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q2-motus

https://github.com/motu-tool/q2-motus

Science Score: 57.0%

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Repository

Basic Info
  • Host: GitHub
  • Owner: motu-tool
  • License: gpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 7.22 MB
Statistics
  • Stars: 8
  • Watchers: 2
  • Forks: 1
  • Open Issues: 1
  • Releases: 0
Created over 3 years ago · Last pushed almost 3 years ago
Metadata Files
Readme License Citation

README.md

q2-mOTUs

This is a QIIME 2 wrapper for mOTU-tool. The tool will help you to assign taxonomy to your metagenomic samples. For details on QIIME 2, see https://qiime2.org.

Principal concept

mOTU is an attempt to build a taxonomy utilizing genomic information about organisms formalized with the help of differences in 40 universal gene markers (40 MGs) sequences. The basic unit of taxonomical profile is an mOTU, and thus is used in the output. It is different from classical taxa and may encompass one, few or no species. A detailed map of relationship between mOTUs and standard taxonomical units is located in data/motus_taxonomy_map.tsv

Requirements

  • QIIME 2 >= 2022.8 (https://qiime2.org/)
  • Git

Known issues

QIIME2 makes the copy of data to a temporary directory. By default, it's located in the /tmp folder, which may not have enough space to store the data. Please, change the TMPDIR variable to the folder with enough space during data import. export TMPDIR=/path/to/tmpdir

Installation

1. Install QIIME 2

Follow the instructions on https://docs.qiime2.org/2022.8/install/native/ to install QIIME 2. You will need to install the latest version of QIIME 2 (2022.8 or later).

2. Activate QIIME 2

Activate the QIIME 2 environment by running the following command: conda activate qiime2-2022.8

3. Install mOTU-tool

Make sure to start by installing mamba in your QIIME2 environment. This will help to solve dependency conflicts faster: conda activate qiime2-2022.8 conda install mamba -c conda-forge

Next, install q2-mOTUs git clone https://github.com/motu-tool/q2-mOTUs cd q2-mOTUs make install Test the installation qiime dev refresh-cache qiime motus --help

Usage

The plugin executes one function - assigns taxonomy to metagenomic reads. Therefore, there is a single workflow.

1. Import your data to QIIME 2

Import your metagenomic sequencing data in .fastq format (don't forget to preprocess your data) to QIIME2 as a SampleData semantic type using manifest file. See examples in q2_motus/tests/data.

2. Run mOTU-tool

Whether you have a single sample or multiple samples, you can run mOTU-tool using the following command: qiime motus profile \ --i-samples q2_motus/tests/data/paired-end.qza \ --o-taxonomy paired-end-taxonomy.qza \ --o-table paired-end-classified.qza \ --p-threads 4 \ --p-jobs 2

Optimal combination threads and jobs

q2-mOTUs runs multiple instances of motu profile command from original software, which aligns reads to the reference using bwa mem. Alignment step execution time scales effectively (linearly) for up to 8 threads per job. The amount of jobs you can deploy is amount of CPUs available divided by number of threads used for a single job.

Optionally, you can import precomputed, merged mOTU profiles

Attention: precomputed mOTU table should be generated from full taxonomy -q flag and counts -c flag profiles.

qiime motus import-table \ --i-motus-table $TMPDIR/merged.motus \ --o-table artifacts/motu-table.qza \ --o-taxonomy artifacts/motu-taxonomy.qza

Output

  1. table - FeatureTable[Frequency] - A table of the counts of gene markers in samples.
  2. taxonomy - FeatureData[Taxonomy] - A full taxonomy for each of the gene marker.

3. Process the results

Because table is a FeatureTable[Frequency] artifact, QIIME2 offers a lot of possibilities to analyze it. For example, use feature-table summarize: qiime feature-table summarize \ --i-table paired-end-classified.qza \ --o-visualization paired-end-classified.qzv To get summary of your feature table.

image

Or create all-time favourite taxa barplot: qiime taxa barplot \ --i-table paired-end-classified.qza \ --i-taxonomy paired-end-taxonomy.qza \ --o-visualization paired-end-taxa-barplot.qzv

image

Or analyze the samples using Metadata you have on hand!

