From c328a792a4678e6d44187957256bb5e9390fc4f6 Mon Sep 17 00:00:00 2001 From: mirpedrol Date: Wed, 7 Jun 2023 15:42:08 +0200 Subject: [PATCH 1/4] docs review --- README.md | 2 +- docs/output_screening.md | 8 ++++---- docs/usage.md | 7 ++++++- docs/usage_screening.md | 6 +----- 4 files changed, 12 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 88799c1f..24cafb75 100644 --- a/README.md +++ b/README.md @@ -26,7 +26,7 @@ On release, automated continuous integration tests run the pipeline on a full-si ## Pipeline summary -For crispr editing : +For crispr targeted : 1. Merge paired-end reads (([`Pear`](https://cme.h-its.org/exelixis/web/software/pear/doc.html))) 2. Read QC ([`FastQC`](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)) diff --git a/docs/output_screening.md b/docs/output_screening.md index 130d5d43..f10f8e18 100644 --- a/docs/output_screening.md +++ b/docs/output_screening.md @@ -14,12 +14,12 @@ The pipeline is built using [Nextflow](https://www.nextflow.io/) and processes d - [FastQC](#sequences) - Input sequence preparation (reference, protospacer, template) - [FastQC](#fastqc) - Read Quality Control - [Counting](#counting) - - [MAGeCK count](#count) - Mapping reads to reference + - [MAGeCK count](#mageck-count) - Mapping reads to reference - [CNV correction](#counting) - [CRISPRcleanR](#crisprcleanr) - Copy Number Variation correction and read normalization in case of knock-out screens. -- [Gene essentiality](#gene-essentiality) - - [MAGeCK rra](#rra) - modified robust ranking aggregation (RRA) algorithm - - [MAGeCK mle](#mle) - maximum-likelihood estimation (MLE) for robust identification of CRISPR-screen hits +- [Gene essentiality](#gene-essentiality-computation) + - [MAGeCK rra](#mageck-rra) - modified robust ranking aggregation (RRA) algorithm + - [MAGeCK mle](#mageck-mle) - maximum-likelihood estimation (MLE) for robust identification of CRISPR-screen hits - [MultiQC](#multiqc) - Aggregate report describing results and QC from the whole pipeline - [Pipeline information](#pipeline-information) - Report metrics generated during the workflow execution diff --git a/docs/usage.md b/docs/usage.md index 0ac8519c..2325df0b 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -6,7 +6,12 @@ ## Introduction -The **nf-core/crisprseq** pipeline allows the analysis of CRISPR edited next generation sequencing (NGS) data and CRISPR pooled DNA. It evaluates the quality of gene editing experiments using targeted NGS data. +The **nf-core/crisprseq** pipeline allows the analysis of CRISPR edited DNA. It evaluates the quality of gene editing experiments using targeted next generation sequencing (NGS) data (`targeted`) as well as important genes from knock-out or activation CRISPR-Cas9 screens using CRISPR pooled DNA (`screening`). + +## Type of analysis + +The `--analysis` parameter specifies whether the user intends to perform editing or screening in the crisprseq pipeline. + ## Samplesheet input diff --git a/docs/usage_screening.md b/docs/usage_screening.md index 6204b63a..8c1fa331 100644 --- a/docs/usage_screening.md +++ b/docs/usage_screening.md @@ -6,11 +6,7 @@ ## Introduction -The **nf-core/crisprseq** pipeline allows the analysis of CRISPR edited next generation sequencing (NGS) data and CRISPR pooled DNA. It can evaluate the quality of gene editing experiments using targeted NGS data, as well as evaluate important genes from knock-out or activation CRISPR-Cas9 screens. - -## Type of analysis - -The "type" parameter specifies whether the user intends to perform editing or screening in the crisprseq pipeline. +The **nf-core/crisprseq** pipeline allows the analysis of CRISPR edited CRISPR pooled DNA. It can evaluate important genes from knock-out or activation CRISPR-Cas9 screens. ## Samplesheet input editing From b108bc9d15606e365b31a9329df8c6c8126e026c Mon Sep 17 00:00:00 2001 From: mirpedrol Date: Wed, 7 Jun 2023 16:06:26 +0200 Subject: [PATCH 2/4] check input for targeted analysis --- lib/WorkflowMain.groovy | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/lib/WorkflowMain.groovy b/lib/WorkflowMain.groovy index ca43e2df..92b176a5 100755 --- a/lib/WorkflowMain.groovy +++ b/lib/WorkflowMain.groovy @@ -80,10 +80,9 @@ class WorkflowMain { NfcoreTemplate.awsBatch(workflow, params) // Check input has been provided - //if (!params.input) { - // log.error "Please provide an input samplesheet to the pipeline e.g. '--input samplesheet.csv'" - // System.exit(1) - // } + if (params.analysis == 'targeted' && !params.input) { + throw new Exception("Please provide an input samplesheet to the pipeline e.g. '--input samplesheet.csv'") + } } // // Get attribute from genome config file e.g. fasta From 1b508a662e586dd12d0c7e88a1a77aa50c00b08b Mon Sep 17 00:00:00 2001 From: mirpedrol Date: Wed, 7 Jun 2023 16:19:42 +0200 Subject: [PATCH 3/4] split docs --- docs/output.md | 227 +-------------------------------- docs/output_targeted.md | 228 +++++++++++++++++++++++++++++++++ docs/usage.md | 263 +------------------------------------- docs/usage_targeted.md | 270 ++++++++++++++++++++++++++++++++++++++++ 4 files changed, 505 insertions(+), 483 deletions(-) create mode 100644 docs/output_targeted.md create mode 100644 docs/usage_targeted.md diff --git a/docs/output.md b/docs/output.md index 1cb835c1..19740483 100644 --- a/docs/output.md +++ b/docs/output.md @@ -2,227 +2,8 @@ ## Introduction -This document describes the output produced by the pipeline. Most of the plots are taken from the MultiQC report, which summarises results at the end of the pipeline. +This document describes the output produced by the pipeline. -The directories listed below will be created in the results directory after the pipeline has finished. All paths are relative to the top-level results directory. - -## Pipeline overview - -The pipeline is built using [Nextflow](https://www.nextflow.io/) and processes data using the following steps: - -- [Preprocessing](#preprocessing) - - [Sequences](#sequences) - Input sequence preparation (reference, protospacer, template) - - [cat](#cat) - Concatenate sample fastq files if required - - [Pear](#pear) - Join double-end reads if required - - [FastQC](#fastqc) - Read Quality Control - - [Adapters](#adapters) - Find adapters (Overrepresented sequences) in reads - - [Cutadapt](#cutadapt) - Trim adapters - - [Seqtk](#seqtk) - Mask low-quality bases - -- [Mapping](#mapping) - - [minimap2](#minimap2) - Mapping reads to reference - - [BWA](#bwa) - Mapping reads to reference - - [bowtie2](#bowtie2) - Mapping reads to reference -- [Edits calling](#edits-calling) - - [CIGAR](#cigar) - Parse CIGAR to call edits -- [MultiQC](#multiqc) - Aggregate report describing results and QC from the whole pipeline -- [Pipeline information](#pipeline-information) - Report metrics generated during the workflow execution - -## Preprocessing - -### Sequences - -
-Output files - -- `preprocessing/sequences/` - - `*_reference.fasta`: Sequence used as a reference. - - `*_template.fasta`: Provided template sequence. - - `*_correctOrient.fasta`: Reference sequence in the correct orientation. - - `_NewReference.fasta`: New reference generated from adding the changes made by the template to the original reference. - - `*_template-align.bam`: Alignment of the new reference (with template changes) to the original reference. - -
