%global _empty_manifest_terminate_build 0 Name: python-nanome-jax Version: 2.0.11 Release: 1 Summary: NANOME (Nanopore methylation) pipeline developed by Li Lab at The Jackson Laboratory License: MIT License URL: https://github.com/LabShengLi/nanome Source0: https://mirrors.aliyun.com/pypi/web/packages/32/a7/6ea7fa19724d670be02a8b72be6310d1d98a9f394e825bbd8395e94dbb87/nanome-jax-2.0.11.tar.gz BuildArch: noarch Requires: python3-biopython Requires: python3-pybedtools Requires: python3-pandas Requires: python3-seaborn Requires: python3-scipy Requires: python3-numpy Requires: python3-statsmodels Requires: python3-scikit-learn Requires: python3-matplotlib Requires: python3-jinja2 Requires: python3-openpyxl Requires: python3-h5py Requires: python3-tqdm Requires: python3-joblib Requires: python3-psutil Requires: python3-xgboost Requires: python3-pytabix Requires: python3-pysam Requires: python3-ont-fast5-api %description # NANOME pipeline (Nanopore long-read sequencing data consensus DNA methylation detection) [![demo_gif.gif](https://github.com/LabShengLi/nanome/blob/master/docs/demo_gif.gif)](https://www.youtube.com/watch?v=TfotM55KTVE) ## Highlights of NANOME pipeline ### Several first highlights for NANOME ![Figure_pipe_comp](https://github.com/LabShengLi/nanome/blob/master/docs/resources/pipeline_comparison.jpg) * Enables users to process **terabasescale** Oxford Nanopore sequencing datasets. * Provide a **one command line**/**web-based UI** for end-to-end analyzing Nanopore sequencing methylation-callings. * Support **various platform** executions: 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. * The **first 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-x and https://doi.org/10.1038/nbt.3820. * Allow **add new modules/tools** in 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 pipeline [comment]: <> (**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.) ![Figure1A](https://github.com/LabShengLi/nanome/blob/master/docs/Fig1A.jpg) **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. ![Figure1B](https://github.com/LabShengLi/nanome/blob/master/docs/Fig1B.jpg) **Fig. 1B. Workflow for 5-methylcytosine (5mC) detection for nanopore sequencing.** [comment]: <> (**Results:** We compared several 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.) [comment]: <> (**Conclusions:** Our study is the first benchmark of state-of-the-art 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. ) ### CI/CD features We use CI Automation Tools to **enable the automated testing on every commit and on PRs** to make sure that updates are not introducing bugs. Please check the automatic testing results on [Github](https://github.com/LabShengLi/nanome/actions). ## System Requirements ### Hardware requirements NANOME 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 requirements NANOME pipeline uses Nextflow technology. Users only need to install [Nextflow](https://www.nextflow.io/) (check the installation guide from https://nf-co.re/usage/installation), and have one of below commonly used environment tool: * [Conda](https://docs.conda.io/en/latest/miniconda.html) * [Docker](https://docs.docker.com/get-docker) * [Singularity](https://sylabs.io/guides/3.0/user-guide/installation.html) We 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](https://github.com/jts/nanopolish) >=0.13.2 [megalodon](https://github.com/nanoporetech/megalodon) >=2.2.9 [deepsignal](https://github.com/bioinfomaticsCSU/deepsignal) >=0.1.8 [ont-tombo](https://github.com/nanoporetech/tombo) >=1.5.1 [deepmod](https://github.com/WGLab/DeepMod) >=0.1.3 [METEORE](https://github.com/comprna/METEORE) >=1.0.0 [ont-pyguppy-client-lib](https://github.com/nanoporetech/pyguppyclient) >=4.2.2 [fast5mod](https://github.com/nanoporetech/fast5mod) >=1.0.5 [Clair3](https://github.com/HKU-BAL/Clair3) >=v0.1-r11 [Whatshap](https://github.com/whatshap/whatshap) >=1.0 [NanomethPhase bam2bis](https://github.com/vahidAK/NanoMethPhase) >= 1.0 [GNU Parallel](https://www.gnu.org/software/parallel) >=20170422 Guppy software >= 4.2.2 from [ONT (Oxford Nanopore Technologies) website](https://nanoporetech.com) ## Installation Users only need to install **Nextflow** (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: ```angular2html # Install nextflow conda install -c conda-forge -c bioconda nextflow nextflow -v ``` NANOME pipeline support running with various ways in different platforms: * Docker * Singularity * Conda * **Local** execution: running directly on default platform * HPC clusters with **SLURM** support * Cloud computing platform, e.g., Google Cloud Platform(GCP) with **google-lifesciences** support ## Simple usage Please refer to [Usage](https://github.com/LabShengLi/nanome/blob/master/docs/Usage.md) and [Specific Usage](https://github.com/LabShengLi/nanome/blob/master/docs/SpecificUsage.md) and [NANOME options](https://github.com/LabShengLi/nanome/blob/tutorial1/docs/nanome_params.md) for how to use NANOME pipeline. For running on CloudOS platform (e.g., google cloud), please check [Usage on CloudOS](https://github.com/LabShengLi/nanome/blob/master/docs/Usage.md#5-running-pipeline-on-cloud-computing-platform). We provide a **tutorial video** for running NANOME pipeline: [![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/TfotM55KTVE/0.jpg)](https://www.youtube.com/watch?v=TfotM55KTVE) When you have Nextflow software, NANOME pipeline can be directly executed without any other additional installation steps: ```angular2html # 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 our **CI/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 the **standardized whole genome-wide evaluation packages**, check [standardized evaluation tool usage](https://github.com/LabShengLi/nanome/blob/master/docs/Eval.md) for more detail. We do not suggest evaluating on a portion of CpGs for performance comparisons. ## Pipeline reports for NANOME ### Benchmarking reports on our HPC using [Nextflow](https://www.nextflow.io/) 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](https://github.com/LabShengLi/nanome/blob/master/docs/resources/trace_benchmark.txt.tsv), [Report](https://github.com/LabShengLi/nanome/blob/master/docs/resources/report_benchmark.pdf) and [Timeline](https://github.com/LabShengLi/nanome/blob/master/docs/resources/timeline_benchmark.pdf). 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: ![Bench-timeline](https://github.com/LabShengLi/nanome/blob/master/docs/resources/timeline_benchmark.jpg) ### Pipeline DAG ![NanomeDag](https://github.com/LabShengLi/nanome/blob/master/docs/nanome_dag.png) ### NANOME report Please check [NANOME report](https://github.com/LabShengLi/nanome/blob/master/docs/NANOME_report_html.pdf) for the sample report by NANOME pipeline. ![NanomeReportHtml](https://github.com/LabShengLi/nanome/blob/master/docs/nanome_report_html.png) ### Haplotype-aware consensus methylations Please check [phasing usage](https://github.com/LabShengLi/nanome/blob/tutorial1/docs/Phasing.md). ![PhasingDemo](https://github.com/LabShengLi/nanome/blob/master/docs/resources/nanome3t_5mc_phasing2.png) ### Lifebit CloudOS report We now support running NANOME on cloud computing platform. [Lifebit](https://lifebit.ai/lifebit-cloudos/) is a web-based cloud computing platform, and below is the running reports: * Ecoli test report: https://cloudos.lifebit.ai/public/jobs/61c9fd328c574a01e8d31d2e * Human test report: https://cloudos.lifebit.ai/public/jobs/61c9fe618c574a01e8d31e99 * NA12878 chr22 report: https://cloudos.lifebit.ai/public/jobs/61c4f2ad8c574a01e8d0eee3 * NA12878 chr20 part5 report: https://cloudos.lifebit.ai/public/jobs/61c770748c574a01e8d2062b ## Revision History For release history, please visit [here](https://github.com/LabShengLi/nanome/releases). For details, please go [here](https://github.com/LabShengLi/nanome/blob/master/README.md). ## Contact If you have any questions/issues/bugs, please post them on [GitHub](https://github.com/LabShengLi/nanome/issues). We will continuously update the GitHub to support famous methylation-calling tools for Oxford Nanopore sequencing. ## Reference **DNA 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 %package -n python3-nanome-jax Summary: NANOME (Nanopore methylation) pipeline developed by Li Lab at The Jackson Laboratory Provides: python-nanome-jax BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-nanome-jax # NANOME pipeline (Nanopore long-read sequencing data consensus DNA methylation detection) [![demo_gif.gif](https://github.com/LabShengLi/nanome/blob/master/docs/demo_gif.gif)](https://www.youtube.com/watch?v=TfotM55KTVE) ## Highlights of NANOME pipeline ### Several first highlights for NANOME ![Figure_pipe_comp](https://github.com/LabShengLi/nanome/blob/master/docs/resources/pipeline_comparison.jpg) * Enables users to process **terabasescale** Oxford Nanopore sequencing datasets. * Provide a **one command line**/**web-based UI** for end-to-end analyzing Nanopore sequencing methylation-callings. * Support **various platform** executions: 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. * The **first 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-x and https://doi.org/10.1038/nbt.3820. * Allow **add new modules/tools** in 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 pipeline [comment]: <> (**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.) ![Figure1A](https://github.com/LabShengLi/nanome/blob/master/docs/Fig1A.jpg) **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. ![Figure1B](https://github.com/LabShengLi/nanome/blob/master/docs/Fig1B.jpg) **Fig. 1B. Workflow for 5-methylcytosine (5mC) detection for nanopore sequencing.