CIGAR_SV

Detect TE insertion polymorphisms from long reads using CIGAR

1) Map long reads to the reference using minimap2

minimap2 -t 8 --cs -cx map-ont reference.fa reads.fasta/fastq > output.paf

2) Check CIGAR info using the script

python cigar_sv_fasta.py -i output.paf -r reference.fa -l reads.fasta/fastq ```


usage: python cigar_sv.py -i <input.paf> -r <reference.fa> -l <reads.fastq/fasta>  (option)

A tool to detect large structural variants (SVs) using long read sequencing

optional arguments:
  -h, --help            show this help message and exit
  -i <input.paf>        read alignment paf file with --cs -cx tag
  -r <reference.fa>     reference genome fasta (uncompressed or bgzipped)
  -l <reads.fastq/fasta>
                        query long read fastq/fasta file (uncompressed or bgzipped)
  -o PATH               output directory [./CIGAR_output]

filter options:
  -m INT                minimum length of a structural variant for detection [50]
  -ma INT               minimum length of the alignment block
  -mq INT               minimum length of the query read

Detected SV in csv format.

Col	Type	Description
1	string	query sequence name
2	int	query length
2	int	query start
3	int	query end
4	int	direction
5	string	reference sequence name
6	int	reference length
7	int	sv start position on reference
8	int	sv end position on reference
9	int	sv type (insertion or deletion) (ins/del)
10	int	mapping quality
11	string	primary alignment
12	int	blast identity
13	string	corresponding sv fasta sequence

Contact

CIGAR_SV

Copyright © 2021 Panpan Zhang (njaupanpan@gmail.com)

Any question, concern, or bug report about the program should be posted as an Issue on GitHub. Before posting, please check previous issues (both Open and Closed) to see if your issue has been addressed already. Also, please follow these good GitHub practices.