cuteFC
Getting Start
__________ ___________
| ____ | | _____ |
_ | | |__| | / | |
_______ _ _ ___| |___ ______ | | | | |_|
| ___ | | | | | |___ ___| / ____ \ | |_______ | |
| | |_| | | | | | | / /____\ \ | _______| | |
| | | | | | | | | _______| | | | | _
| | _ | | | | | | _ | | _ | | | | | |
| |___| | | |___| | | |_| | \ \____/ | | | | \______| |
|_______| |_______| |_____| \______/ |__| |__________|
Installation
$ git clone https://github.com/Meltpinkg/cuteFC.git && cd cuteFC/ && python setup.py install
Introduction
Accurate genotype assignment for SVs remains challenging, especially in large-scale joint calling. We develop cuteFC to achieve accurate and efficient regenotyping of SVs through a force-calling approach. Benchmarking results demonstrated that cuteFC outperforms state-of-the-art methods with 2%~5% higher F1 scores. SV joint-calling within the cohort revealed that cuteFC constructs the higher-quality genomic atlas with minimal computational resources. These results prove cuteFC to be a scalable and robust approach suitable for clinical applications, population studies, and related fields.
For more detailed implementation of SV benchmarks, we show an example here.
Dependence
1. python3
2. pysam
3. Biopython
4. cigar
5. numpy
6. pyvcf
Usage
cuteFC <sorted.bam> <reference.fa> <output.vcf> <work_dir> -Ivcf <target.vcf>
Suggestions
> For PacBio CLR data:
--max_cluster_bias_INS 500
--diff_ratio_merging_INS 0.5
--max_cluster_bias_DEL 1000
--diff_ratio_merging_DEL 0.5
> For PacBio CCS(HIFI) data:
--max_cluster_bias_INS 1000
--diff_ratio_merging_INS 0.9
--max_cluster_bias_DEL 1000
--diff_ratio_merging_DEL 0.5
> For ONT data:
--max_cluster_bias_INS 1000
--diff_ratio_merging_INS 0.5
--max_cluster_bias_DEL 1000
--diff_ratio_merging_DEL 0.5
| Parameter | Description | Default |
|---|---|---|
| --threads | Number of threads to use. | 16 |
| --batches | Batch of genome segmentation interval. | 10,000,000 |
| --sample | Sample name/id | NULL |
| --retain_work_dir | Enable to retain temporary folder and files. | False |
| --write_old_sigs | Enable to output temporary sig files. | False |
| --report_readid | Enable to report supporting read ids for each SV. | False |
| --max_split_parts | Maximum number of split segments a read may be aligned before it is ignored. All split segments are considered when using -1. (Recommand -1 when applying assembly-based alignment.) | 7 |
| --min_mapq | Minimum mapping quality value of alignment to be taken into account. | 10 |
| --min_read_len | Ignores reads that only report alignments with not longer than bp. | 500 |
| --merge_del_threshold | Maximum distance of deletion signals to be merged. | 0 |
| --merge_ins_threshold | Maximum distance of insertion signals to be merged. | 100 |
| --min_support | Minimum number of reads that support a SV to be reported. | 10 |
| --min_size | Minimum length of SV to be reported. | 30 |
| --max_size | Maximum size of SV to be reported. Full length SVs are reported when using -1. | 100000 |
| --genotype | Enable to generate genotypes. | False |
| --gt_round | Maximum round of iteration for alignments searching if perform genotyping. | 500 |
| --read_range | The interval range for counting reads distribution. | 1000 |
| --detect_large_ins | Enable the detection of large insertions. | False |
| --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. | 0.3 |
| --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. | 0.5 |
| --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 |
| --remain_reads_ratio | The ratio of reads remained in cluster to generate the breakpoint. Set lower to get more precise breakpoint when the alignment data have high quality but recommand over 0.5. | 1 |
| -include_bed | Optional given bed file. Only detect SVs in regions in the BED file. | NULL |
Citation
Jiang, T., Cao, S., Liu, Y. et al. cuteFC: regenotyping structural variants through an accurate and efficient force-calling method. Genome Biol 26, 166 (2025). https://doi.org/10.1186/s13059-025-03642-2
Contact
For advising, bug reporting, and requiring help, please post on Github Issue or contact tjiang@hit.edu.cn.
1 maintainer
Metadata
1 maintainer
Release files for cuteFC 1.0.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| cuteFC-1.0.2.tar.gz | 30.4 kB | Without provenance | Details for cuteFC-1.0.2.tar.gz |
Release files / cuteFC-1.0.2.tar.gz
| Download URL | cuteFC-1.0.2.tar.gz |
|---|---|
| Size | 30.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
082c49d0a05675ddce3852eef00cbda20a52041ed8e2755ed98608b1a8fb0ca4
|
|
BLAKE2b-256 checksum How to use checksums |
108885e03c8dadcd3dc13a2f480e1933f5cddf09122976a8998710763f288fec
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.10.10
|
Security for cuteFC 1.0.2
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
