nanomonsv is a software for detecting somatic structural variations from paired (tumor and matched control) cancer genome sequence data. nanomonsv is presented in the following paper. When you use nanomonsv or any resource of this repository, please kindly site this paper.
Precise characterization of somatic complex structural variations from tumor/control paired long-read sequencing data with nanomonsv, Shiraishi et al., Nucleic Acids Research, 2023, [link].
The current version of nanomonsv includes two detection modules, Canonical SV module, and Single breakend SV module. Canonical SV module can identify somatic SVs that can be captured by short-read technologies with higher precision and recall than existing methods. Furthermore, Single breakend SV module enables the detection of complex SVs that can only be identified by long-reads, such as SVs involving highly-repetitive centromeric sequences, and LINE1- and virus-mediated rearrangements.
Please see the wiki page for Single breakend SV module.
htslib, mafft, racon(optional from ver. 0.3.0. However, we recommend to use this option. Add --use_racon option when you perfrom get command.)
Python (tested with >=3.6), pysam, numpy, parasail
bwa, minimap2, bedtools, RepeatMasker
nanomonsv uses, tabix, bgzip (which are part of HTSlib projects) and mafft inside the program,
assuming those are installed, and the paths are already added to the running environment.
Since version 0.3.0, we support racon for the step where generating consensus sequence and get single-base resolution breakpoints. racon may become the default instead of mafft in the future.
bwa, minimap2, bedtools and RepeatMasker are required to be installed and these paths are added to the running environment.
nanomonsv accept the BAM file aligned by minimap2.
Starting with version 0.5.0, the use of the control panel is supported.
In this software, supporting reads for SVs are collected for multiple samples other than the target sample,
and such reads are removed as common noise (or those derived from common SVs) in the get stage.
This strategy is expected to exclude many false positives as well as improve computational cost.
We have prepared the command to create control panels from the user's own sequencing data. In addition, for users who do not have sufficient sequencing data that can serve as a control panel (or just do not have time for processing), we prepared a control panel that has been created using the 30 Nanopore sequencing data from the Human Pangenome Reference Consortium, which is available at zenodo.
This control panel is made by aligning 30~40 Nanopore (basecalled by Guppy ver. 4 and 6) and PacBio HiFi sequencing data to the GRCh38/CHM13 reference genome (obtained from here) with minimap2 version 2.24. For the choice of the control panel, we recommend that you use one that is as close as possible in platform and basecall quality. However, based on our experience, a noisier control panel tends to be more versatile. Therefore, when unsure, we advise to use the Nanopore control panel from Guppy version 4. When you use these control panels and publish, do not forget to credit to HPRC!
- Install all the prerequisite software and install nanomonsv.
pip install nanomonsv (--user)
You can also install nanomonsv via conda (bioconda channel).
conda create -n nanomonsv -c conda-forge -c bioconda nanomonsv
Occasionally the conda releases lag behind the source code and PyPI releases.
- Prepare the reference genome for the test data (here, we show the path to Genomic Data Commons reference genome).
wget https://api.gdc.cancer.gov/data/254f697d-310d-4d7d-a27b-27fbf767a834 -O GRCh38.d1.vd1.fa.tar.gz
tar xvf GRCh38.d1.vd1.fa.tar.gz
- Parse the putative structural variation supporting reads of the test data.
nanomonsv parse tests/resource/bam/test_tumor.bam output/test_tumor
nanomonsv parse tests/resource/bam/test_ctrl.bam output/test_ctrl
- Get the final result.
nanomonsv get output/test_tumor tests/resource/bam/test_tumor.bam GRCh38.d1.vd1.fa --control_prefix output/test_ctrl --control_bam tests/resource/bam/test_ctrl.bam
You will see the result file named test_tumor.nanomonsv.result.txt.
The Oxford Nanopore Sequencing data used in the bioRxiv paper is available through the public sequence repository service (BioProject ID: PRJDB10898):
The results of nanomonsv for the above data are available here. When you perform nanomonsv to the above data and have experienced errors, please report to us. Also, please kindly cite the NAR paper when you use these data.
