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Galaxy Day Fr 2015 - ASaiM: A Galaxy framework to analyze gut microbiota data

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Massive sequencing data from intestinal microbiota are available in public data repositories (Genbank, ENA, …) but are not easy to identify with associated metadata, query and compare because they are dispatched and possibly underwent different analyses. To extract useful information from these datasets, they need to be collected and formatted using a similar workflow based on specific gut microbiota databases. To process and analyze data from gut microbiota, we developed a framework based on a personalized Galaxy instance. In this instance, several tools are incorporated (PRINSEQ, FastQ-Join, SortMeRNA, Reago, usearch, framebot, cd-hit, MetaPhlAn, HUMAnN, QIIME), with databases such as COG (Clusters of Orthologous Groups of proteins) or the catalog of reference genes in the human gut microbiome (Li et al, Nature Biotechnology, 2014). We defined some standard workflows using these tools and databases.

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Galaxy Day Fr 2015 - ASaiM: A Galaxy framework to analyze gut microbiota data

  1. 1. ASaiM A Galaxy framework to analyze gut microbiota data Bérénice Batut — EA CIDAM, Clermont-Ferrand November 19th, 2015
  2. 2. Context
  3. 3. Gut microbiota Community of microorganism species that live in the digestive tracts
  4. 4. Importance of gut microbiota "Forgotten" organ
  5. 5. Metagenomic / Metatranscriptomic
  6. 6. Comparative meta-omic Study of microbiota in its globality
  7. 7. Figure 4C - Li et al, Nat Biotech, 2014
  8. 8. Comparative meta-omic of different projects For a global view?
  9. 9. Public data repository Source of information
  10. 10. European Nucleotide Archive "human gut metagenome" search: 236 studies 1,545 runs, ... Dispersed and not comparable information
  11. 11. Must Collect datasets Analyze them given a standard workflow Quality control Sequence sorting Taxonomic analysis Functional analysis Raw sequences Formatted taxonomic assignations Formatted functional assignations
  12. 12. Existing tools QIIME, MG-RAST, EBI metagenomics, MetAMOS, ... But none of them follows all the requirements:
  13. 13. Existing tools QIIME, MG-RAST, EBI metagenomics, MetAMOS, ... But none of them follows all the requirements: Analyze datasets given the standard workflow
  14. 14. Existing tools QIIME, MG-RAST, EBI metagenomics, MetAMOS, ... But none of them follows all the requirements: Analyze datasets given the standard workflow Use gut microbiota specific databases
  15. 15. Existing tools QIIME, MG-RAST, EBI metagenomics, MetAMOS, ... But none of them follows all the requirements: Analyze datasets given the standard workflow Use gut microbiota specific databases Combine user-friendly interface and command-line
  16. 16. ASaiMAuvergne Sequence analysis of intestinal Microbiota An environment to analyze metagenomic and metatranscriptomic sequences from gut microbiota
  17. 17. Components Expert database Web interface Framework
  18. 18. R1 sequences R2 sequences COG databaseNon rRNA sequencesrRNA sequencesLong rRNA sequences Functional a Diamond KEGG module ab KEGG module c HUMAnN COG family coverage COG family abundance KEGG pathway abundance KEGG pathway coverage Similarity search report Taxonomic assignation MetaPhlAn QIIME De novo OTU picking Taxonomic assignation report of non rRNA sequences OTU of long rRNA sequences QIIME Taxonomic assignation of OTU OTU table of long rRNA sequences QIIME Community summary by taxonomic composition Taxonomy table of long rRNA sequences QIIME Alpha diversity and alpha rarefaction computation Alpha diversity of long rRNA sequences action of long sequences QIIME De novo OTU picking OTU of long rRNA sequences QIIME Taxonomic assignation of OTU OTU table of long rRNA sequences QIIME Community summary by taxonomic composition Taxonomy table of long rRNA sequences QIIME Alpha diversity and alpha rarefaction computation Alpha diversity of long rRNA sequences Alpha rarefaction of long rRNA sequences Paired-end assembly FastQ Joiner Quality control PRINSEQ Sequence sorting Reago SortMeRNA Paired-end assembled sequences Quality controlled sequences
  19. 19. ASaiM framework Bioinformatics framework to generate workflows for analyses of gut microbiota data
  20. 20. Main requirements Generation of workflow with numerous tools Easy use Flexibility and modularity Incorporation of wanted/needed tools and databases
  21. 21. Things I tried
  22. 22. Things I tried Simple Python scripts
  23. 23. Things I tried Simple Python scripts Workflow managers such as Luigi, Airflow, ...
  24. 24. Things I tried Simple Python scripts Workflow managers such as Luigi, Airflow, ... Homemade approach Configuration file Workflow description Web interface for generation Python scripts to execute workflow
  25. 25. Galaxy Fit main requirements Generation of workflow with numerous tools Easy use Flexibility and modularity Incorporation of wanted/needed tools and databases
  26. 26. ASaiM Galaxy instance
  27. 27. To launch the instance Get the code source from Install the required dependencies Launch the instance Browse it on GitHub $ git clone git@github.com:ASaiM/framework.git $ cd framework $ ./src/launch_galaxy.sh http://127.0.0.1:8080/
  28. 28. Behind the magic Shell scripts to configure the instance 1. Get latest revision of Galaxy from GitHub 2. Prepare databases and local tools 3. Configure with Custom configuration files Wanted tools Wanted databases 4. Launch Galaxy
  29. 29. Tools From standard Galaxy instance From ToolShed Developed wrappers Planemo Integration in test Tool Shed https://github.com/ASaiM/galaxytools
  30. 30. Workflows
  31. 31. Databases SortMeRNA ribosomal databases COG RefSeq Catalog of reference genes in the human gut microbiome from Li et al. (2014) Greengenes
  32. 32. Documentation http://asaim.readthedocs.org/
  33. 33. To do Automatize the configuration and deployment of the instance with Ansible Add tools in development, databases, workflows Validate workflows on datasets (local, mock, ...) Integrate tools and workflows to the ToolShed Automatize tool integration from ToolShed with Ansible Complete the documentation
  34. 34. Thank You. Questions? http://asaim.github.io bebatut.fr github.com/bebatut twitter.com/bebatut

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