Giraffe is specially designed to provide a comprehensive assessment of the accuracy of long-read sequencing datasets obtained from both the Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) platforms, offering four distinct functions.
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 by Conda
# install on the current environment
conda install -c raymond_liu giraffe_view -y
# install on a new environment
conda create -n giraffe -c raymond_liu giraffe_view -y
Installation by PyPI
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
# install on the currently environment
conda install -c bioconda -c conda-forge samtools minimap2 bedtools -y
# install on a new environment
conda create -n giraffe -c bioconda -c conda-forge python==3.9 samtools==1.17 minimap2==2.17 bedtools==2.30.0 -y && conda activate giraffe
To install this tool, please use the following command.
pip install Giraffe-View
Giraffe can be run with a one-button command or by executing individual functions.
# Running function of "estimate", "observe", and "gcbias" with FASTQ files
giraffe --read <read table> --ref <reference> --cpu <number of processes or threads>
# Running function of "estimate", "observe", and "gcbias" with unaligned SAM/BAM files
giraffe --read <unaligned SAM/BAM table> --ref <reference> --cpu <number of processes or threads>
# Example for input table (sample_ID data_type file_path)
sample_A ONT /home/user/data/S1.fastq
sample_B ONT /home/user/data/S2.fastq
sample_C ONT /home/user/data/S3.fastq
...
Here the data_type can be ONT DNA reads (ONT), ONT directly sequencing reads (ONT_RNA), and Pacbio DNA reads (Pacbio).
# For the FASTQ reads
giraffe estimate --read <read table>
# For the unaligned SAM/BAM files
giraffe estimate --unaligned <unaligned SAM/BAM table>
# For FASTQ reads
giraffe observe --read <read table> --ref <reference>
# For unaligned SAM/BAM files
giraffe observe --unaligned <unaligned SAM/BAM table> --ref <reference>
# For aligned SAM/BAM files
giraffe observe --aligned <aligned SAM/BAM table>
Note: If you are going to use aligned SAM/BAM files as input, please remove the secondary alignment (--secondary=no) and add the MD tag (--MD) before mapping by adding these two highlighted parameters.
giraffe gcbias --ref <reference> --aligned <aligned SAM/BAM table>
giraffe modbin --methyl <methylation table> --region <target region>
# Example for methylation file (Chrom Start End Value):
contig_A 132 133 0.92
contig_A 255 256 0.27
contig_A 954 955 0.52
...
Here, we provide demo datasets for testing the Giraffe. The following commands can help to download them and run the demo.
giraffe_run_demo
The demo datasets included three E. coli datasets including a 4.2 MB reference, 79 MB R10.4.1 reads, and 121 MB R9.4.1 reads. For the methylation files, two files of zebrafish blood (23 MB)and kidney (19 KB) are included. This demo takes about 7 minutes and 20 seconds with a maximum memory of 391 MB. This running includes the one-command pattern and four individual functions testing.
The one-command pattern will generate a summary in HTML format. If the scale of the X/Y-axis is not reasonable, the script of giraffe_plot
can be used to replot the figure.
For more details about the usage of Giraffe and results profiling, please refer to the document.
Liu, X., Shao, Y., Guo, Z., Ni, Y., Sun, X., Leung, A. Y. H., & Li, R. (2024). Giraffe: A tool for comprehensive processing and visualization of multiple long-read sequencing data. Computational and Structural Biotechnology Journal, 23, 3241-3246. https://doi.org/10.1016/j.csbj.2024.08.003