Data
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Experiment Accession
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sample
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FASTQ
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Experiment Title
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Organism Name
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Instrument
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Submitter
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Study Accession
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Study Title
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Sample Accession
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Total Size, Mb
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Total Spots
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Total Bases
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Library Strategy
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Library Source
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Library Selection
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SRX14748451
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S1
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SRR18645307
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Homo sapiens
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Homo sapiens
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MinION
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Drexel University
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SRP367676
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Multiplex structural variant detection by whole-genome mapping and nanopore sequencing.
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SRS12509856
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821.1
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348226
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972620520
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OTHER
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GENOMIC
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other
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SRX19406878
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S2
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SRR23513621
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NA12878 DNA sequencing from nanopore WSG consortium - basecalled sequences (Guppy 6.1.3 super accuracy)
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Homo sapiens
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MinION
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Garvan Institute of Medical Research
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SRP421403
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Curated publicly available nanopore datasets
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SRS16801715
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78526.8
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11173458
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97545895593
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WGS
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GENOMIC
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RANDOM
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ERX8211413
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S3
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ERR8578833
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MinION sequencing
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Homo sapiens
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MinION
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the university of hong kong
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ERP135493
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Target enrichment sequencing and variant calling on medical exome using ONT MinION
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ERS10590135
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8961.02
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9636172
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10382057986
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Targeted-Capture
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GENOMIC
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PCR
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ERX8211414
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S4
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ERR8578834
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MinION sequencing
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Homo sapiens
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MinION
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the university of hong kong
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ERP135493
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Target enrichment sequencing and variant calling on medical exome using ONT MinION
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ERS10590135
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10669.72
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10644000
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12212807287
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Targeted-Capture
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GENOMIC
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PCR
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SRX13322984
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S5
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SRR17138639
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Nanopore targeted sequencing (ReadUntil/ReadFish) of NA12878-HG001- basecalled sequences
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Homo sapiens
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MinION
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Garvan Institute of Medical Research
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SRP349335
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Comprehensive genetic diagnosis of tandem repeat expansion disorders with programmable targeted nanopore sequencing
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SRS11230712
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6629.97
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5513156
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7815960904
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WGS
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GENOMIC
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other
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SRX13323057
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S6
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SRR17138566
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Nanopore targeted sequencing (ReadUntil/ReadFish) of NA12878-HG001- basecalled sequences
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Homo sapiens
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MinION
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Overview of today’s session:
Learn to use CONDA to install tools and create conda environments
Hands-on exercises:
Quality Control (QC) of raw Nanopore data
Mapping of processed Nanopore data onto a reference genome
Run the epi2me-labs/wf-human-variation nextflow pipeline
Public Nanopore Datasets
Experiment Accession | sample | FASTQ | Experiment Title | Organism Name | Instrument | Submitter | Study Accession | Study Title | Sample Accession | Total Size, Mb | Total Spots | Total Bases | Library Strategy | Library Source | Library Selection | |
SRX14748451 | S1 | SRR18645307 | Homo sapiens | Homo sapiens | MinION | Drexel University | SRP367676 | Multiplex structural variant detection by whole-genome mapping and nanopore sequencing. | SRS12509856 | 821.1 | 348226 | 972620520 | OTHER | GENOMIC | other | |
ERX8211413 | S3 | ERR8578833 | MinION sequencing | Homo sapiens | MinION | the university of hong kong | ERP135493 | Target enrichment sequencing and variant calling on medical exome using ONT MinION | ERS10590135 | 8961.02 | 9636172 | 10382057986 | Targeted-Capture | GENOMIC | PCR | |
SRX13322984 | S5 | SRR17138639 | Nanopore targeted sequencing (ReadUntil/ReadFish) of NA12878-HG001- basecalled sequences | Homo sapiens | MinION | Garvan Institute of Medical Research | SRP349335 | Comprehensive genetic diagnosis of tandem repeat expansion disorders with programmable targeted nanopore sequencing | SRS11230747 | SRS11230712 | 6629.97 | 5513156 | 7815960904 | WGS | GENOMIC | other |
SRX13323057 | S6 | SRR17138566 | Nanopore targeted sequencing (ReadUntil/ReadFish) of NA12878-HG001- basecalled sequences | Homo sapiens | MinION | Garvan Institute of Medical Research | SRP349335 | Comprehensive genetic diagnosis of tandem repeat expansion disorders with programmable targeted nanopore sequencing | SRS11230747 | 17107.98 | 12278391 | 20238395479 | WGS | GENOMIC | other |
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Anaconda Distribution is a full featured installer that comes with a suite of packages for data science, as well as Anaconda Navigator, a GUI application for working with conda environments.
