cuteSV
Getting Start
__________ __ __
| ____ | | | | |
_ | | |__| | | | |
_______ _ _ ___| |___ ______ | | | | | |
| ___ | | | | | |___ ___| / ____ \ | |_______ | | | |
| | |_| | | | | | | / /____\ \ |_______ | | | | |
| | | | | | | | | _______| __ | | \ \ / /
| | _ | | | | | | _ | | _ | | | | \ \ / /
| |___| | | |___| | | |_| | \ \____/ | | |____| | \ \_/ /
|_______| |_______| |_____| \______/ |__________| \_____/
Installation
$ pip install cuteSV
or
$ conda install -c bioconda cutesv
or
$ git clone https://github.com/tjiangHIT/cuteSV.git && cd cuteSV/ && pip install .
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.
Dependence
1. python3
2. pysam
3. Biopython
4. cigar
5. numpy
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. | 3 |
| --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 |
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).
Please cite the manuscript of cuteSV before using these callsets.
Changelog
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.
Citation
Long Read based Human Genomic Structural Variation Detection with cuteSV. Tao Jiang, et al. bioRxiv 780700; doi: https://doi.org/10.1101/780700
Contact
For advising, bug reporting and requiring help, please post on Github Issue or contact tjiang@hit.edu.cn.
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
File details
Details for the file cuteSV-1.0.2.1.tar.gz.
File metadata
- Download URL: cuteSV-1.0.2.1.tar.gz
- Upload date:
- Size: 25.0 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.6.0.post20191101 requests-toolbelt/0.9.1 tqdm/4.38.0 CPython/3.6.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
8aed976e42ebf8334642fab88645380393ed65f88bf166e7b43af0cbf9790920
|
|
| MD5 |
0687814add5e63f2cd918b9e928200f1
|
|
| BLAKE2b-256 |
59bb07fe981e2fccb9458d33eb02448db3e44fef9d82b40af97ae12dc875bcef
|