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Copy file name to clipboardExpand all lines: CHANGELOG.md
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@@ -7,7 +7,8 @@ and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.
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### `Added`
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-[#583](https://github.com/nf-core/eager/issues/583) - mapDamage2 rescaling of BAM files to remove damage
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-[#640](https://github.com/nf-core/eager/issues/640) - Added a pre-metagenomic screening filtering of low-sequence complexity reads with `bbduk`
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-[#583](https://github.com/nf-core/eager/issues/583) - Added `mapDamage2` rescaling of BAM files to remove damage
2. Install any of [`Docker`](https://docs.docker.com/engine/installation/), [`Singularity`](https://www.sylabs.io/guides/3.0/user-guide/) or [`Podman`](https://podman.io/) for full pipeline reproducibility _(please only use [`Conda`](https://conda.io/miniconda.html) as a last resort; see [docs](https://nf-co.re/usage/configuration#basic-configuration-profiles))_
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3. Download the pipeline and test it on a minimal dataset with a single command:
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```bash
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nextflow run nf-core/eager -profile test,<docker/singularity/podman/conda/institute>
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```
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> Please check [nf-core/configs](https://github.com/nf-core/configs#documentation) to see if a custom config file to run nf-core pipelines already exists foryour Institute. If so, you can simply use `-profile <institute>`in your command. This will enable either `docker` or `singularity` and set the appropriate execution settings for your local compute environment.
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4. Start running your own analysis!
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```bash
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nextflow run nf-core/eager -profile <docker/singularity/conda> --input '*_R{1,2}.fastq.gz' --fasta '<your_reference>.fasta'
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```
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5. Once your run has completed successfully, clean up the intermediate files.
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```bash
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nextflow clean -f -k
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```
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See [usage docs](https://nf-co.re/eager/docs/usage.md) for all of the available options when running the pipeline.
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**N.B.** You can see an overview of the run in the MultiQC report located at `./results/MultiQC/multiqc_report.html`
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Modifications to the default pipeline are easily made using various options as described in the documentation.
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## Pipeline Summary
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### Default Steps
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#### Metagenomic Screening
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* Low-sequenced complexity filtering (`BBduk`)
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* Taxonomic binner with alignment (`MALT`)
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* Taxonomic binner without alignment (`Kraken2`)
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* aDNA characteristic screening of taxonomically binned data from MALT (`MaltExtract`)
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<img src="docs/images/output/overview/eager2_metromap_complex.png" alt="nf-core/eager metro map" width="70%"
2. Install any of [`Docker`](https://docs.docker.com/engine/installation/), [`Singularity`](https://www.sylabs.io/guides/3.0/user-guide/) or [`Podman`](https://podman.io/) for full pipeline reproducibility _(please only use [`Conda`](https://conda.io/miniconda.html) as a last resort; see [docs](https://nf-co.re/usage/configuration#basic-configuration-profiles))_
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3. Download the pipeline and test it on a minimal dataset with a single command:
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```bash
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nextflow run nf-core/eager -profile test,<docker/singularity/podman/conda/institute>
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```
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> Please check [nf-core/configs](https://github.com/nf-core/configs#documentation) to see if a custom config file to run nf-core pipelines already exists foryour Institute. If so, you can simply use `-profile <institute>`in your command. This will enable either `docker` or `singularity` and set the appropriate execution settings for your local compute environment.
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4. Start running your own analysis!
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```bash
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nextflow run nf-core/eager -profile <docker/singularity/conda> --input '*_R{1,2}.fastq.gz' --fasta '<your_reference>.fasta'
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```
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5. Once your run has completed successfully, clean up the intermediate files.
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```bash
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nextflow clean -f -k
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```
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See [usage docs](https://nf-co.re/eager/docs/usage.md) for all of the available options when running the pipeline.
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**N.B.** You can see an overview of the run in the MultiQC report located at `./results/MultiQC/multiqc_report.html`
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Modifications to the default pipeline are easily made using various options
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as described in the documentation.