See tutorial wiki page for an example workflow on analyzing COLO829 sample.
This step parses all the supporting reads of putative somatic SVs.
nanomonsv parse [-h] [--reference_fasta reference.fa] [--debug]
[--split_alignment_check_margin SPLIT_ALIGNMENT_CHECK_MARGIN]
[--minimum_breakpoint_ambiguity MINIMUM_BREAKPOINT_AMBIGUITY]
alignment_file output_prefix
- alignment_file: Path to input indexed BAM or CRAM file
- output_prefix: Output file prefix
See the help (nanomonsv parse -h) for other options.
From v0.7.0, nanomonsv can accept CRAM format files. For CRAM files, we recommend to add the PATH to the reference genome file by --reference_fasta.
After successful completion, you will find supporting reads stratified by deletions, insertions, and rearrangements: ({output_prefix}.deletion.sorted.bed.gz, {output_prefix}.insertion.sorted.bed.gz, {output_prefix}.rearrangement.sorted.bedpe.gz, and {output_prefix}.bp_info.sorted.bed.gz and their indexes (.tbi files).
This step gets the SV result from the parsed supporting reads data obtained above.
nanomonsv get [-h] [--control_prefix CONTROL_PREFIX]
[--control_bam CONTROL_BAM]
[--min_tumor_variant_read_num MIN_TUMOR_VARIANT_READ_NUM]
[--min_tumor_VAF MIN_TUMOR_VAF]
[--max_control_variant_read_num MAX_CONTROL_VARIANT_READ_NUM]
[--max_control_VAF MAX_CONTROL_VAF]
[--cluster_margin_size CLUSTER_MARGIN_SIZE]
[--median_mapQ_thres MEDIAN_MAPQ_THRES]
[--max_overhang_size_thres MAX_OVERHANG_SIZE_THRES]
[--var_read_min_mapq VAR_READ_MIN_MAPQ]
[--qv10] [--qv15] [--qv20] [--qv25] [--use_racon]
[--single_bnd] [--threads THREADS] [--processes PROCESSES]
[--sort_option SORT_OPTION] [--max_memory_minimap2] [--debug]
tumor_prefix tumor_bam reference.fa
- tumor_prefix: Prefix to the tumor data set in the parse step
- tumor_bam: Path to input indexed BAM file
- reference.fa: Path to reference genome used for the alignment
This software can generate a list of SVs without specifying the matched control. But we have not tested the performance of the approach just using tumor sample, and strongly recommend using the matched control data. Even when only tumor sample is available, we still recommend using dummy control sample (collected from other person's tissue).
- control_prefix: Prefix to the matched control data set in the parse step
- control_bam: Path to the matched control BAM file
When you use the control panel (recommended!), use the following argument.
- control_panel_prefix: Prefix of non-matched control panel data processed in merge_control step.
You can also use --process to use multi-processing mode. Currently, we do not recommend using --thread option.
From v0.7.0, we prepared preset parameter options:
- --qv10: Parameter preset for sequencing data with a base quality of around 10. Recommended for ONT data called by Guppy before version 5
- --qv15: Parameter preset for sequencing data with a base quality of around 15. Recommended for ONT data called by Guppy version 5, 6.
- --qv20: Parameter preset for sequencing data with a base quality of around 20. Recommended for ONT data with Q20+ chemistry.
- --qv25: Parameter preset for sequencing data with a base quality above 25. Recommended for PacBio Hifi data.
After successful execution, you will be able to find the result file names as {tumor_prefix}.nanomonsv.result.txt.
See the help (nanomonsv get -h) for other options.
When you want to change the engine of Smith-Waterman algorithm to SSW Library, specify --use_ssw_lib option,
though we do not generally recommend this.
Also, we basically recommend using --use_racon option. This will slightly improve the identification of single-base resolution breakpoint,
and polishing of inserted sequences.