- -Contains the input sequences (reference, protospacer and template). Sequences are preprocessed as required: - -- The reference is returned in the correct orientation. - > In order to provide the reference in the correct orientation, the protospacer is searched in the reference sequence. The reverse complement is returned if the protospacer matches the reference in reverse complement. -- The template is used to obtain a new reference with the expected changed. - -### cat - -
-Output files - -- `preprocessing/cat/` - - `*.merged.fastq.gz`: Concatenated fastq files - -
- -If multiple libraries/runs have been provided for the same sample in the input samplesheet (e.g. to increase sequencing depth) then these will be merged at the very beginning of the pipeline in order to have consistent sample naming throughout the pipeline. Please refer to the [usage](https://nf-co.re/crisprseq/usage) documentation to see how to specify these samples in the input samplesheet. - -### Pear - -
-Output files - -- `preprocessing/pear/` - - `*.assembled.fastq.gz`: Assembled paired-end reads - - `*.discarded.fastq.gz`: Discarded reads - - `*.unassembled.forward.fastq.gz`: Unassembled paired-end reads - forward (R1) - - `*.unassembled.reverse.fastq.gz`: Unassembled paired-end reads - reverse (R2) - -
- -[PEAR](https://cme.h-its.org/exelixis/web/software/pear/) is a pair-end read merger. - -### FastQC - -
-Output files - -- `fastqc/` - - `*_fastqc.html`: FastQC report containing quality metrics. - - `*_fastqc.zip`: Zip archive containing the FastQC report, tab-delimited data file and plot images. - -
- -[FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) gives general quality metrics about your sequenced reads. It provides information about the quality score distribution across your reads, per base sequence content (%A/T/G/C), adapter contamination and overrepresented sequences. For further reading and documentation see the [FastQC help pages](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/). - -![MultiQC - FastQC sequence counts plot](images/mqc_fastqc_counts.png) - -![MultiQC - FastQC mean quality scores plot](images/mqc_fastqc_quality.png) - -![MultiQC - FastQC adapter content plot](images/mqc_fastqc_adapter.png) - -> **NB:** The FastQC plots displayed in the MultiQC report shows _untrimmed_ reads. They may contain adapter sequence and potentially regions with low quality. - -### Adapters - -
-Output files - -- `preprocessing/adapters/` - - `*_overrepresented.fasta`: Contains overrepresented sequences found by FastQC - -
- -[FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) finds over-represented sequences in samples. It lists all of the sequence which make up more than 0.1% of the total reads. For each over-represented sequence the program will look for matches in a database of common contaminants and will report the best hit it finds. Hits must be at least 20bp in length and have no more than 1 mismatch. - -### Cutadapt - -
-Output files - -- `preprocessing/cutadapt/` - - `*.cutadapt.log`: Cutadapt log file - - `*.trim.fastq.gz`: Sample reads trimmed with overrepresented sequences removed - -
- -### Seqtk - -
-Output files - -- `preprocessing/seqtk/` - - `*.seqtk-seq.fastq.gz`: Quality filtered reads. - -
- -[Seqtk](https://github.com/lh3/seqtk) masks (converts to Ns) bases with quality lower than 20 and removes sequences shorter than 80 bases. - - - -## Mapping - -### minimap2 - -
-Output files - -- `minimap2/` - - `*.bam`: BAM file containing aligned reads - - `*.bai`: BAI index - -
- -[Minimap2](https://github.com/lh3/minimap2) is a sequence alignment program that aligns DNA sequences against a reference database. - -### BWA - -
-Output files - -- `bwa/` - - `*.bam`: BAM file containing aligned reads - - `*.bai`: BAI index - -
- -[BWA-MEM](https://github.com/lh3/bwa) BWA is a software package for mapping low-divergent sequences against a reference genome. - -### bowtie2 - -
-Output files - -- `bowtie2/` - - `*.bam`: BAM file containing aligned reads - - `*.bai`: BAI index - -
- -[Bowtie2](https://bowtie-bio.sourceforge.net/bowtie2/index.shtml) aligns sequencing reads to reference sequences. - -## Edits calling - -### CIGAR - -
-Output files - -- `cigar/` - - `*_cutSite.json`: Contains the protospacer cut site position in the reference. - - `*_edition.html`: Interactive pie chart with the percentage of edition types. Reads are classified between WT (without an edit) and indels. Indes are divided between deletions, insertions and delins (deletion + insertion). Deletions and insertions can be out of frame or in frame. - ![Test sample hCas9-AAVS1-a edition plot](images/hCas9-AAVS1-a_edition.png) - - `*_edits.csv`: Table containing the data visualized in the pie chart. - - `*_indels.csv`: Table containing information of all reads. Edit type, edit start and length, if the edition happens above the error rate, if it's located into the common edit window, the frequency, the percentage, the pattern, surrounding nucleotides in case of insertions, the protospacer cut site, the sample id, number of aligned reads and number of reads with and without a template modification. - - `*_QC-indels.html`: Interactive pie chart with information about aligned reads. Reads are classified between WT and containing indels. Both types are classified between passing the filtering steps or not. Indel reads passing the filtering steps are divided in reads with a modification above the error rate and located in the common edit window, above the error rate but not in the edit region, viceversa, or any of those conditions. - ![Test sample hCas9-AAVS1-a QC indels plot](images/hCas9-AAVS1-a_QC-indels.png) - - `*_reads.html`: Interactive pie chart with percentage of the number of raw reads, reads merged with Pear, reads passing quality filters and UMI clustered reads. - ![Test sample hCas9-AAVS1-a reads plot](images/hCas9-AAVS1-a_reads.png) - - `*_subs-perc.csv`: Table containing the percentage of each nucleotide found for each reference position. - -
- -## MultiQC - -
-Output files - -- `multiqc/` - - `multiqc_report.html`: a standalone HTML file that can be viewed in your web browser. - - `multiqc_data/`: directory containing parsed statistics from the different tools used in the pipeline. - - `multiqc_plots/`: directory containing static images from the report in various formats. - -
- -[MultiQC](http://multiqc.info) is a visualization tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in the report data directory. - -Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQC. The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see . - -## Pipeline information - -
-Output files - -- `pipeline_info/` - - Reports generated by Nextflow: `execution_report.html`, `execution_timeline.html`, `execution_trace.txt` and `pipeline_dag.dot`/`pipeline_dag.svg`. - - Reports generated by the pipeline: `pipeline_report.html`, `pipeline_report.txt` and `software_versions.yml`. The `pipeline_report*` files will only be present if the `--email` / `--email_on_fail` parameter's are used when running the pipeline. - - Reformatted samplesheet files used as input to the pipeline: `samplesheet.valid.csv`. - -
