Giraffe-View 0.1.0.0

Giraffe_View is specially designed to provide a comprehensive assessment of the accuracy of long-read sequencing datasets obtained from both the PacBio and Nanopore platforms.

Giraffe View

Giraffe_View is specially designed to provide a comprehensive assessment of the accuracy of long-read sequencing datasets obtained from both the PacBio and Nanopore platforms.

• estimate Calculation of estimated read accuracy (Q score), length, and GC content.

• observe Calculation of observed read accuracy, mismatch proportion, and homopolymer identification (e.g. AAAA).

• gcbias Calculation of the relationship between GC content and sequencing depth.

• modbin Calculation of the distribution of modification (e.g. 5mC or 6mA methylation) at the regional level.

Installation

Before using this tool, you need to install additional dependencies for read processing, including the samtools，minimap2, and bedtools. The following commands can help you install both the software package and its dependencies.

conda install -c bioconda -c conda-forge samtools minimap2 bedtools -y
pip install Giraffe-View

If you are unfamiliar with the process of installing conda, you can refer to the official conda documentation for detailed instructions. Please follow this link for guidance on installing conda.

General Usage

The giraffe can be run using following commands:

giraffe -h

usage: giraffe [-h] {estimate,observe,gcbias,modbin} ...

A tool to help you assess the quality of long-read sequencing data.

positional arguments:
  {estimate,observe,gcbias,modbin}
    estimate            Estimated accuracy, length, and GC content.
    observe             Observed accuracy, mismatch proportion, and homopolymer identification.
    gcbias              Relationship between GC content and sequencing depth.
    modbin              Average modification proportion at regional level.

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

The available sub-commands are:

estimate

giraffe estimate -h

usage: giraffe estimate [-h] --input <file list> [--cpu <number>] [--plot]

options:
  -h, --help           show this help message and exit
  --input <file list>  input the file list
  --cpu <number>       number of cpu (default:10)
  --plot               results visualization

file list - a table with your sample ID, sequencing platforms (ONT/Pacbio), and path of your datasets (FASTQ format), please using the SPACE(" ") to gap them.

cpu - number of CPUs will be used during processing.

# A example of file list
R1 ONT test/reads/S1.fastq
R2 Pacbio test/reads/S2.fastq
R3 ONT test/reads/S3.fastq

observe

giraffe observe -h

usage: giraffe observe [-h] --input <file list> --ref <reference> [--cpu <number>] [--plot]

optional arguments:
  -h, --help           show this help message and exit
  --input <file list>  input the file list
  --ref <reference>    input reference
  --cpu <number>       number of CPU (default:10)
  --plot               results visualization

• file list - a table same with the above one.
• reference - reference file in FASTA format.
• cpu - number of CPUs will be used during processing.

gcbias

giraffe gcbias -h

usage: giraffe gcbias [-h] --ref <reference> --input <list> [--binsize] [--plot]

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

• file list - a table with your sample ID, sequencing platforms (ONT/Pacbio), and path of your alignment files (sam/bam format), please using the SPACE(" ") to gap them.

  P.S. If you have used the observe function to process your data, the resulting bam file can be used as the input.

• reference - the reference file.

• binsize - the length of BIN. The BIN is the unit to count the read coverage and GC content (e.g. 3000 or 5000).

# A example of file list
R1 ONT test/reads/S1.bam
R2 Pacbio test/reads/S2.bam
R3 ONT test/reads/S3.bam

modbin

giraffe modbin -h

usage: giraffe modbin [-h] --input <list> --bed <reference> [--cpu <number>] [--plot]

optional arguments:
  -h, --help         show this help message and exit
  --input <list>     input list of modificated file
  --bed <reference>  input position file with CSV format
  --cpu <number>     number of CPU (default:10)
  --plot             results visualization

• list - a table with your sample ID, sequencing platforms (ONT/Pacbio), and path of your files with methylation information, please using the SPACE(" ") to gap them.

  # A example of file list
  R1 ONT test/reads/5mC_S1.txt
  R2 Pacbio test/reads/5mC_S2.txt
  R3 ONT test/reads/5mC_S3.txt
  
  # A example of your methylation file with four columns (three columns for position, one for methylation value).  
  # 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 (chromosome, start, end, ID)

  chr1,0,100000,bin1
  chr1,100000,200000,bin2
  

Results

if you run the demo data in the example, you will obtain a fold named Giraffe_Results with following structure.

