Spectre is a long read copy number variation (CNV) caller. Spectre is designed to detect large CNVs (>100kb) in a couple of minutes depending on your hardware.
To calculate CNVs Spectre uses primarily the coverage (Read depth) data. However, it can also use SNV data to detect loss of heterozygosity (LoH) regions. Additionally, Spectre can use the breakpoint (SNF) data from Sniffles to improve the CNV calling. However, it has to be converted to the SNFJ format using snf2json.
The CNV output of Spectre is stored in three files, VCF, BED and .SPC which can be used in the population mode.
Furthermore, Spectre offers a population mode, which can be used to search for CNV support in multiple samples. Compared to other tools, Spectre searches not only in the final CNVs but also in CNV candidates which did not qualify for the final output of Spectre.
Tested on Python version: 3.11 and 3.10
Install Spectre with Pip:
pip install spectre-cnvGet the latest changes by building Spectre from source on your own and install it locally in your conda environment.
pip install build
git clone https://github.com/fritzsedlazeck/Spectre.git
cd ./Spectre
python3 -m build
pip install dist/spectre_cnv-<VERSION>.tar.gz # replace <VERSION> with e.g. 0.2.0Setup a conda environment for Spectre (copy and paste the following commands)
conda create -n spectre python=3.10 pysam==0.22.0 numpy==1.24.3 pandas==2.0.1 matplotlib==3.7.1 scipy==1.10.1 -y
conda activate spectreAlternatively, you can use pip for installing the packages stored in the requirements txt
conda create -n spectre python=3.10 pip -y
conda activate spectre
pip install -r requirements.txtor install everything manually (check for package version in the requirements.txt file)
| Program | Conda |
|---|---|
| python3 | conda install python=3.10 |
| pysam | conda install -c bioconda pysam=0.22.0 |
| pandas | conda install -c anaconda pandas==2.0.1 |
| numpy | conda install -c anaconda numpy==1.24.3 |
| scipy | conda install -c anaconda scipy==1.10.1 |
| matplotlib | conda install -c anaconda matplotlib==3.7.1 |
Spectre need as input:
Prerequisites: Extract the coverage data from a BAM using Mosdepth. Example command:
mosdepth -t 8 -x -b 1000 -Q 20 "${out_path}/${sample_id}" "${bam_path}"IMPORTANT: We recommend to run Mosdepth with a bin size of 1kb and a mapping quality of at least 20 (-Q 20), as Spectre is optimized for that.
- The region coverage file (mosdepth)
- SampleID e.g.
- Output directory
- Reference genome (can be bgzip compressed)
Optional
- MDR file (if not already generated, Spectre will do that for you. You can also use the MDR file for every sample which has been aligned to the same reference genome)
- VCF file containing SNV
- SNF data from Sniffles (if parsed through snf2json)
MDR files hold information about N regions in the reference genome and restrict Spectre's use of data from those regions. We are providing sample MDR files for the reference genomes GRCh37 and GRCh38.
Spectre will generate an MDR file for you if not provided, which can take some time. Thus, we highly recommend generating an MDR file for your reference genome before running Spectre on multiple samples aligned to the same reference.
Providing an MDR file will save you a substantial amount of time, as Spectre will not have to calculate the N regions for every sample.
The generation of the MDR file can be done with either the RemoveNs or CNVCaller command. In the latter case, the MDR (metadata. MDR) file will be saved in the sample's output directory.
Running the RemoveNs will generate an MDR file under the name in the directory you specified.
spectre RemoveNs \
--reference reference.fasta.gz \
--output-dir output_directory_path/ \
--output-file name_of_metadata_file.mdr
The blacklist is a supplementary file to the MDR file. It contains regions which should be ignored by Spectre. Those regions are based on gap data from USCS. During testing we found that the gap data is not totally sufficient masking high frequency coverage regions such as telomeric and centromeric regions. Thus we have extended the especially those problematic regions in the blacklist file. (grch37_blacklist_spectre_refined.bed and grch38_blacklist_spectre.bed)
spectre CNVCaller \
--coverage mosdepth/sampleid/mosdepth.regions.bed.gz \
--sample-id sampleid \
--output-dir sampleid_output_directory_path/ \
--reference reference.fasta.gzRun Spectre with multiple samples:
INFO: This will start the population mode automatically. All provided settings will be applied to all samples.