Parameters

Due to a QIIME2 naming convention, parameter names in plugin and standalone version are different. The table summarizes differences. | Q2-mOTUs parameter | mOTU parameter | Description | |---------------------------|----------------|-------------| | --p-min-alen | -l | Minimum length of the alignment. | | --p-marker-gene-cutoff | -g | Minimum number of marker genes to be considered a species. Ranges from 1 to 10. A higher value increases precision (and lowers recall).| | --p-mode | -y | The mode to use for abundance estimation. base.coverage measures the average base coverage of the gene. insert.raw_counts measures the number of reads that map to the gene. insert.scaled_counts measures the number of reads that map to the gene, scaled by the length of the gene. | | --p-reference-genomes/ --p-no-reference-genomes| -e | Only use species with reference genomes (ref-mOTUs). | | --p-threads | -t | Number of threads to use. | | --p-jobs | -j | Number of jobs to run in parallel. |

Citation

If you use this tool, please cite the following paper: @article{Ruscheweyh2022, doi = {10.1186/s40168-022-01410-z}, url = {https://doi.org/10.1186/s40168-022-01410-z}, year = {2022}, month = dec, publisher = {Springer Science and Business Media {LLC}}, volume = {10}, number = {1}, author = {Hans-Joachim Ruscheweyh and Alessio Milanese and Lucas Paoli and Nicolai Karcher and Quentin Clayssen and Marisa Isabell Keller and Jakob Wirbel and Peer Bork and Daniel R. Mende and Georg Zeller and Shinichi Sunagawa}, title = {Cultivation-independent genomes greatly expand taxonomic-profiling capabilities of {mOTUs} across various environments}, journal = {Microbiome} }

Owner

  • Name: motu-tool
  • Login: motu-tool
  • Kind: organization

Citation (CITATION.cff) 

cff-version: 1.2.0
message: If you use this software, please cite it as below.
title: 'Cultivation-independent genomes greatly expand taxonomic-profiling capabilities of mOTUs across various environments'
doi: 10.1186/s40168-022-01410-z
authors:
  - given-names: Hans-Joachim
    family-names: Ruscheweyh
    affiliation: Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Zürich, Switzerland
    orcid: https://orcid.org/0000-0001-7473-6086  
  - given-names: Alessio
    family-names: Milanese
    affiliation: European Molecular Biology Laboratory, Heidelberg, Germany
    orcid: https://orcid.org/0000-0002-7050-2239
  - given-names: Lucas
    family-names: Paoli
    affiliation: Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich
    orcid: https://orcid.org/0000-0003-0771-8309
  - given-names: Nicolai
    family-names: Karcher
    affiliation: Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    orcid: https://orcid.org/0000-0001-7894-8182
  - given-names: Quentin
    family-names: Clayssen
    affiliation: Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich
    orcid: https://orcid.org/0000-0002-2574-073X
  - given-names: Marisa Isabell
    family-names: Keller
    affiliation: Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    orcid: https://orcid.org/0000-0001-6831-4557
  - given-names: Jakob
    family-names: Wirbel
    affiliation: Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
    orcid: https://orcid.org/0000-0002-4073-3562
  - given-names: Peer 
    family-names: Bork
    affiliation: European Molecular Biology Laboratory, Heidelberg, Germany
      & Max Delbrück Centre for Molecular Medicine, Berlin, Germany
      & Molecular Medicine Partnership Unit, Heidelberg, Germany
      & Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
    orcid: https://orcid.org/0000-0002-2627-833X
  - given-names: Daniel R.
    family-names: Mende
    affiliation: Department of Medical Microbiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
    orcid: https://orcid.org/0000-0001-6831-4557
  - given-names: Georg
    family-names: Zeller
    affiliation: European Molecular Biology Laboratory, Heidelberg, Germany
    orcid: https://orcid.org/0000-0003-1429-7485
  - given-names: Sunagawa
    family-names: Shinichi
    affiliation: Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich
    orcid: https://orcid.org/0000-0003-3065-0314