** [comment]: <> (**Results:** We compared several 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.) [comment]: <> (**Conclusions:** Our study is the first benchmark of state-of-the-art 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. ) ### CI/CD features We use CI Automation Tools to **enable the automated testing on every commit and on PRs** to make sure that updates are not introducing bugs. Please check the automatic testing results on [Github](https://github.com/LabShengLi/nanome/actions). ## System Requirements ### Hardware requirements NANOME 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 requirements NANOME pipeline uses Nextflow technology. Users only need to install [Nextflow](https://www.nextflow.io/) (check the installation guide from https://nf-co.re/usage/installation), and have one of below commonly used environment tool: * [Conda](https://docs.conda.io/en/latest/miniconda.html) * [Docker](https://docs.docker.com/get-docker) * [Singularity](https://sylabs.io/guides/3.0/user-guide/installation.html) We 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](https://github.com/jts/nanopolish) >=0.13.2 [megalodon](https://github.com/nanoporetech/megalodon) >=2.2.9 [deepsignal](https://github.com/bioinfomaticsCSU/deepsignal) >=0.1.8 [ont-tombo](https://github.com/nanoporetech/tombo) >=1.5.1 [deepmod](https://github.com/WGLab/DeepMod) >=0.1.3 [METEORE](https://github.com/comprna/METEORE) >=1.0.0 [ont-pyguppy-client-lib](https://github.com/nanoporetech/pyguppyclient) >=4.2.2 [fast5mod](https://github.com/nanoporetech/fast5mod) >=1.0.5 [Clair3](https://github.com/HKU-BAL/Clair3) >=v0.1-r11 [Whatshap](https://github.com/whatshap/whatshap) >=1.0 [NanomethPhase bam2bis](https://github.com/vahidAK/NanoMethPhase) >= 1.0 [GNU Parallel](https://www.gnu.org/software/parallel) >=20170422 Guppy software >= 4.2.2 from [ONT (Oxford Nanopore Technologies) website](https://nanoporetech.com) ## Installation Users only need to install **Nextflow** (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: ```angular2html # Install nextflow conda install -c conda-forge -c bioconda nextflow nextflow -v ``` NANOME pipeline support running with various ways in different platforms: * Docker * Singularity * Conda * **Local** execution: running directly on default platform * HPC clusters with **SLURM** support * Cloud computing platform, e.g., Google Cloud Platform(GCP) with **google-lifesciences** support ## Simple usage Please refer to [Usage](https://github.com/LabShengLi/nanome/blob/master/docs/Usage.md) and [Specific Usage](https://github.com/LabShengLi/nanome/blob/master/docs/SpecificUsage.md) and [NANOME options](https://github.com/LabShengLi/nanome/blob/tutorial1/docs/nanome_params.md) for how to use NANOME pipeline. For running on CloudOS platform (e.g., google cloud), please check [Usage on CloudOS](https://github.com/LabShengLi/nanome/blob/master/docs/Usage.md#5-running-pipeline-on-cloud-computing-platform). We provide a **tutorial video** for running NANOME pipeline: [![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/TfotM55KTVE/0.jpg)](https://www.youtube.com/watch?v=TfotM55KTVE) When you have Nextflow software, NANOME pipeline can be directly executed without any other additional installation steps: ```angular2html # 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 our **CI/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 the **standardized whole genome-wide evaluation packages**, check [standardized evaluation tool usage](https://github.com/LabShengLi/nanome/blob/master/docs/Eval.md) for more detail. We do not suggest evaluating on a portion of CpGs for performance comparisons. ## Pipeline reports for NANOME ### Benchmarking reports on our HPC using [Nextflow](https://www.nextflow.io/) 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](https://github.com/LabShengLi/nanome/blob/master/docs/resources/trace_benchmark.txt.tsv), [Report](https://github.com/LabShengLi/nanome/blob/master/docs/resources/report_benchmark.pdf) and [Timeline](https://github.com/LabShengLi/nanome/blob/master/docs/resources/timeline_benchmark.pdf). 