- -[Nextflow](https://www.nextflow.io/docs/latest/tracing.html) provides excellent functionality for generating various reports relevant to the running and execution of the pipeline. This will allow you to troubleshoot errors with the running of the pipeline, and also provide you with other information such as launch commands, run times and resource usage. +Please refer to the respective Output documentation: +- [Output targeted](output-targeted) +- [Output screening](output-screening) diff --git a/docs/output_targeted.md b/docs/output_targeted.md new file mode 100644 index 00000000..1cb835c1 --- /dev/null +++ b/docs/output_targeted.md @@ -0,0 +1,228 @@ +# nf-core/crisprseq: Output + +## Introduction + +This document describes the output produced by the pipeline. Most of the plots are taken from the MultiQC report, which summarises results at the end of the pipeline. + +The directories listed below will be created in the results directory after the pipeline has finished. All paths are relative to the top-level results directory. + +## Pipeline overview + +The pipeline is built using [Nextflow](https://www.nextflow.io/) and processes data using the following steps: + +- [Preprocessing](#preprocessing) + - [Sequences](#sequences) - Input sequence preparation (reference, protospacer, template) + - [cat](#cat) - Concatenate sample fastq files if required + - [Pear](#pear) - Join double-end reads if required + - [FastQC](#fastqc) - Read Quality Control + - [Adapters](#adapters) - Find adapters (Overrepresented sequences) in reads + - [Cutadapt](#cutadapt) - Trim adapters + - [Seqtk](#seqtk) - Mask low-quality bases + +- [Mapping](#mapping) + - [minimap2](#minimap2) - Mapping reads to reference + - [BWA](#bwa) - Mapping reads to reference + - [bowtie2](#bowtie2) - Mapping reads to reference +- [Edits calling](#edits-calling) + - [CIGAR](#cigar) - Parse CIGAR to call edits +- [MultiQC](#multiqc) - Aggregate report describing results and QC from the whole pipeline +- [Pipeline information](#pipeline-information) - Report metrics generated during the workflow execution + +## Preprocessing + +### Sequences + +
+Output files + +- `preprocessing/sequences/` + - `*_reference.fasta`: Sequence used as a reference. + - `*_template.fasta`: Provided template sequence. + - `*_correctOrient.fasta`: Reference sequence in the correct orientation. + - `_NewReference.fasta`: New reference generated from adding the changes made by the template to the original reference. + - `*_template-align.bam`: Alignment of the new reference (with template changes) to the original reference. + +
+ +Contains the input sequences (reference, protospacer and template). Sequences are preprocessed as required: + +- The reference is returned in the correct orientation. + > In order to provide the reference in the correct orientation, the protospacer is searched in the reference sequence. The reverse complement is returned if the protospacer matches the reference in reverse complement. +- The template is used to obtain a new reference with the expected changed. + +### cat + +
+Output files + +- `preprocessing/cat/` + - `*.merged.fastq.gz`: Concatenated fastq files + +
+ +If multiple libraries/runs have been provided for the same sample in the input samplesheet (e.g. to increase sequencing depth) then these will be merged at the very beginning of the pipeline in order to have consistent sample naming throughout the pipeline. Please refer to the [usage](https://nf-co.re/crisprseq/usage) documentation to see how to specify these samples in the input samplesheet. + +### Pear + +
+Output files + +- `preprocessing/pear/` + - `*.assembled.fastq.gz`: Assembled paired-end reads + - `*.discarded.fastq.gz`: Discarded reads + - `*.unassembled.forward.fastq.gz`: Unassembled paired-end reads - forward (R1) + - `*.unassembled.reverse.fastq.gz`: Unassembled paired-end reads - reverse (R2) + +
+ +[PEAR](https://cme.h-its.org/exelixis/web/software/pear/) is a pair-end read merger. + +### FastQC + +
+Output files + +- `fastqc/` + - `*_fastqc.html`: FastQC report containing quality metrics. + - `*_fastqc.zip`: Zip archive containing the FastQC report, tab-delimited data file and plot images. + +
+ +[FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) gives general quality metrics about your sequenced reads. It provides information about the quality score distribution across your reads, per base sequence content (%A/T/G/C), adapter contamination and overrepresented sequences. For further reading and documentation see the [FastQC help pages](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/). + +![MultiQC - FastQC sequence counts plot](images/mqc_fastqc_counts.png) + +![MultiQC - FastQC mean quality scores plot](images/mqc_fastqc_quality.png) + +![MultiQC - FastQC adapter content plot](images/mqc_fastqc_adapter.png) + +> **NB:** The FastQC plots displayed in the MultiQC report shows _untrimmed_ reads. They may contain adapter sequence and potentially regions with low quality. + +### Adapters + +
+Output files + +- `preprocessing/adapters/` + - `*_overrepresented.fasta`: Contains overrepresented sequences found by FastQC + +
+ +[FastQC](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) finds over-represented sequences in samples. It lists all of the sequence which make up more than 0.1% of the total reads. For each over-represented sequence the program will look for matches in a database of common contaminants and will report the best hit it finds. Hits must be at least 20bp in length and have no more than 1 mismatch. + +### Cutadapt + +
+Output files + +- `preprocessing/cutadapt/` + - `*.cutadapt.log`: Cutadapt log file + - `*.trim.fastq.gz`: Sample reads trimmed with overrepresented sequences removed + +
+ +### Seqtk + +
+Output files + +- `preprocessing/seqtk/` + - `*.seqtk-seq.fastq.gz`: Quality filtered reads. + +
+ +[Seqtk](https://github.com/lh3/seqtk) masks (converts to Ns) bases with quality lower than 20 and removes sequences shorter than 80 bases. + + + +## Mapping + +### minimap2 + +
+Output files + +- `minimap2/` + - `*.bam`: BAM file containing aligned reads + - `*.bai`: BAI index + +
+ +[Minimap2](https://github.com/lh3/minimap2) is a sequence alignment program that aligns DNA sequences against a reference database. + +### BWA + +
+Output files + +- `bwa/` + - `*.bam`: BAM file containing aligned reads + - `*.bai`: BAI index + +
+ +[BWA-MEM](https://github.com/lh3/bwa) BWA is a software package for mapping low-divergent sequences against a reference genome. + +### bowtie2 + +
+Output files + +- `bowtie2/` + - `*.bam`: BAM file containing aligned reads + - `*.bai`: BAI index + +
+ +[Bowtie2](https://bowtie-bio.sourceforge.net/bowtie2/index.shtml) aligns sequencing reads to reference sequences. + +## Edits calling + +### CIGAR + +
+Output files + +- `cigar/` + - `*_cutSite.json`: Contains the protospacer cut site position in the reference. + - `*_edition.html`: Interactive pie chart with the percentage of edition types. Reads are classified between WT (without an edit) and indels. Indes are divided between deletions, insertions and delins (deletion + insertion). Deletions and insertions can be out of frame or in frame. + ![Test sample hCas9-AAVS1-a edition plot](images/hCas9-AAVS1-a_edition.png) + - `*_edits.csv`: Table containing the data visualized in the pie chart. + - `*_indels.csv`: Table containing information of all reads. Edit type, edit start and length, if the edition happens above the error rate, if it's located into the common edit window, the frequency, the percentage, the pattern, surrounding nucleotides in case of insertions, the protospacer cut site, the sample id, number of aligned reads and number of reads with and without a template modification. + - `*_QC-indels.html`: Interactive pie chart with information about aligned reads. Reads are classified between WT and containing indels. Both types are classified between passing the filtering steps or not. Indel reads passing the filtering steps are divided in reads with a modification above the error rate and located in the common edit window, above the error rate but not in the edit region, viceversa, or any of those conditions. + ![Test sample hCas9-AAVS1-a QC indels plot](images/hCas9-AAVS1-a_QC-indels.png) + - `*_reads.html`: Interactive pie chart with percentage of the number of raw reads, reads merged with Pear, reads passing quality filters and UMI clustered reads. + ![Test sample hCas9-AAVS1-a reads plot](images/hCas9-AAVS1-a_reads.png) + - `*_subs-perc.csv`: Table containing the percentage of each nucleotide found for each reference position. + +
