cuteSV 1.0.3

Long read based human genomic structural variation detection with cuteSV

cuteSV

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Getting Start

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Installation

$ pip install cuteSV
or
$ conda install -c bioconda cutesv
or 
$ git clone https://github.com/tjiangHIT/cuteSV.git && cd cuteSV/ && pip install .

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Introduction

Long-read sequencing enables the comprehensive discovery of structural variations (SVs). However, it is still non-trivial to achieve high sensitivity and performance simultaneously due to the complex SV signatures implied by the noisy long reads. Therefore, we propose cuteSV, a sensitive, fast and scalable long read-based SV detection approach. cuteSV uses tailored methods to collect the signatures of various types of SVs and it employs a clustering-and-refinement method to analyze the signatures to implement sensitive SV detection. Benchmark on real PacBio and ONT datasets demonstrate that cuteSV has better yields and scalability than state-of-the-art tools.

The benchmark results of cuteSV on the HG002 human sample are below:

BTW, we used Truvari to calculate the recall, precision, and f-measure. For more detailed implementation of SV benchmarks, we show an example here.

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Dependence

1. python3
2. pysam
3. Biopython
4. cigar
5. numpy

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Usage

cuteSV <sorted.bam> <output.vcf> <work_dir>

Suggestions

> For PacBio CLR data:
	--max_cluster_bias_INS		100
	--diff_ratio_merging_INS	0.2
	--diff_ratio_filtering_INS	0.6
	--diff_ratio_filtering_DEL	0.7
> For PacBio CCS(HIFI) data:
	--max_cluster_bias_INS		200
	--diff_ratio_merging_INS	0.65
	--diff_ratio_filtering_INS	0.65
	--diff_ratio_filtering_DEL	0.35

Parameter	Description	Default
--threads	Number of threads to use.	16
--batches	Batch of genome segmentation interval.	10,000,000
--sample	Sample name/id	NULL
--max_split_parts	Maximum number of split segments a read may be aligned before it is ignored.	7
--min_mapq	Minimum mapping quality value of alignment to be taken into account.	20
--min_read_len	Ignores reads that only report alignments with not longer then bp.	500
--min_support	Minimum number of reads that support a SV to be reported.	10
--min_length	Minimum length of SV to be reported.	30
--max_cluster_bias_INS	Maximum distance to cluster read together for insertion.	100
--diff_ratio_merging_INS	Do not merge breakpoints with basepair identity more than the ratio of default for insertion.	0.2
--diff_ratio_filtering_INS	Filter breakpoints with basepair identity less than the ratio of default for insertion.	0.6
--max_cluster_bias_DEL	Maximum distance to cluster read together for deletion.	200
--diff_ratio_merging_DEL	Do not merge breakpoints with basepair identity more than the ratio of default for deletion.	0.3
--diff_ratio_filtering_DEL	Filter breakpoints with basepair identity less than the ratio of default for deletion.	0.7
--max_cluster_bias_INV	Maximum distance to cluster read together for inversion.	500
--max_cluster_bias_DUP	Maximum distance to cluster read together for duplication.	500
--max_cluster_bias_TRA	Maximum distance to cluster read together for translocation.	50
--diff_ratio_filtering_TRA	Filter breakpoints with basepair identity less than the ratio of default for translocation.	0.6

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Datasets generated from cuteSV

We provided the SV callsets of the HG002 human sample produced by cuteSV form three different long-read sequencing platforms (i.e. PacBio CLR, PacBio CCS, and ONT PromethION).

You can download them at:

Please cite the manuscript of cuteSV before using these callsets.

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Changelog

cuteSV (v1.0.3):
1.Refine the genotyping model.
2.Adjust the threshold value of heterozygosis alleles.

cuteSV (v1.0.2):
1.Improve the genotyping performance and enable it to be default option.
2.Make the description of parameters better.
3.Modify the header description of vcf file.
4.Add two new indicators, i.e., BREAKPOINT_STD and SVLEN_STD, to further characterise deletion and insertion.
5.Remove a few redundant functions which will reduce code readability.

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Citation

Long Read based Human Genomic Structural Variation Detection with cuteSV. Tao Jiang, et al. bioRxiv 780700; doi: https://doi.org/10.1101/780700

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Contact

For advising, bug reporting and requiring help, please post on Github Issue or contact [e-mail].