For detection of single breakend SVs, please use --single_bnd option as well as --use_racon.
Please see wiki page.
Also, we have prepared the script (misc/post_fileter.py) for filtering the result. Please see the wiki page. For output files of the version 0.4.0 and later, some filtering has already been performed (see the wiki page). However, we strongly recommed to perform additional processing; removing indels within simple repeat regions (see the wiki page).
From the version 0.4.0, we will also provide the VCF format result files.
- Chr_1: Chromosome for the 1st breakpoint
- Pos_1: Coordinate for the 1st breakpoint
- Dir_1: Direction of the 1st breakpoint
- Chr_2: Chromosome for the 2nd breakpoint
- Pos_2: Coordinate for the 2nd breakpoint
- Dir_2: Direction of the 2nd breakpoint
- Inserted_Seq: Inserted nucleotides within the breakpoints
- SV_ID: Identifier of SVs (originally comming from cluster of SV supporting reads)
- Checked_Read_Num_Tumor: Total number of reads in the tumor used for the validation alignment step
- Supporting_Read_Num_Tumor: Total number of variant reads in the tumor determined in the validation alignment step
- Checked_Read_Num_Control: Total number of reads in the normal used for the validation alignment step
- Supporting_Read_Num_Control: Total number of variant reads in the matched control determined in the validation alignment step
- Is_Filter: Filter status. PASS if this SV has passed all the filters
This command merges non-matched control panel supporting reads obtained by performing parse command.
nanomonsv merge_control [-h] prefix_list_file output_prefix
- prefix_list_file: The list of output_prefix generated at the above
parsestage. - output_prefix: Prefix to the merged control supporting reads.
This command classifies the long insertions into several mobile element insertions (still in alpha version). This does not yet support VCF format, but we will do so in the near future.
nanomonsv insert_classify [-h] [--grc] [--genome_id {hg19,hg38,mm10}]
[--debug]
sv_list_file output_file reference.fa
- sv_list_file: SV list file obtained in the get step
- output_file: Path to the output file for this command
- reference.fa: Path to the reference genome
- genome_id: The type of reference genome. Choose from hg19 and hg38 (default is hg38). This is used for selecting LINE1 database.
- Insert_Type: Type of insertion (Solo_L1, Partnered_L1, Orphan_L1, Alu, SVA, PSD)
- Is_Inversion: Type of inverted form for Solo LINE1 insertion (Simple, Inverted, Other)
- L1_Raito: The match rate with LINE1 sequences for the inserted sequences
- Alu_Ratio: The match rate with Alu sequences for the inserted sequences
- SVA_Ratio: The match rate with SVA sequences for the inserted sequences
- RMSK_Info: Summary information of RepeatMasker
- Alignment_Info: Alignment information to the human genome
- Inserted_Pos: Inserted position (appears only when the inserted sequence is aligned near the other insertion and implicated to be the tandem duplication or nested LINE1 transduction).
- Is_PolyA_T: Extracted poly-A or poly-T sequences
- Target_Site_Duplication: Nucleotides of target site duplications
- L1_Source_Info: Inferred source site of LINE1 transduction
- PSD_Gene: Processed pseudogene name
- PSD_Overlap_Ratio: The match rate with the pseudogene
- PDS_Exon_Num: The number of pseudogene exons matched with the inserted sequence
This command, which is part of the procedures of get command,
performs validation of the candidate SVs by alignment of tumor and matched control BAM files.
This may be helpful for the evaluation of SV tools of the short-read platform
when pairs of short-read and long-read sequencing data are available.
This is still in alpha version.
nanomonsv validate [-h] [--control_bam CONTROL_BAM]
[--var_read_min_mapq VAR_READ_MIN_MAPQ] [--debug]
sv_list_file tumor_bam output reference.fa
- sv_list_file: SV candidate list file (similar format with the result file by
getcommand. But only from Chr_1 to Inserted_Seq columns are necessary. - output_file: Path to the output file
- reference.fa: Path to the reference genome
