cstag 1.1.1

cstag is a Python library tailored for manipulating and visualizing minimap2's cs tags.

[!NOTE] To add cs tags to SAM/BAM files, check out cstag-cli.

🌟 Features

• cstag.call(): Generate a cs tag
• cstag.shorten(): Convert a cs tag from its long to short format
• cstag.lengthen(): Convert a cs tag from its short to long format
• cstag.consensus(): Create a consensus cs tag from multiple cs tags
• cstag.mask(): Mask low-quality bases within a cs tag
• cstag.split(): Break down a cs tag into its constituent parts
• cstag.revcomp(): Convert a cs tag to its reverse complement
• cstag.to_sequence(): Reconstruct a reference subsequence from the alignment
• cstag.to_vcf(): Generate a VCF representation
• cstag.to_html(): Generate an HTML representation
• cstag.to_mutation_percentages(): Report and plot per-position mutation percentages

For comprehensive documentation, please visit our docs.

🛠 Installation

Using PyPI:

pip install cstag

Using Bioconda:

conda install -c bioconda cstag

💡 Usage

Generating cs tags

import cstag

cigar = "8M2D4M2I3N1M"
md = "2A5^AG7"
seq = "ACGTACGTACGTACG"

print(cstag.call(cigar, md, seq))
# :2*ag:5-ag:4+ac~nn3nn:1

print(cstag.call(cigar, md, seq, long=True))
# =AC*ag=TACGT-ag=ACGT+ac~nn3nn=G

Shortening or Lengthening cs tags

import cstag

# Convert a cs tag from long to short
cs_tag = "=ACGT*ag=CGT"

print(cstag.shorten(cs_tag))
# :4*ag:3


# Convert a cs tag from short to long
cs_tag = ":4*ag:3"
cigar = "8M"
seq = "ACGTACGT"

print(cstag.lengthen(cs_tag, cigar, seq))
# =ACGT*ag=CGT

Creating a Consensus

import cstag

cs_tags = ["=ACGT", "=AC*gt=T", "=C*gt=T", "=C*gt=T", "=ACT+ccc=T"]
positions = [1, 1, 2, 2, 1]

print(cstag.consensus(cs_tags, positions))
# =AC*gt=T

To require a minimum level of agreement and report consensus quality, set min_agreement to a value between 0 and 1. The returned dictionary retains the candidate consensus even when it does not pass the requirement.

import cstag

cs_tags = ["=ACGT", "=ACGT", "=ACGT", "=AC*gt=T"]
positions = [1, 1, 1, 1]

print(cstag.consensus(cs_tags, positions, min_agreement=0.75))
# {
#     'consensus': '=ACGT',
#     'passed': True,
#     'agreement': 0.75,
#     'max_edit_distance': 1,
# }

agreement is the lowest modal-token fraction over all covered reference positions. max_edit_distance is the largest, among all reads, of the Levenshtein distance to its nearest overlapping read. Use return_result=True to obtain the same report without applying an agreement threshold. Calls that set neither option continue to return only the consensus string.

Masking Low-Quality Bases

import cstag

cs_tag = "=ACGT*ac+gg-cc=T"
cigar = "5M2I2D1M"
qual = "AA!!!!AA"
phred_threshold = 10
print(cstag.mask(cs_tag, cigar, qual, phred_threshold))
# =ACNN*an+ng-cc=T

Splitting a cs tag

import cstag

cs_tag = "=ACGT*ac+gg-cc=T"
print(cstag.split(cs_tag))
# ['=ACGT', '*ac', '+gg', '-cc', '=T']

Reverse Complement of a cs tag

import cstag

cs_tag = "=ACGT*ac+gg-cc=T"
print(cstag.revcomp(cs_tag))
# =A-gg+cc*tg=ACGT

Reconstructing the Reference Subsequence

import cstag
cs_tag = "=AC*gt=T-gg=C+tt=A"
print(cstag.to_sequence(cs_tag))
# ACTTCTTA

Generating a VCF Report

import cstag
cs_tag = "=AC*gt=T-gg=C+tt=A"
chrom = "chr1"
pos = 1
print(cstag.to_vcf(cs_tag, chrom, pos))
"""
##fileformat=VCFv4.2
#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO
chr1	3	.	G	T	.	.	.
chr1	4	.	TGG	T	.	.	.
chr1	5	.	C	CTT	.	.	.
"""

The multiple cs tags enable reporting of the variant allele frequency (VAF).

import cstag
cs_tags = ["=ACGT", "=AC*gt=T", "=C*gt=T", "=ACGT", "=AC*gt=T"]
chroms = ["chr1", "chr1", "chr1", "chr2", "chr2"]
positions = [2, 2, 3, 10, 100]
print(cstag.to_vcf(cs_tags, chroms, positions))
"""
##fileformat=VCFv4.2
##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth">
##INFO=<ID=RD,Number=1,Type=Integer,Description="Depth of Ref allele">
##INFO=<ID=AD,Number=1,Type=Integer,Description="Depth of Alt allele">
##INFO=<ID=VAF,Number=1,Type=Float,Description="Variant allele frequency (AD/DP)">
#CHROM	POS	ID	REF	ALT	QUAL	FILTER	INFO
chr1	4	.	G	T	.	.	DP=3;RD=1;AD=2;VAF=0.667
chr2	102	.	G	T	.	.	DP=1;RD=0;AD=1;VAF=1.0
"""

Generating an HTML Report

import cstag
from pathlib import Path

cs_tag = "=AC+ggg=T-acgt*at~gt10ag=GNNN"
description = "Example"

cs_tag_html = cstag.to_html(cs_tag, description)
Path("report.html").write_text(cs_tag_html)
# Output "report.html"

You can visualize mutations indicated by the cs tag using the generated report.html file as shown below:

Plotting Mutation Percentages

from pathlib import Path

import cstag

cs_tags = ["=ACGT", "=AC*gt=T", "=C*gt=T", "=ACGT", "=AC*gt=T"]
regions = [
    {"name": "crRNA", "start": 1, "end": 2, "color": "lightblue"},
    {"name": "index", "start": 3, "end": 4, "color": "lightgreen"},
]

report = cstag.to_mutation_percentages(
    cs_tags,
    Path("mutation_percentages.pdf"),
    regions=regions,
)
print(report)

The function accepts short- or long-form cs tags and returns one dictionary per 1-based relative reference position. Percentages are reported for all mutations, insertions, deletions, and substitutions. A PDF is saved as editable vector graphics; use a .png output path for a raster image.

📣 Feedback and Support

For questions, bug reports, or other forms of feedback, we'd love to hear from you!
Please use GitHub Issues for all reporting purposes.

Please refer to CONTRIBUTING for how to contribute and how to verify your contributions.

🤝 Code of Conduct

Please note that this project is released with a Contributor Code of Conduct.
By participating in this project you agree to abide by its terms.

📄 Citation

• Kuno, A., (2024). cstag and cstag-cli: tools for manipulating and visualizing cs tags. Journal of Open Source Software, 9(93), 6066, https://doi.org/10.21105/joss.06066