NOTE: If population flag is not set, Spectre will run in single sample mode. Thus, calculating only CNVs for the first sample.
spectre.py CNVCaller \
--coverage mosdepth/sampleid-1/mosdepth.regions.bed.gz mosdepth/sampleid-2/mosdepth.regions.bed.gz \
--sample-id sampleid-1 sampleid-2 \
--output-dir sampleid_output_directory_path/ \
--reference reference.fasta.gz
--populationRun Spectre in population mode with two or more samples:
INFO: Spectre produces an intermediate file (.spc) which contains all calculated CNVs from a given samples. They are located in the output folder of given sample.
spectre population \
--candidates /path/to/sample1.spc /path/to/sample2.spc \
--sample-id output_name \
--output-dir sampleid_output_directory_path/vcf_utils <command> [<args>]
Spectre:
CNVCaller:
[Required]
--coverage Path to the coverage file from Mosdepth output. Expects the following files:
<prefix>.regions.bed.gz
<prefix>.regions.bed.gz.csi
Can be one or more paths. However, providing multiple samples is only intended to
work with the --population flag. Example:
--coverage /path/md1.regions.gz /path/md2.regions.gz
--sample-id Sample name/ID. Can be one or more ID. However, providing multiple sample ids is only
intended to work with the --population flag. Example:
--sample-id id1 id2
--output-dir Output directory
--reference Reference sequence used for mapping (for N removal)
[Optional, if missing it will be created]
--metadata Metadata file for Ns removal (this will speed up Spectre massively if provided)
--n-size Required amount of consecutive Ns to be considered an NRegion
in the reference sequence (Default = 5)
[Optional]
--blacklist Blacklist in bed format for sites that will be ignored (Default = "")
--only-chr Comma separated list of chromosomes to use (e.g. chr1,chr2,chr3)
--ploidy Set the ploidy for the analysis, useful for sex chromosomes (Default = 2)
--ploidy-chr Comma separated list of key:value-pairs for individual chromosome ploidy control
(e.g. chrX:2,chrY:1) If chromosome is not specified, the default ploidy will be used.
--snfj Breakpoints from from Sniffle which has been converted from the SNF to the SNFJ format.
SNFJ files can be generated using the program snf2json.
--min-cnv-len Minimum length of CNV (Default 100kb)
--snv VCF file containing the SNV for the same sample CNV want to be called
--cancer Set this flag if the sample is cancer (Default = False) This will disable some safety
checks, when determining the DEL and DUP thresholds.
[Optional, Coverage]
--sample-coverage-overwrite Overwrites the calculated sample coverage, which is used to normalize
the coverage. e.g. a value of 30 equals to 30X coverage.
--disable-max-coverage Disables the maximum coverage check. This will allow to call CNVs
[Optional, LoH (requires --snv)]
--loh-min-snv-perkb Minimum number of SNVs per kilobase for an LoH region (default=5)
--loh-min-snv-total Minimum number of SNVs total for an LoH region (default=100)
--loh-min-region-size Minimum size of a region for a LoH region (default=100000)
--population Runs the population mode on all provided samples. It will apply all the provided
configurations as well as the default population mode values to all samples.
--threads Amount of threads (This will boost performance if multiple samples are provided)
RemoveNs:
[Required]
--reference Reference genome used for mapping
--output-dir Output dir
--output-file Output file for results
[Optional]
--n-size Required amount of consecutive Ns to be considered an NRegion
in the reference sequence (Default = 5)
--save-only Will only save the metadata file and not show the results on screen (Default = False)
Population:
[Required]
--candidates At least 2 .spc sample files which should be used in the population mode.
(e.g. sample1.spc sample2.spc)
--sample-id The name of the sample-id will be added accordingly at the output.
(e.g. population_mode_<sample-id>.vcf.gz)
--output-dir Path of the output directory
[Optional]
--reference Reference sequence
--reciprocal-overlap Minimum reciprocal overlap for supporting CNVs [0.0 - 1.0] (Default = 0.8)
--disable-quality-filter Disables the quality filter for the population mode. Spectre will also
search for supporting CNVs in the .SPC files, which have not been reported
as final CNVs in the VCF and BED file.
Version:
version Shows current version/build