+ +## MultiQC + +
+Output files + +- `multiqc/` + - `multiqc_report.html`: a standalone HTML file that can be viewed in your web browser. + - `multiqc_data/`: directory containing parsed statistics from the different tools used in the pipeline. + - `multiqc_plots/`: directory containing static images from the report in various formats. + +
+ +[MultiQC](http://multiqc.info) is a visualization tool that generates a single HTML report summarising all samples in your project. Most of the pipeline QC results are visualised in the report and further statistics are available in the report data directory. + +Results generated by MultiQC collate pipeline QC from supported tools e.g. FastQC. The pipeline has special steps which also allow the software versions to be reported in the MultiQC output for future traceability. For more information about how to use MultiQC reports, see . + +## Pipeline information + +
+Output files + +- `pipeline_info/` + - Reports generated by Nextflow: `execution_report.html`, `execution_timeline.html`, `execution_trace.txt` and `pipeline_dag.dot`/`pipeline_dag.svg`. + - Reports generated by the pipeline: `pipeline_report.html`, `pipeline_report.txt` and `software_versions.yml`. The `pipeline_report*` files will only be present if the `--email` / `--email_on_fail` parameter's are used when running the pipeline. + - Reformatted samplesheet files used as input to the pipeline: `samplesheet.valid.csv`. + +
+ +[Nextflow](https://www.nextflow.io/docs/latest/tracing.html) provides excellent functionality for generating various reports relevant to the running and execution of the pipeline. This will allow you to troubleshoot errors with the running of the pipeline, and also provide you with other information such as launch commands, run times and resource usage. diff --git a/docs/usage.md b/docs/usage.md index 2325df0b..1e9a8187 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -12,263 +12,6 @@ The **nf-core/crisprseq** pipeline allows the analysis of CRISPR edited DNA. It The `--analysis` parameter specifies whether the user intends to perform editing or screening in the crisprseq pipeline. - -## Samplesheet input - -You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 6 columns, and a header row as shown in the examples below. - -```bash ---input '[path to samplesheet file]' -``` - -### Multiple runs of the same sample - -The `sample` identifiers have to be the same when you have re-sequenced the same sample more than once e.g. to increase sequencing depth. The pipeline will concatenate the raw reads before performing any downstream analysis. Below is an example for the same sample sequenced across 3 lanes _(see section below for an explanation of samplesheet columns)_: - -```console -sample,fastq_1,fastq_2,reference,protospacer,template -CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,GCT...CCT,GGGGCCACTAGGGACAGGAT, -CONTROL_REP1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz,GCT...CCT,GGGGCCACTAGGGACAGGAT, -CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz,GCT...CCT,GGGGCCACTAGGGACAGGAT, -``` - -### Full samplesheet - -The pipeline will auto-detect whether a sample is single- or paired-end using the information provided in the samplesheet. The samplesheet can have as many columns as you desire, however, there is a strict requirement for the first 6 columns to match those defined in the table below. - -A final samplesheet file consisting of both single- and paired-end data may look something like the one below. This is for 3 samples, where `chr6` is single-end and has a template sequence _(this is a reduced samplesheet, please refer to the [pipeline example saplesheet](https://nf-co.re/crisprseq/1.0/assets/samplesheet.csv) to see the full version)_. - -```console -sample,fastq_1,fastq_2,reference,protospacer,template -hCas9-TRAC-a,hCas9-TRAC-a_R1.fastq.gz,hCas9-TRAC-a_R2.fastq.gz,GCT...CCT,GGGGCCACTAGGGACAGGAT, -hCas9-AAVS1-a,hCas9-AAVS1-a_R1.fastq.gz,hCas9-AAVS1-a_R2.fastq.gz,GCT...CCT,GGGGCCACTAGGGACAGGAT, -chr6,chr6-61942198-61942498_R1.fastq.gz,,CAA...GGA,TTTTATGATATTTATCTTTT,TTC...CAA -``` - -| Column | Description | -| ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `sample` | Custom sample name. This entry will be identical for multiple sequencing libraries/runs from the same sample. Spaces in sample names are automatically converted to underscores (`_`). | -| `fastq_1` | Full path to FastQ file for Illumina short reads 1. File has to be gzipped and have the extension ".fastq.gz" or ".fq.gz". | -| `fastq_2` | Full path to FastQ file for Illumina short reads 2. File has to be gzipped and have the extension ".fastq.gz" or ".fq.gz". | -| `reference` | Reference sequence of the target region. | -| `protospacer` | Sequence of the protospacer used for CRISPR editing. Must not includ the PAM. | -| `template` | Sequence of the template used in templet-based editing experiments. | - -An [example samplesheet](https://nf-co.re/crisprseq/1.0/assets/samplesheet.csv) has been provided with the pipeline. - -## Running the pipeline - -The typical command for running the pipeline is as follows: - -```bash -nextflow run nf-core/crisprseq --input samplesheet.csv --outdir -profile docker -``` - -This will launch the pipeline with the `docker` configuration profile. See below for more information about profiles. - -Note that the pipeline will create the following files in your working directory: - -```bash -work # Directory containing the nextflow working files - # Finished results in specified location (defined with --outdir) -.nextflow_log # Log file from Nextflow -# Other nextflow hidden files, eg. history of pipeline runs and old logs. -``` - -### Updating the pipeline - -When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you're running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline: - -```bash -nextflow pull nf-core/crisprseq -``` - -### Reproducibility - -It is a good idea to specify a pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you'll be running the same version of the pipeline, even if there have been changes to the code since. - -First, go to the [nf-core/crisprseq releases page](https://github.com/nf-core/crisprseq/releases) and find the latest pipeline version - numeric only (eg. `1.3.1`). Then specify this when running the pipeline with `-r` (one hyphen) - eg. `-r 1.3.1`. Of course, you can switch to another version by changing the number after the `-r` flag. - -This version number will be logged in reports when you run the pipeline, so that you'll know what you used when you look back in the future. For example, at the bottom of the MultiQC reports. - -## Core Nextflow arguments - -> **NB:** These options are part of Nextflow and use a _single_ hyphen (pipeline parameters use a