nanome-jax 1.3.5

NANOME (Nanopore methylation) pipeline developed by Li Lab at The Jackson Laboratory

DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation

--NANOME(Nanopore methylation) pipeline of DNA methylation calling tools for Oxford Nanopore sequencing

Methodology of NANOME pipeline

Background: Nanopore long-read sequencing technology greatly expands the capacity of long-range, single-molecule DNA-modification detection. A growing number of analytical tools have been developed to detect DNA methylation from nanopore sequencing reads. Here, we assess the performance of different methylation calling tools to provide a systematic evaluation to guide researchers performing human epigenome-wide studies.

Fig. 1A. Survey of methylation calling tools . Timeline of publication and technological developments of Oxford Nanopore Technologies (ONT) methylation calling tools to detect DNA cytosine modifications.

Fig. 1B. Workflow for 5-methylcytosine (5mC) detection for nanopore sequencing.

Results: We compared seven analytic tools for detecting DNA modifications from nanopore long-read sequencing data. We evaluated the CpG methylation-detection accuracy, CpG site coverage, and running time using nanopore sequencing data across different genomic contexts, using natural human DNA. Furthermore, we provide an online DNA methylation database (https://nanome.jax.org) with which to display the DNA methylation levels detected by nanopore sequencing and bisulfite sequencing data across different genomic contexts.

Conclusions: Our study is the first benchmark of computational methods for detection of mammalian whole-genome DNA-modifications in nanopore sequencing. We provide a broad foundation for cross-platform standardization, and an evaluation of analytical tools designed for genome-scale modified-base detection using nanopore sequencing.

System Requirements

Hardware requirements

NANOME is based on Nextflow pipeline framework, and start with raw fast5 nanopore sequencing input data with a reference genome. The pipeline can be configured with different RAM, number of processors, CPU/GPU resources schema to parallel run methylation-calling tools. For optimal usage, we recommend using NANOME pipeline on HPC:

• GPU or CPU with 2+ cores.
• RAM: 7+ GB per cpu.
• Storage using HDD or SSD. Please ensure your storage before running the pipeline.

Software requirements

NANOME pipeline uses Nextflow technology. Users only need to install Nextflow and one of below commonly used environment tool:

• conda
• docker
• singularity

We provide conda, docker and singularity environments which depend on below well-known open-source packages for methylation calling on nanopore sequencing data:

nanopolish >=0.13.2
megalodon >=2.2.9
deepsignal >=0.1.8
ont-tombo >=1.5.1
deepmod >=0.1.3
METEORE >=1.0.0
ont-pyguppy-client-lib >=4.2.2
fast5mod >=1.0.5

Guppy software >= 4.2.2 from ONT (Oxford Nanopore Technologies) website

Installation

Users only need to install Nextflow, see installation document. NANOME execution environment will be automatically configured with the support of conda, docker or singularity containers.

NANOME pipeline support running with various ways in different platforms:

• Conda
  1. Create conda enviroment: conda env create -f environment.yml
• Docker
  1. Docker container name from Docker Hub: liuyangzzu/nanome:latest, you can also build docker image by docker build -t liuyangzzu/nanome:latest .
• Singularity
  1. Pull image from Docker Hub: singularity pull docker://liuyangzzu/nanome:latest
• HPC clusters with SLURM support
• Google Cloud platform with google-lifesciences support

Usage

NANOME pipeline can be directly executed without any other additional installation steps:

# Run NANOME using docker
nextflow run TheJacksonLaboratory/nanome\
    -profile ci,docker

# Run NANOME using singularity
nextflow run TheJacksonLaboratory/nanome\
    -profile ci,singularity

Please refer to Usage for how to use NANOME pipeline. For running on CloudOS platform (e.g., google cloud), please check Usage on CloudOS.

If you prefer using our evaluation packages, please check evaluation script usage for more detail.

Pipeline reports for NANOME

Benchmarking reports on our HPC using Nextflow

We constructed a set of benchmarking datasets that contain reads from 800 to about 7,200 reads for NA19240, and monitored job running timeline and resource usage on our HPC, reports generated by Nextflow workflows are: Trace file, Report and Timeline.

Our HPC hardware specifications are as follows:

• CPU: Intel(R) Xeon(R) Gold 6136 CPU @ 3.00GHz
• GPU: Tesla V100-SXM2-32GB
• RAM: 300 GB
• Slurm manager version: 19.05.5

Timeline figure for benchmarking experiments are below:

Pipeline DAG

NANOME report

Please check NANOME report for the sample report by NANOME pipeline.

Revision History

For release history, please visit here. For details, please go here.

Contact

If you have any questions/issues/bugs, please post them on GitHub. We will continuously update the Github to support famous methylation-calling tools for Oxford Nanopore sequencing.

Reference

Detailed results can be found in our publication. Please cite our article below if you are interested in our GitHub repository:

DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation. Genome Biology 22, 295 (2021). https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02510-z