Giraffe-View 0.1.0.13

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.

# Testing version
# samtools 1.17
# minimap2 2.17-r941
# bedtools 2.30.0

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 the following commands.

estimate

giraffe estimate --input read_list.txt --cpu 4 --plot 

read_list.txt - a table with your sample ID, sequencing platforms (ONT/Pacbio for DNA, ONT_RNA for direct sequencing RNA), and path of your sequencing reads (FASTQ format).

# A demo of read_list.txt
# Note: please use the SPACE(" ") to gap them.
R1 ONT /home/user/test/reads/S1.fastq
R2 Pacbio /home/user/test/reads/S2.fastq
R3 ONT /home/user/test/reads/S3.fastq

observe

giraffe observe --input read_list.txt --ref genome.fa --cpu 4 --plot 

read_list.txt - a table the same as the above one.

gcbias

giraffe gcbias --input bam_list.txt --ref genome.fa --plot 

bam_list.txt - a table with your sample ID, sequencing platforms, and path of your alignment files (sam/bam format).

# A demo of bam_list.txt
# Note: please use the SPACE(" ") to gap them.
# If you have used the observe function to process your data, the resulting bam files can be used as the input.
R1 ONT /home/user/test/Giraffe_Results/2_Observed_quality/S1.bam
R2 Pacbio /home/user/test/Giraffe_Results/2_Observed_quality/S2.bam
R3 ONT /home/user/test/Giraffe_Results/2_Observed_quality/S3.bam

modbin

giraffe modbin --input methylation_list.txt --pos promoter.csv --cpu 4 --plot

bam_list.txt - a table with your sample ID, sequencing platforms, and path of your methylation profiling files (bed format).

# A demo of methylation_list.txt
# Note: please use the SPACE(" ") to gap them.
R1 ONT test/reads/5mC_S1.txt
R2 Pacbio test/reads/5mC_S2.txt
R3 ONT test/reads/5mC_S3.txt

# A demo of your methylation file (e.g. 5mC_S1.txt).
# Please use the tab ("\t") to gap the column.
# chromosome start end methylation_proportion
chr1	81	83	0.8
chr1	21314	21315	0.3
chr1	32421	32422	0.85

# A demo of promoter.csv
#chromosome, start, end, geneID
chr1,12027,17027,ENSDARG00000099104
chr1,6822,11822,ENSDARG00000102407

# Note: there is no Header for all tables.

Example

Here, we provide demo datasets for testing the giraffe. The following commands can help to download them.

# The input file list
wget https://figshare.com/ndownloader/files/44967445 -O fastq.list
wget https://figshare.com/ndownloader/files/44967442 -O bed.list
wget https://figshare.com/ndownloader/files/44967499 -O bam.list

# The reference and ONT reads (R10.4.1 and R9.4.1) of E.coli
wget https://figshare.com/ndownloader/files/44967436 -O Read.tar.gz

# The 5mC methylation files of zebrafish blood and kidney samples.
# The position file is the gene promoter region in chromosome 1. 
wget https://figshare.com/ndownloader/files/44967427 -O Methylation.tar.gz

tar -xzvf Read.tar.gz
tar -xzvf Methylation.tar.gz
rm Read.tar.gz Methylation.tar.gz

Please run the following commands to start data analysis!

giraffe estimate --input fastq.list --plot --cpu 4
giraffe observe --input fastq.list --plot --cpu 4 --ref Read/ecoli_chrom.fa
giraffe gcbias --input bam.list --plot --ref Read/ecoli_chrom.fa
giraffe modbin --input bed.list --cpu 4 --plot --pos Methylation/zf_promoter.db

Results

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

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

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 D).

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

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

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 G).

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

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 - genomic regional methylation proportion (Fig I).

Additional scripts

Here, we provide some scripts with user to understanding their data better.

homopolymer_count

# A script that counts the position and type of homopolymer in  reference
# if you download the example data, you can run following cammond.
homopolymer_count --ref Read/ecoli_chrom.fa > Read/ecoli_chrom.homopolymer.txt

# Output
# chromosome start end base_type feature
ecoli_chrom	2	4	T	3T
ecoli_chrom	24	26	T	3T
ecoli_chrom	32	34	C	3C
ecoli_chrom	85	87	A	3A
ecoli_chrom	92	94	T	3T
ecoli_chrom	139	141	A	3A
ecoli_chrom	145	147	A	3A

renormalization_sequencing_bias

# A script provides a solution for renormalizing the sequencing depth based on the given GC content scale.
# Here, we seclected the bins within 30% to 60% GC content to renomalization in our demo data. 
renormalization_sequencing_bias -i Giraffe_Results/3_GC_bias/R941_relationship_raw.txt -l 30 -r 60 -o renorm.txt

# Output
GC_content	Depth	Number	Group	Normalized_depth
30	10.913	8	R941	0.9739095164943401
31	11.4585	8	R941	1.0225916058600197
32	12.424866666666668	15	R941	1.1088331245157133
33	12.4052	10	R941	1.107078010997488
34	10.8758125	16	R941	0.9705907901913406
35	11.2746875	16	R941	1.006187615848051
36	12.220117647058824	17	R941	1.090560695422983
37	11.645590909090908	22	R941	1.0392881711320083
38	10.52416	25	R941	0.9392082449472257

replot_sequencing_bias

# A script to replot the figure of relatiob between renormalized sequencing depth and GC content.
replot_sequencing_bias -i renorm.txt -o renorm

# Output
# A figure named renorm.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)