Skip to main content

An extension of breseq to determine copy number variations from coverage data

Project description

CNery

breseq copy number variation extension predicts copy numbers using sequencing coverage output from breseq. The predictions account for biases in coverage due to sequencing methodologies such as GC content or prokaryotic physiological states during DNA isolation that may bias sequencing coverage towards origin of genome replication.

Installation:

Recommended: Create conda python environment.

mamba env create -f environment.yml
mamba activate CNery

Install breseq-ext-cnv

pip install git+https://github.com/barricklab/breseq-ext-cnv.git

Run:

Run CNery in the breseq folder that contains folders 01_.., 02_.., data and output:

CNery [-o <output folder location>] [-w <window>] [-s <step size>] [-f <fragment length>]

If running this script from in a different location, specify input .bam file, refrence.fasta file and output paths:

CNery [-i <input .bam file>] [-ref <refrence.fasta file location>][-o <output folder location>] [-w <window>] [-s <step size>] [-f <fragment length>]

Run examples:

Calculate coverage with a window size of 500 and an sliding increment size of 250 to summarize coverage across the genome. Average fragment length of the sequencing platform is 300bp

CNery -o <output folder> -w 500 -s 250 -f 300

Analyze coverage across the whole genome but output copy number prediction and coverage plots of a specific genomic segment

CNery -o <output folder> --region 3497890-3955678 -w 1000 -s 500

Correct bias in sequencing coverage due to either one of the two factors GC or OTR (origin-terminus of replication) or no correction:

CNery -o <output folder> -w 500 -s 250 --bias otr #only correct for bias in coverage due to replication

CNery -o <output folder> -w 500 -s 250 --bias gc #only correct for bias in coverage due to GC content of the sequence

CNery -o <output folder> -w 500 -s 250 --bias none #no bias correction to be applied before CN prediction

CNery run options

$CNery -h

usage: CNery [-h] [-i I] [-ref REF] [-reg REG] [-o O] [-w W] [-s S] [-ori ORI] [-ter TER] [-f F] [-e E]
             [--bias {all,none,gc,otr}]

CNery is python package extension to breseq that analyzes the sequencing coverage across the genome to predict copy number variation (CNV)

options:
  -h, --help            show this help message and exit
  -i, --input I         input .bam file from breseq output
  -ref REF              select the reference file used for breseq
  -reg REG              select the region of the genome to evaluate
  -o, --output O        output file prefix. Defaults to the CNV_out folder.
  -w, --window W        Define window length to parse through the genome and calculate coverage and GC statistics.
  -s, --step-size S     Define step size (<= window size) for each progression of the window across the genome sequence. Set step-size=window-size if non-overlapping windows.
  -ori, --origin ORI    Genomic coordinate for origin of replication.
  -ter, --terminus TER  Genomic coordinate for terminus of replication.
  -f, --frag_size F     Average fragment size of the sequencing reads.
  -e, --error-rate E    Approximate error rate in sequencing read coverage/refrence alignment.
  --bias {all,none,gc,otr}
                        Select specific bias correction (only OTR or only GC) to run before CN prediction.

Run this script in the breseq output folder that contains 'data' and 'output' folders. 

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

cnery-1.0.0.tar.gz (21.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

cnery-1.0.0-py3-none-any.whl (29.4 kB view details)

Uploaded Python 3

File details

Details for the file cnery-1.0.0.tar.gz.

File metadata

  • Download URL: cnery-1.0.0.tar.gz
  • Upload date:
  • Size: 21.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.8

File hashes

Hashes for cnery-1.0.0.tar.gz
Algorithm Hash digest
SHA256 bbaa48050255bc7dca206a40dffe6f79b72b18858a569dccc4cb27810574093d
MD5 f095e702d33a72847eb2a76d9b26db86
BLAKE2b-256 848f5d03702b3eac6a132018e70a7c3589fbb144ef82c0688de3d8e624033433

See more details on using hashes here.

File details

Details for the file cnery-1.0.0-py3-none-any.whl.

File metadata

  • Download URL: cnery-1.0.0-py3-none-any.whl
  • Upload date:
  • Size: 29.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.8

File hashes

Hashes for cnery-1.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 c17d753536b5c6aa4227c037660ba5bae0afadf7c5e871c07f694a61e59ef9bc
MD5 1b0971cd0553a1d772f21e4afc4d6a2d
BLAKE2b-256 64112dd7c1d166cf84651515cf875d009449a795cc93cb7c039345e13480be28

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page