NOTE: if you have already installed conda then you do not need to do the steps below |
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Download Miniconda installer for your system https://docs.anaconda.com/free/miniconda/
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~/miniconda3/bin/conda init bash |
Mapping
Let’s run the --help option of the pipeline to get information on the available parameters
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module load java
nextflow run epi2me-labs/wf-alignment -profile singularity --help |
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N E X T F L O W ~ version 23.12.0-edge
Launching `https://github.com/epi2me-labs/wf-alignment` [nostalgic_galileo] DSL2 - revision: e1fd7a51dc [master]
WARN: Config setting `prov.formats` is not defined, no provenance reports will be produced
|||||||||| _____ ____ ___ ____ __ __ _____ _ _
|||||||||| | ____| _ \_ _|___ \| \/ | ____| | | __ _| |__ ___
||||| | _| | |_) | | __) | |\/| | _| _____| |/ _` | '_ \/ __|
||||| | |___| __/| | / __/| | | | |__|_____| | (_| | |_) \__ \
|||||||||| |_____|_| |___|_____|_| |_|_____| |_|\__,_|_.__/|___/
|||||||||| wf-alignment v1.1.2-ge1fd7a5
--------------------------------------------------------------------------------
Typical pipeline command:
nextflow run epi2me-labs/wf-alignment \
--fastq 'wf-alignment-demo/fastq' \
--references 'wf-alignment-demo/references'
Input Options
--fastq [string] FASTQ files to use in the analysis.
--bam [string] BAM or unaligned BAM (uBAM) files to use in the analysis.
--analyse_unclassified [boolean] Analyse unclassified reads from input directory. By default the workflow will not process reads in the unclassified
directory.
--references [string] Path to a directory containing FASTA reference files.
--reference_mmi_file [string] Path to an MMI index file to be used as reference.
--counts [string] Path to a CSV file containing expected counts as a control.
Sample Options
--sample_sheet [string] A CSV file used to map barcodes to sample aliases. The sample sheet can be provided when the input data is a directory
containing sub-directories with FASTQ files.
--sample [string] A single sample name for non-multiplexed data. Permissible if passing a single .fastq(.gz) file or directory of .fastq(.gz)
files.
Output Options
--out_dir [string] Directory for output of all workflow results. [default: output]
--prefix [string] Optional prefix attached to each of the output filenames.
Advanced options
--depth_coverage [boolean] Calculate depth coverage statistics and include them in the report. [default: true]
--minimap_preset [choice] Pre-defined parameter sets for `minimap2`, covering most common use cases. [default: dna]
* dna
* rna
--minimap_args [string] String of command line arguments to be passed on to `minimap2`.
Miscellaneous Options
--threads [integer] Number of CPU threads to use for the alignment step. [default: 4]
--disable_ping [boolean] Enable to prevent sending a workflow ping.
Other parameters
--monochrome_logs [boolean] null
--validate_params [boolean] null [default: true]
--show_hidden_params [boolean] null
!! Hiding 4 params, use --show_hidden_params to show them !!
--------------------------------------------------------------------------------
If you use epi2me-labs/wf-alignment for your analysis please cite:
* The nf-core framework
https://doi.org/10.1038/s41587-020-0439-x |
Variant calling
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nextflow run epi2me-labs/wf-human-variation -profile singularity --help |
Once logged in you will be able to access the conda “base” environment
Configure conda channels https://bioconda.github.io/
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conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
conda config --set channel_priority strict |
Source for information below: https://astrobiomike.github.io/unix/conda-intro
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Base environment
The “base” conda environment is, like it sounds, kind of our home base inside conda. We wouldn’t want to install lots of complicated programs here, as the more things added, the more likely something is going to end up having a conflict. But the base environment is somewhere we might want to install smaller programs that we tend to use a lot (example below).
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Making a new environment
The simplest way we can create a new conda environment is like so:
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conda create -n new-env |
Where the base command is conda create, then we are specifying the name of our new environment with -n (here “new-env”). It will check some things out and tell us where it is going to put it, when we hit yand enter, it will be created.
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Entering an environment
To enter that environment, we need to execute:
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conda activate new-env |
And now we can see our prompt has changed to have (new-env) at the front, telling us we are in that environment.
If we had forgotten the name, or wanted to see all of our environments, we can do so with:
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conda env list |
Which will print out all of the available conda environments, and have an asterisk next to the one we are currently in.
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Exiting an environment
We can exit whatever conda environment we are currently in by running:
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conda deactivate |
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Making an environment with a specific python version
By default, the conda create command will use the python version that the base conda environment is running. But we can specify a different one in the command if we’d like:
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conda create -n python-v2.7 python=2.7 |
Breakdown
conda create– this is our base command
-n python-v2.7– we are naming the environment “python-v2.7”
python=2.7– here we are specifying the python version to use within the environment
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Removing an environment
And here is how we can remove an environment, by providing its name to the -n flag:
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conda deactivate # we can't be inside the environment we want to remove
conda env remove -n python-v2.7 |
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