimum number of supporting split reads to call a small variant (default 3)
-q Minimum mapping quality to consider an alignment (default= 5)
-Q Minimum regional mapping quality (default 20)
-n the ploidy of the organism,(default = 2)
-e clustering distance parameter, discordant pairs closer
than this distance are considered to belong to the same
variant(default = sqrt(insert-size*2)*12)
-l min-pts parameter (default=3),must be set >= 2
-s Number of reads to sample when computing library statistics(default=25000000)
-z minimum variant size (default=100), variants smaller than
this will not be printed ( z < 10 is not recomended)
--force_ploidy force the ploidy to be set to -n across the entire genome
(i.e skip coverage normalisation of chromosomes)
--no_cluster Run only the TIDDIT signal extraction
--debug rerun the tiddit clustering procedure
--n_mask exclude regions from coverage calculation if they contain more than this fraction of N (default = 0.5)
--ref reference fasta, used for GC correction and for reading cram
--p_ratio minimum discordant pair/normal pair ratio at the breakpoint junction(default=0.2)
--r_ratio minimum split read/coverage ratio at the breakpoint junction(default=0.1)
output:
TIDDIT SV module produces three output files, a vcf file containing SV calls, a tab file describing the coverage across the genome in bins of size 50 bp, and a tab file dscribing the estimated ploidy and coverage across each contig.
Useful settings:
In noisy datasets you may get too many small variants. If this is the case, then you may increase the -l parameter, or set the -i parameter to a high value (such as 2000) (on 10X linked read data, I usually set -l to 5).
Computes the coverge of different regions of the bam file
python TIDDIT.py --cov [Options] --bam bam
optional parameters:
-o - the prefix of the output files
-z - compute the coverage within bins of a specified size across the entire genome, default bin size is 500
-u - do not print per bin quality values
-w - generate a wig file instead of bed
--ref - reference sequence (fasta), required for reading cram file.
TIDDIT uses four different filters to detect low quality calls. The filter field of variants passing these tests are set to "PASS". If a variant fail any of these tests, the filter field is set to the filter failing that variant. These are the four filters empoyed by TIDDIT:
Expectedlinks
Less than <p_ratio> fraction of the spanning pairs or <r_ratio> fraction reads support the variant
FewLinks
The number of discordant pairs supporting the variant is too low compared to the number of discordant pairs within that genomic region.
Unexpectedcoverage
High coverage
Smear
The two windows that define the regions next to the breakpoints overlap.
Failed Variants may be removed using tools such as VCFtools or grep. Removing these variants greatly improves the precision of TIDDIT, but may reduce the sensitivity. It is adviced to remove filtered variants or prioritize the variants that have passed the quality checks. This command may be usedto filter the TIDDIT vcf:
grep -E "#|PASS" input.vcf > output.filtered.vcf
The scores in the quality column are calculated using non parametric sampling: 1000 points/genomic positions are sampled across each chromosome. And the number of read-pairs and reads spanning these points are counted. The variant support of each call is compared to these values, and the quality column is set to he lowest percentile higher than the (variant support*ploidy).
Note: SVs usually occur in repetetive regions, hence these scores are expected to be relatively low. A true variant may have a low score, and the score itself depends on the input data (mate-pair vs pe for instance).
The INFO field of the VCF contains the following entries:
SVTYPE
Type of structural variant(DEL,DUP,BND,INV,TDUP)
END
End position of an intra-chromosomal variant
LFA
The number of discordant pairs at the the first breakpoint of the variant
LFB
The number of discordant pairs at the the second breakpoint of the variant
LTE
The number of discordnat pairs that form the structural variant.
COVA
Coverage on window A
COVM
The coverage between A and B
COVB
Coverage on window B
CIPOS
start and stop positon of window A
CIEND
start and stop position of window B
QUALA
The average mapping quality of the reads in window A
QUALB
The average mapping quality of the reads in window B
The content of the INFO field can be used to filter out false positives and to gain more understanding of the structure of the variant. More info is found in the vcf file. Merging the vcf files
I usually merge vcf files using SVDB (https://github.com/J35P312)
svdb --merge --vcf file1.vcf file2.vcf --bnd_distance 500 --overlap 0.6 > merged.vcf
Merging of vcf files could be useful for tumor-normal analysis or for analysing a pedigree. But also to combine the output of multiple callers.
run the tumor sample using a lower ratio treshold (to allow for subclonal events, and to account for low purity)
python TIDDIT.py --sv --p_ratio 0.10 --bam tumor.bam -o tumor --ref reference.fasta grep -E "#|PASS" tumor.vcf > tumor.pass.vcf
run the normal sample
python TIDDIT.py --sv --bam normal.bam -o normal --ref reference.fasta grep -E "#|PASS" normal.vcf > normal.pass.vcf
merge files:
svdb --merge --vcf tumor.pass.vcf normal.pass.vcf --bnd_distance 500 --overlap 0.6 > Tumor_normal.vcf
The output vcf should be filtered further and annotated (using a local-frequency database for instance)
genes may be annotated using vep or snpeff. NIRVANA may be used for annotating CNVs, and SVDB may be used as a frequency database
Discordant pairs and split reads (supplementary alignments) are extracted and stored in the ".signals.tab" file. A discordant pair is any pair having a larger insert size than the -i paramater, or a pair where the reads map to different chromosomes. supplementary alignments and discordant pairs are only extracted if their mapping quality exceed the -q parameter.
The most recent version of TIDDIT uses an algorithm similar to DBSCAN: A cluster is formed if -l or more signals are located within the -e distance. Once a cluster is formed, more signals may be added if these signals are within the -e distance of -l signals within a cluster.
A cluster is rejected if it contains less than -r plus -p signals. If the cluster is rejected, it will not be printed to the vcf file.
If the cluster is not rejected, it will be printed to file, even if it fails any quality filter.
The sensitivity and precision may be controlled using the -q,r,p, and -l parameters.
All the tools distributed with this package are distributed under GNU General Public License version 3.0 (GPLv3).
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