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## Pipeline Summary
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By default, the pipeline currently performs the following:
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<!-- TODO nf-core: Fill in short bullet-pointed list of default steps of pipeline -->
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* Sequencing quality control (`FastQC`)
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* Overall pipeline run summaries (`MultiQC`)
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## Documentation
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The nf-core/eager pipeline comes with documentation about the pipeline: [usage](https://nf-co.re/eager/usage) and [output](https://nf-co.re/eager/output).
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* **sequenceTools** Stephan Schiffels (Unpublished). Download: [https://github.com/stschiff/sequenceTools](https://github.com/stschiff/sequenceTools)
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* **EigenstratDatabaseTools** Thiseas C. Lamnidis (Unpublished). Download: [https://github.com/TCLamnidis/EigenStratDatabaseTools.git](https://github.com/TCLamnidis/EigenStratDatabaseTools.git)
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* **mapDamage2** Jónsson, H., et al 2013. mapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics , 29(13), 1682–1684. [https://doi.org/10.1093/bioinformatics/btt193](https://doi.org/10.1093/bioinformatics/btt193)
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* **BBduk** Brian Bushnell (Unpublished). Download: [https://sourceforge.net/projects/bbmap/](sourceforge.net/projects/bbmap/)
Copy file name to clipboardExpand all lines: docs/output.md
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@@ -664,6 +664,7 @@ Each module has it's own output directory which sit alongside the `MultiQC/` dir
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-`sex_determination/` - this contains the output for the sex determination run. This is a single `.tsv` file that includes a table with the sample name, the number of autosomal SNPs, number of SNPs on the X/Y chromosome, the number of reads mapping to the autosomes, the number of reads mapping to the X/Y chromosome, the relative coverage on the X/Y chromosomes, and the standard error associated with the relative coverages. These measures are provided for each bam file, one row per file. If the `sexdeterrmine_bedfile` option has not been provided, the error bars cannot be trusted, and runtime will be considerably longer.
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-`nuclear_contamination/` - this contains the output of the nuclear contamination processes. The directory contains one `*.X.contamination.out` file per individual, as well as `nuclear_contamination.txt` which is a summary table of the results for all individual. `nuclear_contamination.txt` contains a header, followed by one line per individual, comprised of the Method of Moments (MOM) and Maximum Likelihood (ML) contamination estimate (with their respective standard errors) for both Method1 and Method2.
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-`bedtools/` - this contains two files as the output from bedtools coverage. One file contains the 'breadth' coverage (`*.breadth.gz`). This file will have the contents of your annotation file (e.g. BED/GFF), and the following subsequent columns: no. reads on feature, # bases at depth, length of feature, and % of feature. The second file (`*.depth.gz`), contains the contents of your annotation file (e.g. BED/GFF), and an additional column which is mean depth coverage (i.e. average number of reads covering each position).
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-`metagenomic_complexity_filter` - this contains the output from filtering of input reads to metagenomic classification of low-sequence complexity reads as performed by `bbduk`. This will include the filtered FASTQ files (`*_lowcomplexityremoved.fq.gz`) and also the run-time log (`_bbduk.stats`) for each sample. **Note:** there are no sections in the MultiQC report for this module, therefore you must check the `._bbduk.stats` files to get summary statistics of the filtering.
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-`metagenomic_classification/` - this contains the output for a given metagenomic classifier.
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- Running MALT will contain RMA6 files that can be loaded into MEGAN6 or MaltExtract for phylogenetic visualisation of read taxonomic assignments and aDNA characteristics respectively. Additional a `malt.log` file is provided which gives additional information such as run-time, memory usage and per-sample statistics of numbers of alignments with taxonomic assignment etc. This will also include gzip SAM files if requested.
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- Running kraken will contain the Kraken output and report files, as well as a merged Taxon count table.
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