Giraffe_Results/
├── 1_Estimated_quality
│   ├── 1_Read_accuracy.pdf
│   ├── 2_Read_length.pdf
│   ├── 3_Read_GC_content.pdf
│   └── Estimated_information.txt
├── 2_Observed_quality
│   ├── 1_Observed_read_accuracy.pdf
│   ├── 2_Observed_mismatch_proportion.pdf
│   ├── 3_Homoploymer_summary.pdf
│   ├── Homoploymer_summary.txt
│   ├── Observed_information.txt
│   ├── R1041.bam
│   ├── R1041.bam.bai
│   ├── R1041_homopolymer_detail.txt
│   ├── R1041_homopolymer_in_reference.txt
│   ├── R941.bam
│   ├── R941.bam.bai
│   ├── R941_homopolymer_detail.txt
│   └── R941_homopolymer_in_reference.txt
├── 3_GC_bias
│   ├── 1_Bin_distribution.pdf
│   ├── 2_Relationship_normalization.pdf
│   ├── Bin_distribution.txt
│   ├── R1041_relationship_raw.txt
│   ├── R941_relationship_raw.txt
│   └── Relationship_normalization.txt
└── 4_Regional_modification
    ├── 1_Regional_modification.pdf
    ├── Blood.bed
    └── Kidney.bed

1_Estimated_quality

• Estimated_information.txt - File with read ID, estimated read accuracy, estimate read error, Q Score, GC content, read length and sample ID.

  ReadID	Accuracy	Error	Q_value	Length	GC_content	Group
  @9154e0a0	0.935	0.065	11.857	316	0.503	R1041
  @fa8f2a80	0.948	0.052	12.877	9621	0.498	R1041

• 1_Read_accuracy.pdf - Distribution of estimated read accuracy (Fig A).

• 2_Read_length.pdf - Distribution of read length (Fig B).

• 3_Read_GC_content.pdf - Distribution of read GC content (Fig C).

2_Observed_quality

• Homoploymer_summary.txt - Accuracy of identification for each homopolymer type (only the length over 3 base pair was calculated, e.g. AAAA and TTTTT).

  Base	Accuracy	Group
  T	0.909	R1041
  G	0.857	R1041
  A	0.907	R1041
  C	0.859	R1041

• Observed_information.txt - Summary of observed accuracy includes the read ID, insertion length, deletion length, substitution length, matched length, observed identification rate, observed accuracy, and sample ID for each read.

  ID	Ins	Del	Sub	Mat	Iden	Acc	Group
  70fbffe6	3	1	1	354	0.9972	0.9861	R1041
  96a5c10b	3	11	2	342	0.9942	0.9553	R1041

• XXX_homopolymer_detail.txt - Detailed information for homopolymer identification includes the chromosome, start position, end position, homopolymer length, homopolymer type , matched base number, deleted base number, inserted base number, substituted base number, read ID, and sample ID (Read level).

  Chrom	Start	End	length	type	Matched base	Deleted base	Inserted base	Substituted base	ReadID	SampleID
  ecoli_chrom	3083	3086	4	T	4	0	0	0	c322bcea	R941
  ecoli_chrom	3382	3386	5	A	5	0	0	0	c322bcea	R941

• XXX_homopolymer_in_reference.txt - Summarized information includes the position of homopolymer in reference, the number of perfectly matched read, the total number of mapped read, the homopolymer feature, and sample ID (Reference level).

  pos	num_of_mat	depth	type	Group
  ecoli_chrom_3083_3086	1	1	4T	R941
  ecoli_chrom_3382_3386	1	1	5A	R941

• XXX.bam - BAM file generated by aligning the data against the reference genome.

• XXX.bam.bai - Index for BAM file.

• 1_Observed_read_accuracy.pdf - Distribution of observed read accuracy (Fig A).

• 2_Observed_mismatch_proportion.pdf - Distribution of mismatch proportion (Fig B).

• 3_Homoploymer_summary.pdf - Accuracy of homopolymer identification (Fig C).

3_GC_bias

• Bin_distribution.txt - BINs number within each GC content. (GC content, and Number of BINs)

• XXXX_relationship_raw.txt - Read coverage for total GC content (GC content, average depth among the BINs, number of BINs, and sample ID).

• Relationship_normalization - Normalized read coverage for selected GC content (GC content, average depth, Number of BINs, sample ID, and normalized depth).

  GC_content	Depth	Number	Group	Normalized_depth
  40	7.832	55	R1041	1.066
  41	7.655	59	R1041	1.067

• 1_Bin_distribution.pdf - Visualization of BINs number within each GC content (Fig A).

• 2_Relationship_normalization.pdf - Relationship between normalized depth and GC content (Fig B).

4_Regional_modification

• XXX.bed - Average modification proportion for each BIN (BIN name, average value, and sample ID).

  BIN name	5mC proportion	Group
  ENSDARG00000102097	0.6	Blood
  ENSDARG00000099319	0.830	Blood

• 1_Regional_modification.pdf

Workflow

graph TD
	A(raw signal) -.-> |Basecall| B(FASTA)
	A(raw signal) -.-> |Basecall| C(modificated file)
	C(modificated files) --> |modbin| D(Modification distribution)
	B(sequence reads) --> |estimate|e(Estimated table)
	e(Estimated table) --> f(Estimated accuracy)
	e(Estimated table) --> l(Read length)
	e(Estimated table) --> x(Read GC content)
	
	B(sequence reads) --> |observe|g(Aligned files)
	
	g(Aligned files) --> |observe|h(Homopolymer identification)
 	g(Aligned files) --> |observe|i(Observed accuracy)
 	g(Aligned files) --> |observe|c(Mismatch proportion)
	g(Aligned files) --> |gcbias|j(GC bias comparison)