General Overview of IMAP project

IMAP stands for Integrated Microbiome Analysis Pipelines but can also stands for Integrated Multi-Omics Analysis Pipelines. IMAP for microbiome analysis comprises different parts which individually, represents a standalone GitHub repository prefixed with {imap-}. When used sequentially, these parts can provide a systematic microbiome data analysis beyond the traditional methods.


IMAP tentative parts: Each part forms a standalone git repository containing similar project stucture.


IMAP approach

  • We use the snakemake workflow management system[1,2] to:
    • Maintain reproducibility in technical validation and regeneration of results.
    • Create scalable data analysis.
    • Foster sustainable improvement of the microbiome data analysis.
    • Ensure adaptability that enables users to modify the analysis to answer extended research questions.
    • Ensure transparency of the technical analysis methodologies.
  • We break complex workflows into small contiguous but related chunks where each major step forms a separate executable snakemake rule.


Mission and Vision

We envision fostering continuous integration and improvement of highly reproducible and sustainable workflows for microbiome data analysis.


IMAP snakemake workflow series

The workflows define the snakemake rules in a DAG (directed acyclic graph) format. You may find a link to the interactive snakemake HTML report in some of the workflows. The link will enable you explore the analysis workflow, associated statistics and in some the result. While exploring the interactive workflow, you can close the left bar (the overlap) to get a more expansive display view.

NOTE! Use a wider screen (landscape) when viewing the interactive report.


Category 01: Microbiome snakemake workflows

IMAP-PART 01

Title: Software requirement for microbiome data analysis with Snakemake workflows


Explore the IMAP-PART 01 interactive snakemake report (if available) here.


IMAP-PART 02

Title: Downloading and exploring microbiome sample metadata from SRA Database


Explore the IMAP-PART 02 interactive snakemake report (if available) here.


IMAP-PART 03

Title: Downloading microbiome fastq sequences from SRA database


Explore the IMAP-PART 03 interactive snakemake report (if available) here.


IMAP-PART 04

Title: Quality Control of Microbiome Next Generation Sequencing Reads


Explore the IMAP-PART 04 interactive snakemake report (if available) here.


IMAP-PART 05

Title: Microbial profiling using Mothur pipeline with Snakemake workflows


Explore the IMAP-PART 05 interactive snakemake report (if available) here.


IMAP-PART 06

Title: Microbial profiling using QIIME2 pipeline with Snakemake workflows


Explore the IMAP-PART 06 interactive snakemake report (if available) here.


IMAP-PART 07

Title: Processing Output from 16S-based microbiome bioinformatics pipelines


Explore the IMAP-PART 07 interactive snakemake report (if available) here.

IMAP-PART 08

Title: Exploratory Analysis of 16S-Based Microbial Profiles


Explore the IMAP-PART 08 interactive snakemake report (if available) here.

IMAP-PART 09

Title: Multi-Dimensional Data Visualization with integrated tools and Snakemake workflow


Explore the IMAP-PART 09 interactive snakemake report (if available) here.

IMAP-PART 10

Title: Microbiome Alpha diversity analysis


Explore the IMAP-PART 10 interactive snakemake report (if available) here.

IMAP-PART 11

Title: Microbiome Beta diversity analysis


Explore the IMAP-PART 11 interactive snakemake report (if available) here.



Multi-Omics data analysis

…BRIEF INTRO IN PROGRESS…


Citation

Please consider citing the iMAP article[3] if you find any part of the IMAP practical user guides helpful in your microbiome data analysis.


References

[1]
Köster, J., Mölder, F., Jablonski, K. P., Letcher, B., Hall, M. B., Tomkins-Tinch, C. H., … Nahnsen, S. (2021). Sustainable data analysis with snakemake. F1000Research, 10. https://doi.org/10.12688/f1000research.29032.2
[2]
Snakemake. (2023). Snakemake. Retrieved from https://snakemake.readthedocs.io/en/stable
[3]
Buza, T. M., Tonui, T., Stomeo, F., Tiambo, C., Katani, R., Schilling, M., … Kapur, V. (2019). iMAP: An integrated bioinformatics and visualization pipeline for microbiome data analysis. BMC Bioinformatics, 20. https://doi.org/10.1186/S12859-019-2965-4


Appendix

Project main tree

.
├── LICENSE
├── README.md
├── config
│   ├── config.yml
│   ├── pbs
│   │   ├── cluster.yaml
│   │   └── config.yaml
│   ├── samples.tsv
│   ├── slurm
│   │   ├── cluster.yaml
│   │   └── config.yaml
│   └── units.tsv
├── dags
│   ├── rulegraph.png
│   └── rulegraph.svg
├── images
│   ├── bioinformatics.png
│   ├── bkgd.png
│   ├── cicd.png
│   ├── imap.png
│   ├── imap_part00.png
│   ├── imap_part01.svg
│   ├── imap_part02.svg
│   ├── imap_part03.svg
│   ├── imap_part04.svg
│   ├── imap_part05.svg
│   ├── imap_part06.svg
│   ├── imap_part07.svg
│   ├── imap_part08.svg
│   ├── imap_part09.svg
│   ├── imap_part10.svg
│   ├── imap_part11.svg
│   ├── imap_partX.svg
│   ├── rulegraph.png
│   └── smkreport
│       └── screenshot.png
├── imap-snakemake-workflows.Rproj
├── index.Rmd
├── library
│   ├── apa.csl
│   ├── export.bib
│   ├── imap.bib
│   └── references.bib
├── report.html
├── results
│   └── project_tree.txt
├── smk.css
├── styles.css
└── workflow
    ├── Snakefile
    ├── envs
    │   ├── environment.yml
    │   ├── mothur.yml
    │   └── qiime220232.yml
    ├── report
    │   ├── alphadiv.rst
    │   ├── betadiv.rst
    │   ├── biobakery.rst
    │   ├── explore.rst
    │   ├── imap.rst
    │   ├── mothur.rst
    │   ├── process.rst
    │   ├── qiime2.rst
    │   ├── readqc.rst
    │   ├── rsmkrpt.rst
    │   ├── software.rst
    │   ├── sracache.rst
    │   ├── srametadata.rst
    │   ├── srareads.rst
    │   ├── visualize.rst
    │   └── workflow.rst
    ├── rules
    │   ├── render_index.smk
    │   ├── report.smk
    │   ├── rmd_report.smk
    │   ├── rules_dag.smk
    │   └── smk_workflows.smk
    └── scripts
        ├── README.md
        ├── render.R
        ├── rules_dag.sh
        ├── smk_html_report.sh
        ├── smk_workflows.sh
        └── tree.sh

13 directories, 71 files



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