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: ![Bench-timeline](https://github.com/LabShengLi/nanome/blob/master/docs/resources/timeline_benchmark.jpg) ### Pipeline DAG ![NanomeDag](https://github.com/LabShengLi/nanome/blob/master/docs/nanome_dag.png) ### NANOME report Please check [NANOME report](https://github.com/LabShengLi/nanome/blob/master/docs/NANOME_report_html.pdf) for the sample report by NANOME pipeline. ![NanomeReportHtml](https://github.com/LabShengLi/nanome/blob/master/docs/nanome_report_html.png) ### Haplotype-aware consensus methylations Please check [phasing usage](https://github.com/LabShengLi/nanome/blob/tutorial1/docs/Phasing.md). ![PhasingDemo](https://github.com/LabShengLi/nanome/blob/master/docs/resources/nanome3t_5mc_phasing2.png) ### Lifebit CloudOS report We now support running NANOME on cloud computing platform. [Lifebit](https://lifebit.ai/lifebit-cloudos/) is a web-based cloud computing platform, and below is the running reports: * Ecoli test report: https://cloudos.lifebit.ai/public/jobs/61c9fd328c574a01e8d31d2e * Human test report: https://cloudos.lifebit.ai/public/jobs/61c9fe618c574a01e8d31e99 * NA12878 chr22 report: https://cloudos.lifebit.ai/public/jobs/61c4f2ad8c574a01e8d0eee3 * NA12878 chr20 part5 report: https://cloudos.lifebit.ai/public/jobs/61c770748c574a01e8d2062b ## Revision History For release history, please visit [here](https://github.com/LabShengLi/nanome/releases). For details, please go [here](https://github.com/LabShengLi/nanome/blob/master/README.md). ## Contact If you have any questions/issues/bugs, please post them on [GitHub](https://github.com/LabShengLi/nanome/issues). We will continuously update the GitHub to support famous methylation-calling tools for Oxford Nanopore sequencing. ## Reference **DNA 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 %package help Summary: Development documents and examples for nanome-jax Provides: python3-nanome-jax-doc %description help # NANOME pipeline (Nanopore long-read sequencing data consensus DNA methylation detection) [![demo_gif.gif](https://github.com/LabShengLi/nanome/blob/master/docs/demo_gif.gif)](https://www.youtube.com/watch?v=TfotM55KTVE) ## Highlights of NANOME pipeline ### Several first highlights for NANOME ![Figure_pipe_comp](https://github.com/LabShengLi/nanome/blob/master/docs/resources/pipeline_comparison.jpg) * Enables users to process **terabasescale** Oxford Nanopore sequencing datasets. * Provide a **one command line**/**web-based UI** for end-to-end analyzing Nanopore sequencing methylation-callings. * Support **various platform** executions: 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. * The **first 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-x and https://doi.org/10.1038/nbt.3820. * Allow **add new modules/tools** in 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 pipeline [comment]: <> (**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.) ![Figure1A](https://github.com/LabShengLi/nanome/blob/master/docs/Fig1A.jpg) **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. ![Figure1B](https://github.com/LabShengLi/nanome/blob/master/docs/Fig1B.jpg) **Fig. 1B. Workflow for 5-methylcytosine (5mC) detection for nanopore sequencing.** [comment]: <> (**Results:** We compared several 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.) [comment]: <> (**Conclusions:** Our study is the first benchmark of state-of-the-art 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. ) ### CI/CD features We use CI Automation Tools to **enable the automated testing on every commit and on PRs** to make sure that updates are not introducing bugs. Please check the automatic testing results on [Github](https://github.com/LabShengLi/nanome/actions). ## System Requirements ### Hardware requirements NANOME 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 requirements NANOME pipeline uses Nextflow technology. Users only need to install [Nextflow](https://www.nextflow.io/) (check the installation guide from https://nf-co.re/usage/installation), and have one of below commonly used environment tool: * [Conda](https://docs.conda.io/en/latest/miniconda.html) * [Docker](https://docs.docker.com/get-docker) * [Singularity](https://sylabs.io/guides/3.0/user-guide/installation.html) We 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](https://github.com/jts/nanopolish) >=0.13.2 [megalodon](https://github.com/nanoporetech/megalodon) >=2.2.9 [deepsignal](https://github.com/bioinfomaticsCSU/deepsignal) >=0.1.8 [ont-tombo](https://github.com/nanoporetech/tombo) >=1.5.1 [deepmod](https://github.com/WGLab/DeepMod) >=0.1.3 [METEORE](https://github.com/comprna/METEORE) >=1.0.0 [ont-pyguppy-client-lib](https://github.com/nanoporetech/pyguppyclient) >=4.2.2 [fast5mod](https://github.com/nanoporetech/fast5mod) >=1.0.5 [Clair3](https://github.com/HKU-BAL/Clair3) >=v0.1-r11 [Whatshap](https://github.com/whatshap/whatshap) >=1.0 [NanomethPhase bam2bis](https://github.com/vahidAK/NanoMethPhase) >= 1.0 [GNU Parallel](https://www.gnu.org/software/parallel) >=20170422 Guppy software >= 4.2.2 from [ONT (Oxford Nanopore Technologies) website](https://nanoporetech.com) ## Installation Users only need to install **Nextflow** (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: ```angular2html # Install nextflow conda install -c conda-forge -c bioconda nextflow nextflow -v ``` NANOME pipeline support running with various ways in different platforms: * Docker * Singularity * Conda * **Local** execution: running directly on default platform * HPC clusters with **SLURM** support * Cloud computing platform, e.g., Google Cloud Platform(GCP) with **google-lifesciences** support ## Simple usage Please refer to [Usage](https://github.com/LabShengLi/nanome/blob/master/docs/Usage.md) and [Specific Usage](https://github.com/LabShengLi/nanome/blob/master/docs/SpecificUsage.md) and [NANOME options](https://github.com/LabShengLi/nanome/blob/tutorial1/docs/nanome_params.md) for how to use NANOME pipeline. For running on CloudOS platform (e.g., google cloud), please check [Usage on CloudOS](https://github.com/LabShengLi/nanome/blob/master/docs/Usage.md#5-running-pipeline-on-cloud-computing-platform). We provide a **tutorial video** for running NANOME pipeline: [![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/TfotM55KTVE/0.jpg)](https://www.youtube.com/watch?v=TfotM55KTVE) When you have Nextflow software, NANOME pipeline can be directly executed without any other additional installation steps: ```angular2html # 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 our **CI/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 the **standardized whole genome-wide evaluation packages**, check [standardized evaluation tool usage](https://github.com/LabShengLi/nanome/blob/master/docs/Eval.md) for more detail. We do not suggest evaluating on a portion of CpGs for performance comparisons. ## Pipeline reports for NANOME ### Benchmarking reports on our HPC using [Nextflow](https://www.nextflow.io/) 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](https://github.com/LabShengLi/nanome/blob/master/docs/resources/trace_benchmark.txt.tsv), [Report](https://github.com/LabShengLi/nanome/blob/master/docs/resources/report_benchmark.pdf) and [Timeline](https://github.com/LabShengLi/nanome/blob/master/docs/resources/timeline_benchmark.pdf). 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: ![Bench-timeline](https://github.com/LabShengLi/nanome/blob/master/docs/resources/timeline_benchmark.jpg) ### Pipeline DAG ![NanomeDag](https://github.com/LabShengLi/nanome/blob/master/docs/nanome_dag.png) ### NANOME report Please check [NANOME report](https://github.com/LabShengLi/nanome/blob/master/docs/NANOME_report_html.pdf) for the sample report by NANOME pipeline. ![NanomeReportHtml](https://github.com/LabShengLi/nanome/blob/master/docs/nanome_report_html.png) ### Haplotype-aware consensus methylations Please check [phasing usage](https://github.com/LabShengLi/nanome/blob/tutorial1/docs/Phasing.md). ![PhasingDemo](https://github.com/LabShengLi/nanome/blob/master/docs/resources/nanome3t_5mc_phasing2.png) ### Lifebit CloudOS report We now support running NANOME on cloud computing platform. [Lifebit](https://lifebit.ai/lifebit-cloudos/) is a web-based cloud computing platform, and below is the running reports: * Ecoli test report: https://cloudos.lifebit.ai/public/jobs/61c9fd328c574a01e8d31d2e * Human test report: https://cloudos.lifebit.ai/public/jobs/61c9fe618c574a01e8d31e99 * NA12878 chr22 report: https://cloudos.lifebit.ai/public/jobs/61c4f2ad8c574a01e8d0eee3 * NA12878 chr20 part5 report: https://cloudos.lifebit.ai/public/jobs/61c770748c574a01e8d2062b ## Revision History For release history, please visit [here](https://github.com/LabShengLi/nanome/releases). For details, please go [here](https://github.com/LabShengLi/nanome/blob/master/README.md). ## Contact If you have any questions/issues/bugs, please post them on [GitHub](https://github.com/LabShengLi/nanome/issues). We will continuously update the GitHub to support famous methylation-calling tools for Oxford Nanopore sequencing. ## Reference **DNA 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 %prep %autosetup -n nanome-jax-2.0.11 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-nanome-jax -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Thu Jun 08 2023 Python_Bot - 2.0.11-1 - Package Spec generated