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Copy file name to clipboardExpand all lines: CHANGELOG.md
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The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/)
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and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html).
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## [2.3.0] - 2021-01-11 - "Aalen"
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### `Added`
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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
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- Updated usage (merging files) and workflow images reflecting new functionality.
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### `Fixed`
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- Removed leftover old DockerHub push CI commands.
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-[#627](https://github.com/nf-core/eager/issues/627) - Added de Barros Damgaard citation to README
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-[#630](https://github.com/nf-core/eager/pull/630) - Better handling of Qualimap memory requirements and error strategy.
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- Fixed some incomplete schema options to ensure users supply valid input values
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-[#638](https://github.com/nf-core/eager/issues/638#issuecomment-748877567) Fixed inverted circularfilter filtering (previously filtering would happen by default, not when requested by user as originally recorded in documentation)
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-[DeDup:](https://github.com/apeltzer/DeDup/commit/07d47868f10a6830da8c9161caa3755d9da155bf) Fixed Null Pointer Bug in DeDup by updating to 0.12.8 version
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-[#650](https://github.com/nf-core/eager/pull/650) - Increased memory given to FastQC for larger files by making it multithreaded
Copy file name to clipboardExpand all lines: README.md
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The pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible. The pipeline pre-processes raw data from FASTQ inputs, or preprocessed BAM inputs. It can align reads and performs extensive general NGS and aDNA specific quality-control on the results. It comes with docker, singularity or conda containers making installation trivial and results highly reproducible.
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_tsv,<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/podman/conda/institute> --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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A graphical overview of suggested routes through the pipeline depending on context can be seen below.
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<p align="center">
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<img src="docs/images/output/overview/eager2_metromap_complex.png" alt="nf-core/eager metro map" width="70%"
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<img src="docs/images/usage/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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* **Bowtie2** Langmead, B. and Salzberg, S. L. 2012 Fast gapped-read alignment with Bowtie 2. Nature methods, 9(4), p. 357–359. doi: [10.1038/nmeth.1923](https:/dx.doi.org/10.1038/nmeth.1923).
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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/)
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## Data References
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* Fellows Yates, J. A. et al. (2017) ‘Central European Woolly Mammoth Population Dynamics: Insights from Late Pleistocene Mitochondrial Genomes’, Scientific reports, 7(1), p. 17714. [doi: 10.1038/s41598-017-17723-1](https://doi.org/10.1038/s41598-017-17723-1).
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* Gamba, C. et al. (2014) ‘Genome flux and stasis in a five millennium transect of European prehistory’, Nature communications, 5, p. 5257. [doi: 10.1038/ncomms6257](https://doi.org/10.1038/ncomms6257).
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* Star, B. et al. (2017) ‘Ancient DNA reveals the Arctic origin of Viking Age cod from Haithabu, Germany’, Proceedings of the National Academy of Sciences of the United States of America, 114(34), pp. 9152–9157. [doi: 10.1073/pnas.1710186114](https://doi.org/10.1073/pnas.1710186114).
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* de Barros Damgaard, P. et al. (2018). '137 ancient human genomes from across the Eurasian steppes.', Nature, 557(7705), 369–374. [doi: 10.1038/s41586-018-0094-2](https://doi.org/10.1038/s41586-018-0094-2)
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