double-hyphen). - -### `-profile` - -Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments. - -Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Conda) - see below. - -> We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported. - -The pipeline also dynamically loads configurations from [https://github.com/nf-core/configs](https://github.com/nf-core/configs) when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to see if your system is available in these configs please see the [nf-core/configs documentation](https://github.com/nf-core/configs#documentation). - -Note that multiple profiles can be loaded, for example: `-profile test,docker` - the order of arguments is important! -They are loaded in sequence, so later profiles can overwrite earlier profiles. - -If `-profile` is not specified, the pipeline will run locally and expect all software to be installed and available on the `PATH`. This is _not_ recommended, since it can lead to different results on different machines dependent on the computer enviroment. - -- `test` - - A profile with a complete configuration for automated testing - - Includes links to test data so needs no other parameters -- `docker` - - A generic configuration profile to be used with [Docker](https://docker.com/) -- `singularity` - - A generic configuration profile to be used with [Singularity](https://sylabs.io/docs/) -- `podman` - - A generic configuration profile to be used with [Podman](https://podman.io/) -- `shifter` - - A generic configuration profile to be used with [Shifter](https://nersc.gitlab.io/development/shifter/how-to-use/) -- `charliecloud` - - A generic configuration profile to be used with [Charliecloud](https://hpc.github.io/charliecloud/) -- `conda` - - A generic configuration profile to be used with [Conda](https://conda.io/docs/). Please only use Conda as a last resort i.e. when it's not possible to run the pipeline with Docker, Singularity, Podman, Shifter or Charliecloud. - -### `-resume` - -Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files' contents as well. For more info about this parameter, see [this blog post](https://www.nextflow.io/blog/2019/demystifying-nextflow-resume.html). - -You can also supply a run name to resume a specific run: `-resume [run-name]`. Use the `nextflow log` command to show previous run names. - -### `-c` - -Specify the path to a specific config file (this is a core Nextflow command). See the [nf-core website documentation](https://nf-co.re/usage/configuration) for more information. - -## Custom configuration - -### Resource requests - -Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the steps in the pipeline, if the job exits with any of the error codes specified [here](https://github.com/nf-core/rnaseq/blob/4c27ef5610c87db00c3c5a3eed10b1d161abf575/conf/base.config#L18) it will automatically be resubmitted with higher requests (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped. - -For example, if the nf-core/rnaseq pipeline is failing after multiple re-submissions of the `STAR_ALIGN` process due to an exit code of `137` this would indicate that there is an out of memory issue: - -```console -[62/149eb0] NOTE: Process `NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137) -- Execution is retried (1) -Error executing process > 'NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)' - -Caused by: - Process `NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137) - -Command executed: - STAR \ - --genomeDir star \ - --readFilesIn WT_REP1_trimmed.fq.gz \ - --runThreadN 2 \ - --outFileNamePrefix WT_REP1. \ - - -Command exit status: - 137 - -Command output: - (empty) - -Command error: - .command.sh: line 9: 30 Killed STAR --genomeDir star --readFilesIn WT_REP1_trimmed.fq.gz --runThreadN 2 --outFileNamePrefix WT_REP1. -Work dir: - /home/pipelinetest/work/9d/172ca5881234073e8d76f2a19c88fb - -Tip: you can replicate the issue by changing to the process work dir and entering the command `bash .command.run` -``` - -#### For beginners - -A first step to bypass this error, you could try to increase the amount of CPUs, memory, and time for the whole pipeline. Therefor you can try to increase the resource for the parameters `--max_cpus`, `--max_memory`, and `--max_time`. Based on the error above, you have to increase the amount of memory. Therefore you can go to the [parameter documentation of crisprseq](https://nf-co.re/rnaseq/1.0.0/parameters) and scroll down to the `show hidden parameter` button to get the default value for `--max_memory`. In this case 128GB, you than can try to run your pipeline again with `--max_memory 200GB -resume` to skip all process, that were already calculated. If you can not increase the resource of the complete pipeline, you can try to adapt the resource for a single process as mentioned below. - -#### Advanced option on process level - -To bypass this error you would need to find exactly which resources are set by the `STAR_ALIGN` process. The quickest way is to search for `process STAR_ALIGN` in the [nf-core/rnaseq Github repo](https://github.com/nf-core/rnaseq/search?q=process+STAR_ALIGN). -We have standardised the structure of Nextflow DSL2 pipelines such that all module files will be present in the `modules/` directory and so, based on the search results, the file we want is `modules/nf-core/star/align/main.nf`. -If you click on the link to that file you will notice that there is a `label` directive at the top of the module that is set to [`label process_high`](https://github.com/nf-core/rnaseq/blob/4c27ef5610c87db00c3c5a3eed10b1d161abf575/modules/nf-core/software/star/align/main.nf#L9). -The [Nextflow `label`](https://www.nextflow.io/docs/latest/process.html#label) directive allows us to organise workflow processes in separate groups which can be referenced in a configuration file to select and configure subset of processes having similar computing requirements. -The default values for the `process_high` label are set in the pipeline's [`base.config`](https://github.com/nf-core/rnaseq/blob/4c27ef5610c87db00c3c5a3eed10b1d161abf575/conf/base.config#L33-L37) which in this case is defined as 72GB. -Providing you haven't set any other standard nf-core parameters to **cap** the [maximum resources](https://nf-co.re/usage/configuration#max-resources) used by the pipeline then we can try and bypass the `STAR_ALIGN` process failure by creating a custom config file that sets at least 72GB of memory, in this case increased to 100GB. -The custom config below can then be provided to the pipeline via the [`-c`](#-c) parameter as highlighted in previous sections. - -```nextflow -process { - withName: 'NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN' { - memory = 100.GB - } -} -``` - -> **NB:** We specify the full process name i.e. `NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN` in the config file because this takes priority over the short name (`STAR_ALIGN`) and allows existing configuration using the full process name to be correctly overridden. -> -> If you get a warning suggesting that the process selector isn't recognised check that the process name has been specified correctly. - -### Updating containers (advanced users) - -The [Nextflow DSL2](https://www.nextflow.io/docs/latest/dsl2.html) implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. If for some reason you need to use a different version of a particular tool with the pipeline then you just need to identify the `process` name and override the Nextflow `container` definition for that