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# nf-core/mhcquant: Changelog
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## v2.1.0 nf-core/mhcquant "Olive Tin Hamster" - 2021/12/09
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### `Added`
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- Inclusion of assets/schema_input.json
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- Added the multiQC again to report the versions
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- MHCquant parameters are now directly assigned to the argument of the
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### `Fixed`
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- Fixed typos
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-[#165] - Raise memory requirements of FeatureFinderIdentification step
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-[#176] - Pipeline crashes when setting the --skip_quantification flag
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### `Dependencies`
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Note, since the pipeline is now using Nextflow DSL2, each process will be run with its own [Biocontainer](https://biocontainers.pro/#/registry). This means that on occasion it is entirely possible for the pipeline to be using different versions of the same tool. However, the overall software dependency changes compared to the last release have been listed below for reference.
> Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16. PubMed PMID: 27312411; PubMed Central PMCID: PMC5039924.
**Identify and quantify peptides from mass spectrometry raw data**.
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[](https://github.com/nf-core/mhcquant/actions?query=workflow%3A%22nf-core+CI%22)
[](https://nfcore.slack.com/channels/mhcquant)
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[](https://nfcore.slack.com/channels/mhcquant)
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[](https://twitter.com/nf_core)
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[](https://www.youtube.com/c/nf-core)
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## Introduction
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nfcore/mhcquant is a bioinformatics analysis pipeline used for quantitative processing of data dependent (DDA) peptidomics data.
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**nfcore/mhcquant** is a bioinformatics analysis pipeline used for quantitative processing of data dependent (DDA) peptidomics data.
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It was specifically designed to analyse immunopeptidomics data, which deals with the analysis of affinity purified, unspecifically cleaved peptides that have recently been discussed intensively in [the context of cancer vaccines](https://www.nature.com/articles/ncomms13404).
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The workflow is based on the OpenMS C++ framework for computational mass spectrometry. RAW files (mzML) serve as inputs and a database search (Comet) is performed based on a given input protein database. FDR rescoring is applied using Percolator based on a competitive target-decoy approach (reversed decoys). For label free quantification all input files undergo identification based retention time alignment (MapAlignerIdentification), and targeted feature extraction matching ids between runs (FeatureFinderIdentification). In addition, a variant calling file (vcf) can be specified to translate variants into proteins that will be included in the database search and binding predictions on specified alleles (alleles.tsv) using MHCFlurry (Class 1) or MHCNugget (Class 2) can be directly run on the output peptide lists. Moreover, if a vcf file was specified, neoepitopes will automatically be determined and binding predictions can also directly be predicted for them.
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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.
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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 uses Docker/Singularity containers making installation trivial and results highly reproducible. 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. Where possible, these processes have been submitted to and installed from [nf-core/modules](https://github.com/nf-core/modules) in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!
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On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the [nf-core website](https://nf-co.re/mhcquant/results).
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## Pipeline summary
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1. Present QC for raw reads ([`MultiQC`](http://multiqc.info/))
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(This chart was created with the help of [Lucidchart](https://www.lucidchart.com))
2. Install any of [`Docker`](https://docs.docker.com/engine/installation/), [`Singularity`](https://www.sylabs.io/guides/3.0/user-guide/), [`Podman`](https://podman.io/), [`Shifter`](https://nersc.gitlab.io/development/shifter/how-to-use/) or [`Charliecloud`](https://hpc.github.io/charliecloud/) 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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2. Install any of [`Docker`](https://docs.docker.com/engine/installation/), [`Singularity`](https://www.sylabs.io/guides/3.0/user-guide/), [`Podman`](https://podman.io/), [`Shifter`](https://nersc.gitlab.io/development/shifter/how-to-use/) or [`Charliecloud`](https://hpc.github.io/charliecloud/) 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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```console
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nextflow run nf-core/mhcquant -profile test,<docker/singularity/podman/shifter/charliecloud/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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> * 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 for your 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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> * If you are using `singularity` then the pipeline will auto-detect this and attempt to download the Singularity images directly as opposed to performing a conversion from Docker images. If you are persistently observing issues downloading Singularity images directly due to timeout or network issues then please use the `--singularity_pull_docker_container` parameter to pull and convert the Docker image instead. Alternatively, it is highly recommended to use the [`nf-core download`](https://nf-co.re/tools/#downloading-pipelines-for-offline-use) command to pre-download all of the required containers before running the pipeline and to set the [`NXF_SINGULARITY_CACHEDIR` or `singularity.cacheDir`](https://www.nextflow.io/docs/latest/singularity.html?#singularity-docker-hub) Nextflow options to be able to store and re-use the images from a central location for future pipeline runs.
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> * If you are using `conda`, it is highly recommended to use the [`NXF_CONDA_CACHEDIR` or `conda.cacheDir`](https://www.nextflow.io/docs/latest/conda.html) settings to store the environments in a central location for future pipeline runs.
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4. Start running your own analysis!
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```bash
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nextflow run nf-core/mhcquant -profile test,<docker/singularity/podman/shifter/charliecloud/conda/institute>
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--input 'samples.tsv'
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--fasta 'SWISSPROT_2020.fasta'
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--allele_sheet 'alleles.tsv'
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--predict_class_1
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--allele_sheet 'alleles.tsv'
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--predict_class_1
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--refine_fdr_on_predicted_subset
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```
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@@ -84,7 +96,7 @@ For further information or help, don't hesitate to get in touch on the [Slack `#
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## Citations
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If you use `nf-core/mhcquant` for your analysis, please cite:
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If you use `nf-core/mhcquant` for your analysis, please cite it using the following doi: [10.5281/zenodo.5407955](https://doi.org/10.5281/zenodo.5407955) and the corresponding manuscript:
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> **MHCquant: Automated and Reproducible Data Analysis for Immunopeptidomics**
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>
@@ -93,6 +105,8 @@ If you use `nf-core/mhcquant` for your analysis, please cite:
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> Journal of Proteome Research 2019 18 (11), 3876-3884
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> DOI: 10.1021/acs.jproteome.9b00313
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An extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](CITATIONS.md) file.
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You can cite the `nf-core` publication as follows:
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> **The nf-core framework for community-curated bioinformatics pipelines.**
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