version: 3.0.3
date-released: 2022-07-13
repository-code: https://github.com/motu-tool/mOTUs
license: GNU General Public License v3.0
keywords:
- "Metagenomics"
- "Microbiome"
- "Software"
preferred-citation:
  type: article
  authors:
    - given-names: Hans-Joachim
      family-names: Ruscheweyh
      affiliation: Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, Zürich, Switzerland
      orcid: https://orcid.org/0000-0001-7473-6086  
    - given-names: Alessio
      family-names: Milanese
      affiliation: European Molecular Biology Laboratory, Heidelberg, Germany
      orcid: https://orcid.org/0000-0002-7050-2239
    - given-names: Lucas
      family-names: Paoli
      affiliation: Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich
      orcid: https://orcid.org/0000-0003-0771-8309
    - given-names: Nicolai
      family-names: Karcher
      affiliation: Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
      orcid: https://orcid.org/0000-0001-7894-8182
    - given-names: Quentin
      family-names: Clayssen
      affiliation: Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich
      orcid: https://orcid.org/0000-0002-2574-073X
    - given-names: Marisa Isabell
      family-names: Keller
      affiliation: Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
      orcid: https://orcid.org/0000-0001-6831-4557
    - given-names: Jakob
      family-names: Wirbel
      affiliation: Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
      orcid: https://orcid.org/0000-0002-4073-3562
    - given-names: Peer 
      family-names: Bork
      affiliation: European Molecular Biology Laboratory, Heidelberg, Germany
        & Max Delbrück Centre for Molecular Medicine, Berlin, Germany
        & Molecular Medicine Partnership Unit, Heidelberg, Germany
        & Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
      orcid: https://orcid.org/0000-0002-2627-833X
    - given-names: Daniel R.
      family-names: Mende
      affiliation: Department of Medical Microbiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
      orcid: https://orcid.org/0000-0001-6831-4557
    - given-names: Georg
      family-names: Zeller
      affiliation: European Molecular Biology Laboratory, Heidelberg, Germany
      orcid: https://orcid.org/0000-0003-1429-7485
    - given-names: Sunagawa
      family-names: Shinichi
      affiliation: Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich
      orcid: https://orcid.org/0000-0003-3065-0314
  doi: "10.1186/s40168-022-01410-z"
  journal: "Microbiome"
  month: 12
  year: 2022
  title: "Microbial abundance, activity and population genomic profiling with mOTUs2"
  abstract: 'Taxonomic profiling is a fundamental task in microbiome research that aims to detect and quantify 
  the relative abundance of microorganisms in biological samples. Available methods using shotgun metagenomic data 
  generally depend on the deposition of sequenced and taxonomically annotated genomes, usually from cultures of 
  isolated strains, in reference databases (reference genomes). However, the majority of microorganisms have not 
  been cultured yet. Thus, a substantial fraction of microbial community members remains unaccounted for during 
  taxonomic profiling, particularly in samples from underexplored environments. To address this issue, 
  we developed the mOTU profiler, a tool that enables reference genome-independent species-level profiling 
  of metagenomes. As such, it supports the identification and quantification of both “known” and “unknown” 
  species based on a set of select marker genes.

  We present mOTUs3, a command line tool that enables the profiling of metagenomes for >33,000 species-level 
  operational taxonomic units. To achieve this, we leveraged the reconstruction of >600,000 draft genomes, 
  most of which are metagenome-assembled genomes (MAGs), from diverse microbiomes, including soil, 
  freshwater systems, and the gastrointestinal tract of ruminants and other animals, 
  which we found to be underrepresented by reference genomes. Overall, two thirds of all species-level taxa 
  \lacked a reference genome. The cumulative relative abundance of these newly included taxa was low in 
  well-studied microbiomes, such as the human body sites (6–11%). By contrast, they accounted for 
  substantial proportions (ocean, freshwater, soil: 43–63%) or even the majority (pig, fish, cattle: 60–80%) 
  of the relative abundance across diverse non-human-associated microbiomes. 
  Using community-developed benchmarks and datasets, we found mOTUs3 to be more accurate than other 
  methods and to be more congruent with 16S rRNA gene-based methods for taxonomic profiling. 
  Furthermore, we demonstrate that mOTUs3 increases the resolution of well-known microbial groups 
  into species-level taxa and helps identify new differentially abundant taxa in comparative metagenomic studies.

  We developed mOTUs3 to enable accurate species-level profiling of metagenomes. Compared to other methods, 
  it provides a more comprehensive view of prokaryotic community diversity, in particular for currently 
  underexplored microbiomes. To facilitate comparative analyses by the research community, it is released 
  with >11,000 precomputed profiles for publicly available metagenomes and is 
  freely available at: https://github.com/motu-tool/mOTUs.'

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Dependencies

setup.py pypi
  • motu-profiler *
.github/workflows/ci.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v1 composite
  • qiime2/action-library-packaging alpha1 composite