cnnLSV

cnnLSV: detecting structural variants by encoding long-read alignment information and convolutional neural network

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Installation

Clone

git clone https://github.com/mhuidong/cnnLSV.git

Requirements

python3, cv2, numpy, torch, torchvision, pysam, cigar

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Usage

Detecting SVs

python cnnLSV.py <sorted.bam> <initial.vcf> <filtered.vcf> --dataset <real/sim> --model <simmodel.pt/realmodel.pt>

[OPTIONAL] Removing Redundant information

CnnLSV outputs the callset merged of several callers. This means that one SV may be detected by several callers. You can use the foolowing command to obtain unique result.

python cnnLSV_rmrd.py <multi.vcf> <unique.vcf>

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Datasets

HG00512, HG00513, HG00514, HG00731, HG00732, HG00733, NA19238, NA19239 and NA19240 datasets can be downloaded from:

http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/hgsv_sv_discovery/working/20160905_smithm_pacbio_aligns/

HG002 CLR and HG002 CCS datasets can be downloaded from:

https://ftp.ncbi.nih.gov/giab/ftp/data/AshkenazimTrio/

Contact

huidongma@st.gxu.edu.cn