https://github.com/animesh/proteomicslfq

Proteomics label-free quantification (LFQ) analysis pipeline

https://github.com/animesh/proteomicslfq

Repository

Proteomics label-free quantification (LFQ) analysis pipeline

https://github.com/animesh/proteomicslfq/blob/master/

# ![nf-core/proteomicslfq](docs/images/nf-core-proteomicslfq_logo.png)

**Proteomics label-free quantification (LFQ) analysis pipeline using OpenMS and MSstats, with feature quantification, feature summarization, quality control and group-based statistical analysis.**.

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## Introduction

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.

## Quick Start

1. Install [`nextflow`](https://nf-co.re/usage/installation)

2. Install either [`Docker`](https://docs.docker.com/engine/installation/) or [`Singularity`](https://www.sylabs.io/guides/3.0/user-guide/) 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))_

3. Download the pipeline and test it on a minimal dataset with a single command:

    ```bash
    nextflow run nf-core/proteomicslfq -profile test,
    ```

    > 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 ` in your command. This will enable either `docker` or `singularity` and set the appropriate execution settings for your local compute environment.

4. Start running your own analysis!

    ```bash
    nextflow run nf-core/proteomicslfq \
      -profile  \
      --input '*.mzml' \
      --database 'myProteinDB.fasta' \
      --expdesign 'myDesign.tsv'
    ```

See [usage docs](https://nf-co.re/proteomicslfq/usage) for all of the available options when running the pipeline. Or configure the pipeline via
[nf-core launch](https://nf-co.re/launch) from the web or the command line.

## Documentation

The nf-core/proteomicslfq pipeline comes with documentation about the pipeline which you can read at [https://nf-co.re/proteomicslfq](https://nf-co.re/proteomicslfq) or partly find in the [`docs/` directory](docs).

It performs conversion to indexed mzML, database search (with multiple search engines), re-scoring (with e.g. Percolator), merging, FDR filtering, modification localization with Luciphor2 (e.g. phospho-sites), protein inference and grouping as well as label-free quantification by either spectral counting or feature-based alignment and integration. Downstream processing includes statistical post-processing with MSstats and quality control with PTXQC. For more info, see the [output docs](docs/output.md).

## Credits

nf-core/proteomicslfq was originally written by Julianus Pfeuffer, Lukas Heumos, Leon Bichmann, Timo Sachsenberg, Yasset Perez-Riverol.

## Contributions and Support

If you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md).

For further information or help, don't hesitate to get in touch on the [Slack `#proteomicslfq` channel](https://nfcore.slack.com/channels/proteomicslfq) (you can join with [this invite](https://nf-co.re/join/slack)).

## Citation

If you use  nf-core/proteomicslfq for your analysis, please cite it using the following doi: [10.5281/zenodo.XXXXXX](https://doi.org/10.5281/zenodo.XXXXXX)

You can cite the `nf-core` publication as follows:

> **The nf-core framework for community-curated bioinformatics pipelines.**
>
> Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.
>
> _Nat Biotechnol._ 2020 Feb 13. doi: [10.1038/s41587-020-0439-x](https://dx.doi.org/10.1038/s41587-020-0439-x).
> ReadCube: [Full Access Link](https://rdcu.be/b1GjZ)

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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