Giraffe-View 0.0.3

A small tool help assess and visualize the accuracy of a sequencing dataset, specifically for Oxford Nanopore Technologies (ONT) long-read sequencing.

Giraffe_View

Giraffe_View is designed to help assess and visualize the accuracy of a sequencing dataset, specifically for Oxford Nanopore Technologies (ONT) long-read sequencing including DNA and RNA data. There are four main functions to validate the read quality.

• observe calculates the observed read accuracy, mismatches porportion, and homopolymer identification.
• estimate calculates the estimated read accuracy, which is equal to Quality Score.
• GC_bias compares the relationship between GC content and read coverage.
• modi perform statistics on the distribution of modification based on the bed file.

Install

To use this software, you will need to install additional dependencies including samtools, minimap2, seqkit, pysam, numpy, and pandas. You can install these dependencies using the following command.

# for data processing
pip install rpy2==3.0 pysam numpy pandas
conda install -c bioconda -c conda-forge samtools minimap2 seqkit bedtools -y

# for figure plotting
conda install -c R ggplot2 patchwork -y

General Usage

Giraffe View is run simply with fllowing commands:

python Giraffe_View.py --help

usage: Giraffe_view [-h] {observe,modi,GC_bias,estimate} ...

A tool to help you assess quality of your ONT data.

positional arguments:
  {observe,modi,GC_bias,estimate}
    observe             Observed quality in accuracy, mismatch, and homopolymer
    modi                Average modification proportion of regions
    GC_bias             Relationship between GC content and depth
    estimate            Estimated read accuracy

optional arguments:
  -h, --help            show this help message and exit

The available sub-commands are:

observe

python Giraffe_View.py observe --help

usage: Giraffe_view observe [-h] --input <fastq> --ref <reference> [--cpu <number>]

optional arguments:
  -h, --help         show this help message and exit
  --input <fastq>    input reads
  --ref <reference>  input reference
  --cpu <number>     number of cpu (default:10)

• fastq - the raw fastq data, some filter steps will be conducted including short read ( < 200 bp) and low quality read ( < 7 ) removal.
• reference - the reference file in fasta format.
• cpu - the number of CPUs will be used during processing.

estimate

python Giraffe_View.py estimate --help

usage: Giraffe_view estimate [-h] --input <fastq> [--cpu <number>]

optional arguments:
  -h, --help       show this help message and exit
  --input <fastq>  input reads
  --cpu <number>   number of cpu (default:10)

GC_bias

python Giraffe_View.py GC_bias --help

usage: Giraffe_view GC_bias [-h] --ref <reference> --input <sam/bam> [--binsize]

optional arguments:
  -h, --help         show this help message and exit
  --ref <reference>  input reference file
  --input <sam/bam>  input bam/sam file
  --binsize          input bin size (default:1000)

• reference - the reference file in fasta format.
• sam / bam - the result of mapping in sam/bam file. If you have used the observe function to process your data, the resulting tmp.sort.bam file can be used as the input.
• binsize - the length of bin. A bin is the smallest unit to count the read coverage and GC content.

modi

python Giraffe_View.py modi --help

usage: Giraffe_view modi [-h] --input <bed> --ref <reference> [--cpu <number>]

optional arguments:
  -h, --help         show this help message and exit
  --input <bed>      input bed file
  --ref <reference>  input reference
  --cpu <number>     number of cpu (default:10)

• bed - a bed file with four columns (three columns for position, one for methylation proportion). Please use the tab ("\t") to gap the column instead of the space (" ").

  #chrom	start	end	value
  chr1	81	83	0.8
  chr1	21314	21315	0.3
  chr1	32421	32422	0.85
  

• reference - a csv file with target regions.

  chr1,0,100000,1_0_100000
  chr1,100000,200000,1_100000_200000
  

Workflow

graph TD
raw_data --> |Quality control| clean_data
raw_data --> |Basecall| modification_file
modification_file --> modification_distribution
clean_data --> Estimated_accuracy
clean_data --> |Reference| aligned_file
aligned_file --> Homopolymer_analysis
aligned_file --> GC_bias 
aligned_file --> Observed_accuracy

Developing

• A example to show how to run
• polish the result figures
• run the homopolymer identification with multi-processes