process using the `withName` declaration. For example, in the [nf-core/viralrecon](https://nf-co.re/viralrecon) pipeline a tool called [Pangolin](https://github.com/cov-lineages/pangolin) has been used during the COVID-19 pandemic to assign lineages to SARS-CoV-2 genome sequenced samples. Given that the lineage assignments change quite frequently it doesn't make sense to re-release the nf-core/viralrecon everytime a new version of Pangolin has been released. However, you can override the default container used by the pipeline by creating a custom config file and passing it as a command-line argument via `-c custom.config`. - -1. Check the default version used by the pipeline in the module file for [Pangolin](https://github.com/nf-core/viralrecon/blob/a85d5969f9025409e3618d6c280ef15ce417df65/modules/nf-core/software/pangolin/main.nf#L14-L19) -2. Find the latest version of the Biocontainer available on [Quay.io](https://quay.io/repository/biocontainers/pangolin?tag=latest&tab=tags) -3. Create the custom config accordingly: - - - For Docker: - - ```nextflow - process { - withName: PANGOLIN { - container = 'quay.io/biocontainers/pangolin:3.0.5--pyhdfd78af_0' - } - } - ``` - - - For Singularity: - - ```nextflow - process { - withName: PANGOLIN { - container = 'https://depot.galaxyproject.org/singularity/pangolin:3.0.5--pyhdfd78af_0' - } - } - ``` - - - For Conda: - - ```nextflow - process { - withName: PANGOLIN { - conda = 'bioconda::pangolin=3.0.5' - } - } - ``` - -> **NB:** If you wish to periodically update individual tool-specific results (e.g. Pangolin) generated by the pipeline then you must ensure to keep the `work/` directory otherwise the `-resume` ability of the pipeline will be compromised and it will restart from scratch. - -### nf-core/configs - -In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the `nf-core/configs` git repository. Before you do this please can you test that the config file works with your pipeline of choice using the `-c` parameter. You can then create a pull request to the `nf-core/configs` repository with the addition of your config file, associated documentation file (see examples in [`nf-core/configs/docs`](https://github.com/nf-core/configs/tree/master/docs)), and amending [`nfcore_custom.config`](https://github.com/nf-core/configs/blob/master/nfcore_custom.config) to include your custom profile. - -See the main [Nextflow documentation](https://www.nextflow.io/docs/latest/config.html) for more information about creating your own configuration files. - -If you have any questions or issues please send us a message on [Slack](https://nf-co.re/join/slack) on the [`#configs` channel](https://nfcore.slack.com/channels/configs). - -## Azure Resource Requests - -To be used with the `azurebatch` profile by specifying the `-profile azurebatch`. -We recommend providing a compute `params.vm_type` of `Standard_D16_v3` VMs by default but these options can be changed if required. - -Note that the choice of VM size depends on your quota and the overall workload during the analysis. -For a thorough list, please refer the [Azure Sizes for virtual machines in Azure](https://docs.microsoft.com/en-us/azure/virtual-machines/sizes). - -## Running in the background - -Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished. - -The Nextflow `-bg` flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file. - -Alternatively, you can use `screen` / `tmux` or similar tool to create a detached session which you can log back into at a later time. -Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs). - -## Nextflow memory requirements - -In some cases, the Nextflow Java virtual machines can start to request a large amount of memory. -We recommend adding the following line to your environment to limit this (typically in `~/.bashrc` or `~./bash_profile`): - -```bash -NXF_OPTS='-Xms1g -Xmx4g' -``` +Please refer to the respective Usage documentation: +- [Usage targeted](usage-targeted) +- [Usage screening](usage-screening) diff --git a/docs/usage_targeted.md b/docs/usage_targeted.md new file mode 100644 index 00000000..c039764b --- /dev/null +++ b/docs/usage_targeted.md @@ -0,0 +1,270 @@ +# nf-core/crisprseq: Usage + +## :warning: Please read this documentation on the nf-core website: [https://nf-co.re/crisprseq/usage](https://nf-co.re/crisprseq/usage) + +> _Documentation of pipeline parameters is generated automatically from the pipeline schema and can no longer be found in markdown files._ + +## Introduction + +The **nf-core/crisprseq** pipeline allows the analysis of CRISPR edited DNA. It evaluates the quality of gene editing experiments using targeted next generation sequencing (NGS) data. + + +## Samplesheet input + +You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 6 columns, and a header row as shown in the examples below. + +```bash +--input '[path to samplesheet file]' +``` + +### Multiple runs of the same sample + +The `sample` identifiers have to be the same when you have re-sequenced the same sample more than once e.g. to increase sequencing depth. The pipeline will concatenate the raw reads before performing any downstream analysis. Below is an example for the same sample sequenced across 3 lanes _(see section below for an explanation of samplesheet columns)_: + +```console +sample,fastq_1,fastq_2,reference,protospacer,template +CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,GCT...CCT,GGGGCCACTAGGGACAGGAT, +CONTROL_REP1,AEG588A1_S1_L003_R1_001.fastq.gz,AEG588A1_S1_L003_R2_001.fastq.gz,GCT...CCT,GGGGCCACTAGGGACAGGAT, +CONTROL_REP1,AEG588A1_S1_L004_R1_001.fastq.gz,AEG588A1_S1_L004_R2_001.fastq.gz,GCT...CCT,GGGGCCACTAGGGACAGGAT, +``` + +### Full samplesheet + +The pipeline will auto-detect whether a sample is single- or paired-end using the information provided in the samplesheet. The samplesheet can have as many columns as you desire, however, there is a strict requirement for the first 6 columns to match those defined in the table below. + +A final samplesheet file consisting of both single- and paired-end data may look something like the one below. This is for 3 samples, where `chr6` is single-end and has a template sequence _(this is a reduced samplesheet, please refer to the [pipeline example saplesheet](https://nf-co.re/crisprseq/1.0/assets/samplesheet.csv) to see the full version)_. + +```console +sample,fastq_1,fastq_2,reference,protospacer,template +hCas9-TRAC-a,hCas9-TRAC-a_R1.fastq.gz,hCas9-TRAC-a_R2.fastq.gz,GCT...CCT,GGGGCCACTAGGGACAGGAT, +hCas9-AAVS1-a,hCas9-AAVS1-a_R1.fastq.gz,hCas9-AAVS1-a_R2.fastq.gz,GCT...CCT,GGGGCCACTAGGGACAGGAT, +chr6,chr6-61942198-61942498_R1.fastq.gz,,CAA...GGA,TTTTATGATATTTATCTTTT,TTC...CAA +``` + +| Column | Description | +| ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `sample` | Custom sample name. This entry will be identical for multiple sequencing libraries/runs from the same sample. Spaces in sample names are automatically converted to underscores (`_`). | +| `fastq_1` | Full path to FastQ file for Illumina short reads 1. File has to be gzipped and have the extension ".fastq.gz" or ".fq.gz". | +| `fastq_2` | Full path to FastQ file for Illumina short reads 2. File has to be gzipped and have the extension ".fastq.gz" or ".fq.gz". | +| `reference` | Reference sequence of the target region. | +| `protospacer` | Sequence of the protospacer used for CRISPR editing. Must not includ the PAM. | +| `template` | Sequence of the template used in templet-based editing experiments. | + +An [example samplesheet](https://nf-co.re/crisprseq/1.0/assets/samplesheet.csv) has been provided with the pipeline. + +## Running the pipeline + +The typical command for running the pipeline is as follows: + +```bash +nextflow run nf-core/crisprseq --input samplesheet.csv --outdir -profile docker +``` + +This will launch the pipeline with the `docker` configuration profile. See below for more information about profiles. + +Note that the pipeline will create the following files in your working directory: + +```bash +work # Directory containing the nextflow working files + # Finished results in specified location (defined with --outdir) +.nextflow_log # Log file from Nextflow +# Other nextflow hidden files, eg. history of pipeline runs and old logs. +``` + +### Updating the pipeline + +When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you're running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline: + +```bash +nextflow pull nf-core/crisprseq +``` + +### Reproducibility + +It is a good idea to specify a pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you'll be running the same version of the pipeline, even if there have been changes to the code since. + +First, go to the [nf-core/crisprseq releases page](https://github.com/nf-core/crisprseq/releases) and find the latest pipeline version - numeric only (eg. `1.3.1`). Then specify this when running the pipeline with `-r` (one hyphen) - eg. `-r 1.3.1`. Of course, you can switch to another version by changing the number after the `-r` flag. + +This version number will be logged in reports when you run the pipeline, so that you'll know what you used when you look back in the future. For example, at the bottom of the MultiQC reports. + +## Core Nextflow arguments + +> **NB:** These options are part of Nextflow and use a _single_ hyphen (pipeline parameters use a double-hyphen). + +### `-profile` + +Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments. + +Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Conda) - see below. + +> We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported. + +The pipeline also dynamically loads configurations from [https://github.com/nf-core/configs](https://github.com/nf-core/configs) when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to see if your system is available in these configs please see the [nf-core/configs documentation](https://github.com/nf-core/configs#documentation). + +Note that multiple profiles can be loaded, for example: `-profile test,docker` - the order of arguments is important! +They are loaded in sequence, so later profiles can overwrite earlier profiles. + +If `-profile` is not specified, the pipeline will run locally and expect all software to be installed and available on the `PATH`. This is _not_ recommended, since it can lead to different results on different machines dependent on the computer enviroment. + +- `test` + - A profile with a complete configuration for automated testing + - Includes links to test data so needs no other parameters +- `docker` + - A generic configuration profile to be used with [Docker](https://docker.com/) +- `singularity` + - A generic configuration profile to be used with [Singularity](https://sylabs.io/docs/) +- `podman` + - A generic configuration profile to be used with [Podman](https://podman.io/) +- `shifter` + - A generic configuration profile to be used with [Shifter](https://nersc.gitlab.io/development/shifter/how-to-use/) +- `charliecloud` + - A generic configuration profile to be used with [Charliecloud](https://hpc.github.io/charliecloud/) +- `conda` + - A generic configuration profile to be used with [Conda](https://conda.io/docs/). Please only use Conda as a last resort i.e. when it's not possible to run the pipeline with Docker, Singularity, Podman, Shifter or Charliecloud. + +### `-resume` + +Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files' contents as well. For more info about this parameter, see [this blog post](https://www.nextflow.io/blog/2019/demystifying-nextflow-resume.html). + +You can also supply a run name to resume a specific run: `-resume [run-name]`. Use the `nextflow log` command to show previous run names. + +### `-c` + +Specify the path to a specific config file (this is a core Nextflow command). See the [nf-core website documentation](https://nf-co.re/usage/configuration) for more information. + +## Custom configuration + +### Resource requests + +Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the steps in the pipeline, if the job exits with any of the error codes specified [here](https://github.com/nf-core/rnaseq/blob/4c27ef5610c87db00c3c5a3eed10b1d161abf575/conf/base.config#L18) it will automatically be resubmitted with higher requests (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped. + +For example, if the nf-core/rnaseq pipeline is failing after multiple re-submissions of the `STAR_ALIGN` process due to an exit code of `137` this would indicate that there is an out of memory issue: + +```console +[62/149eb0] NOTE: Process `NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137) -- Execution is retried (1) +Error executing process > 'NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)' + +Caused by: + Process `NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN (WT_REP1)` terminated with an error exit status (137) + +Command executed: + STAR \ + --genomeDir star \ + --readFilesIn WT_REP1_trimmed.fq.gz \ + --runThreadN 2 \ + --outFileNamePrefix WT_REP1. \ + + +Command exit status: + 137 + +Command output: + (empty) + +Command error: + .command.sh: line 9: 30 Killed STAR --genomeDir star --readFilesIn WT_REP1_trimmed.fq.gz --runThreadN 2 --outFileNamePrefix WT_REP1. +Work dir: + /home/pipelinetest/work/9d/172ca5881234073e8d76f2a19c88fb + +Tip: you can replicate the issue by changing to the process work dir and entering the command `bash .command.run` +``` + +#### For beginners + +A first step to bypass this error, you could try to increase the amount of CPUs, memory, and time for the whole pipeline. Therefor you can try to increase the resource for the parameters `--max_cpus`, `--max_memory`, and `--max_time`. Based on the error above, you have to increase the amount of memory. Therefore you can go to the [parameter documentation of crisprseq](https://nf-co.re/rnaseq/1.0.0/parameters) and scroll down to the `show hidden parameter` button to get the default value for `--max_memory`. In this case 128GB, you than can try to run your pipeline again with `--max_memory 200GB -resume` to skip all process, that were already calculated. If you can not increase the resource of the complete pipeline, you can try to adapt the resource for a single process as mentioned below. + +#### Advanced option on process level + +To bypass this error you would need to find exactly which resources are set by the `STAR_ALIGN` process. The quickest way is to search for `process STAR_ALIGN` in the [nf-core/rnaseq Github repo](https://github.com/nf-core/rnaseq/search?q=process+STAR_ALIGN). +We have standardised the structure of Nextflow DSL2 pipelines such that all module files will be present in the `modules/` directory and so, based on the search results, the file we want is `modules/nf-core/star/align/main.nf`. +If you click on the link to that file you will notice that there is a `label` directive at the top of the module that is set to [`label process_high`](https://github.com/nf-core/rnaseq/blob/4c27ef5610c87db00c3c5a3eed10b1d161abf575/modules/nf-core/software/star/align/main.nf#L9). +The [Nextflow `label`](https://www.nextflow.io/docs/latest/process.html#label) directive allows us to organise workflow processes in separate groups which can be referenced in a configuration file to select and configure subset of processes having similar computing requirements. +The default values for the `process_high` label are set in the pipeline's [`base.config`](https://github.com/nf-core/rnaseq/blob/4c27ef5610c87db00c3c5a3eed10b1d161abf575/conf/base.config#L33-L37) which in this case is defined as 72GB. +Providing you haven't set any other standard nf-core parameters to **cap** the [maximum resources](https://nf-co.re/usage/configuration#max-resources) used by the pipeline then we can try and bypass the `STAR_ALIGN` process failure by creating a custom config file that sets at least 72GB of memory, in this case increased to 100GB. +The custom config below can then be provided to the pipeline via the [`-c`](#-c) parameter as highlighted in previous sections. + +```nextflow +process { + withName: 'NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN' { + memory = 100.GB + } +} +``` + +> **NB:** We specify the full process name i.e. `NFCORE_RNASEQ:RNASEQ:ALIGN_STAR:STAR_ALIGN` in the config file because this takes priority over the short name (`STAR_ALIGN`) and allows existing configuration using the full process name to be correctly overridden. +> +> If you get a warning suggesting that the process selector isn't recognised check that the process name has been specified correctly. + +### Updating containers (advanced users) + +The [Nextflow DSL2](https://www.nextflow.io/docs/latest/dsl2.html) implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. If for some reason you need to use a different version of a particular tool with the pipeline then you just need to identify the `process` name and override the Nextflow `container` definition for that process using the `withName` declaration. For example, in the [nf-core/viralrecon](https://nf-co.re/viralrecon) pipeline a tool called [Pangolin](https://github.com/cov-lineages/pangolin) has been used during the COVID-19 pandemic to assign lineages to SARS-CoV-2 genome sequenced samples. Given that the lineage assignments change quite frequently it doesn't make sense to re-release the nf-core/viralrecon everytime a new version of Pangolin has been released. However, you can override the default container used by the pipeline by creating a custom config file and passing it as a command-line argument via `-c custom.config`. + +1. Check the default version used by the pipeline in the module file for [Pangolin](https://github.com/nf-core/viralrecon/blob/a85d5969f9025409e3618d6c280ef15ce417df65/modules/nf-core/software/pangolin/main.nf#L14-L19) +2. Find the latest version of the Biocontainer available on [Quay.io](https://quay.io/repository/biocontainers/pangolin?tag=latest&tab=tags) +3. Create the custom config accordingly: + + - For Docker: + + ```nextflow + process { + withName: PANGOLIN { + container = 'quay.io/biocontainers/pangolin:3.0.5--pyhdfd78af_0' + } + } + ``` + + - For Singularity: + + ```nextflow + process { + withName: PANGOLIN { + container = 'https://depot.galaxyproject.org/singularity/pangolin:3.0.5--pyhdfd78af_0' + } + } + ``` + + - For Conda: + + ```nextflow + process { + withName: PANGOLIN { + conda = 'bioconda::pangolin=3.0.5' + } + } + ``` + +> **NB:** If you wish to periodically update individual tool-specific results (e.g. Pangolin) generated by the pipeline then you must ensure to keep the `work/` directory otherwise the `-resume` ability of the pipeline will be compromised and it will restart from scratch. + +### nf-core/configs + +In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings regularly it may be a good idea to request that your custom config file is uploaded to the `nf-core/configs` git repository. Before you do this please can you test that the config file works with your pipeline of choice using the `-c` parameter. You can then create a pull request to the `nf-core/configs` repository with the addition of your config file, associated documentation file (see examples in [`nf-core/configs/docs`](https://github.com/nf-core/configs/tree/master/docs)), and amending [`nfcore_custom.config`](https://github.com/nf-core/configs/blob/master/nfcore_custom.config) to include your custom profile. + +See the main [Nextflow documentation](https://www.nextflow.io/docs/latest/config.html) for more information about creating your own configuration files. + +If you have any questions or issues please send us a message on [Slack](https://nf-co.re/join/slack) on the [`#configs` channel](https://nfcore.slack.com/channels/configs). + +## Azure Resource Requests + +To be used with the `azurebatch` profile by specifying the `-profile azurebatch`. +We recommend providing a compute `params.vm_type` of `Standard_D16_v3` VMs by default but these options can be changed if required. + +Note that the choice of VM size depends on your quota and the overall workload during the analysis. +For a thorough list, please refer the [Azure Sizes for virtual machines in Azure](https://docs.microsoft.com/en-us/azure/virtual-machines/sizes). + +## Running in the background + +Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished. + +The Nextflow `-bg` flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file. + +Alternatively, you can use `screen` / `tmux` or similar tool to create a detached session which you can log back into at a later time. +Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs). + +## Nextflow memory requirements + +In some cases, the Nextflow Java virtual machines can start to request a large amount of memory. +We recommend adding the following line to your environment to limit this (typically in `~/.bashrc` or `~./bash_profile`): + +```bash +NXF_OPTS='-Xms1g -Xmx4g' +``` From bb56c3556e7e2cf292844121c019f676517c292f Mon Sep 17 00:00:00 2001 From: nf-core-bot Date: Thu, 8 Jun 2023 07:28:36 +0000 Subject: [PATCH 4/4] [automated] Fix linting with Prettier --- docs/output.md | 1 + docs/usage.md | 1 + docs/usage_targeted.md | 1 - 3 files changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/output.md b/docs/output.md index 19740483..1731e563 100644 --- a/docs/output.md +++ b/docs/output.md @@ -5,5 +5,6 @@ This document describes the output produced by the pipeline. Please refer to the respective Output documentation: + - [Output targeted](output-targeted) - [Output screening](output-screening) diff --git a/docs/usage.md b/docs/usage.md index 1e9a8187..a8bbe4d8 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -13,5 +13,6 @@ The **nf-core/crisprseq** pipeline allows the analysis of CRISPR edited DNA. It The `--analysis` parameter specifies whether the user intends to perform editing or screening in the crisprseq pipeline. Please refer to the respective Usage documentation: + - [Usage targeted](usage-targeted) - [Usage screening](usage-screening) diff --git a/docs/usage_targeted.md b/docs/usage_targeted.md index c039764b..f6af7ff1 100644 --- a/docs/usage_targeted.md +++ b/docs/usage_targeted.md @@ -8,7 +8,6 @@ The **nf-core/crisprseq** pipeline allows the analysis of CRISPR edited DNA. It evaluates the quality of gene editing experiments using targeted next generation sequencing (NGS) data. - ## Samplesheet input You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 6 columns, and a header row as shown in the examples below.