Package: nanome-jax
NANOME pipeline (Nanopore long-read sequencing data consensus DNA methylation detection)Highlights of NANOME pipelineSeveral first highlights for NANOMEEnables users to processterabasescaleOxford Nanopore sequencing datasets.Provide aone command line/web-based UIfor end-to-end analyzing Nanopore sequencing methylation-callings.Supportvarious platformexecutions: local, HPC and CloudOS,without needs for tools' installation(NANOME support docker and singularity).First standardized whole genome-wide evaluation framework, considering per-read and per-site performance for singletons/non-singletons, genic and intergenic regions, CpG islands/shores/shelves, different CG densities regions and repetitive regions.Thefirst Nextflow based DNA methylation-calling pipeline for ONT data. Please check more articles about Nextflow based workflow technology from Nature Biotechnology:https://doi.org/10.1038/s41587-020-0439-xandhttps://doi.org/10.1038/nbt.3820.Allowadd new modules/toolsin simple config txt file, without need to touch the main pipeline codes, supporting rapid development and evaluation.Consensus of top performers by XGBoost model, allow NA values.Multi-modifications for 5mC and 5hmC.Haplotype-awared phasing and allele-specific methylation detection.Methodology of NANOME pipelineFig. 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.CI/CD featuresWe use  CI Automation Tools toenable the automated testing on every commit and on PRsto make sure that updates are not introducing bugs. Please check the automatic testing results onGithub.System RequirementsHardware requirementsNANOME pipeline can be easily configured with different RAM, CPU/GPU resources schema to parallelly run methylation-calling tools. For optimal usage, we recommend running NANOME pipeline on HPC or cloud computing platform, e.g., google cloud platform (GCP). The basic hardware requirements are below:GPU or CPU with 2+ cores.RAM: 7+ GB per cpu.Storage using HDD or SSD. Please ensure the storage before running the pipeline.Software requirementsNANOME pipeline uses Nextflow technology. Users only need to installNextflow(check the installation guide fromhttps://nf-co.re/usage/installation), and have one of below commonly used environment tool:CondaDockerSingularityWe provide conda, docker and singularity environments that depend on below well-known open-source packages for basecalling/methylation-calling/phasing on nanopore sequencing data:nanopolish>=0.13.2megalodon>=2.2.9deepsignal>=0.1.8ont-tombo>=1.5.1deepmod>=0.1.3METEORE>=1.0.0ont-pyguppy-client-lib>=4.2.2fast5mod>=1.0.5Clair3>=v0.1-r11Whatshap>=1.0NanomethPhase bam2bis>= 1.0GNU Parallel>=20170422Guppy software >= 4.2.2 fromONT (Oxford Nanopore Technologies) websiteInstallationUsers only need to installNextflow(https://nf-co.re/usage/installation). NANOME execution environment will be automatically configured with the support of conda, docker or singularity containers. Below is steps for installing Nextflow:# Install nextflow
conda install -c conda-forge -c bioconda nextflow
nextflow -vNANOME pipeline support running with various ways in different platforms:DockerSingularityCondaLocalexecution: running directly on default platformHPC clusters withSLURMsupportCloud computing platform, e.g., Google Cloud Platform(GCP) withgoogle-lifesciencessupportSimple usagePlease refer toUsageandSpecific UsageandNANOME optionsfor how to use NANOME pipeline. For running on CloudOS platform (e.g., google cloud), please checkUsage on CloudOS. We provide atutorial videofor running NANOME pipeline:When you have Nextflow software, NANOME pipeline can be directly executed without any other additional installation steps:# Run NANOME via docker
nextflow run LabShengLi/nanome\
    -profile test,docker

# Run NANOME via singularity
nextflow run LabShengLi/nanome\
    -profile test,singularity

# Run NANOME for human data
nextflow run LabShengLi/nanome\
    -profile test_human,[docker/singularity]Please note that above commands are integrated in ourCI/CD test cases. Our GitHub will automatically test and report results on every commit and PRs (https://github.com/LabShengLi/nanome/actions).We firstly proposed thestandardized whole genome-wide evaluation packages, checkstandardized evaluation tool usagefor more detail. We do not suggest evaluating on a portion of CpGs for performance comparisons.Pipeline reports for NANOMEBenchmarking reports on our HPC usingNextflowWe 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 byNextflowworkflows are:Trace file,ReportandTimeline.Our HPC hardware specifications are as follows:CPU: Intel(R) Xeon(R) Gold 6136 CPU @ 3.00GHzGPU: Tesla V100-SXM2-32GBRAM: 300 GBSlurm manager version: 19.05.5Timeline figure for benchmarking experiments are below:Pipeline DAGNANOME reportPlease checkNANOME reportfor the sample report by NANOME pipeline.Haplotype-aware consensus methylationsPlease checkphasing usage.Lifebit CloudOS reportWe now support running NANOME on cloud computing platform.Lifebitis a web-based cloud computing platform, and below is the running reports:Ecoli test report:https://cloudos.lifebit.ai/public/jobs/61c9fd328c574a01e8d31d2eHuman test report:https://cloudos.lifebit.ai/public/jobs/61c9fe618c574a01e8d31e99NA12878 chr22 report:https://cloudos.lifebit.ai/public/jobs/61c4f2ad8c574a01e8d0eee3NA12878 chr20 part5 report:https://cloudos.lifebit.ai/public/jobs/61c770748c574a01e8d2062bRevision HistoryFor release history, please visithere. For details, please gohere.ContactIf you have any questions/issues/bugs, please post them onGitHub. We will continuously update the GitHub to support famous methylation-calling tools for Oxford Nanopore sequencing.ReferenceDNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation.Genome Biology 22, 295 (2021).https://doi.org/10.1186/s13059-021-02510-z