Sources: https://github.com/bebatut-slides/galaxy-day_2015 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.

1. ASaiM
A Galaxy framework to analyze gut microbiota data
Bérénice Batut — EA CIDAM, Clermont-Ferrand
November 19th, 2015

2. Context

3. Gut microbiota
Community of microorganism species that live in the digestive tracts

4. Importance of gut microbiota
"Forgotten" organ

5. Metagenomic / Metatranscriptomic

6. Comparative meta-omic
Study of microbiota in its globality

7. Figure 4C - Li et al, Nat Biotech, 2014

8. Comparative meta-omic of different projects
For a global view?

9. Public data repository
Source of information

10. European Nucleotide Archive
"human gut metagenome" search:
236 studies
1,545 runs, ...
Dispersed and not comparable information

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. Existing tools
QIIME, MG-RAST, EBI metagenomics, MetAMOS, ...
But none of them follows all the requirements:

13. Existing tools
QIIME, MG-RAST, EBI metagenomics, MetAMOS, ...
But none of them follows all the requirements:
Analyze datasets given the standard workflow

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. 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. ASaiMAuvergne Sequence analysis of intestinal Microbiota
An environment to analyze metagenomic and
metatranscriptomic sequences from gut microbiota

17. Components
Expert database
Web interface
Framework

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. ASaiM framework
Bioinformatics framework to generate workflows for
analyses of gut microbiota data

20. Main requirements
Generation of workflow with numerous tools
Easy use
Flexibility and modularity
Incorporation of wanted/needed tools and databases

21. Things I tried

22. Things I tried
Simple Python scripts

23. Things I tried
Simple Python scripts
Workflow managers such as Luigi, Airflow, ...

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

26. Galaxy
Fit main requirements
Generation of workflow with numerous tools
Easy use
Flexibility and modularity
Incorporation of wanted/needed tools and databases

27. ASaiM Galaxy instance

28. 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/

29. 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

30. Tools
From standard Galaxy instance
From ToolShed
Developed wrappers
Planemo
Integration in test Tool Shed
https://github.com/ASaiM/galaxytools

31. Workflows

32. Databases
SortMeRNA ribosomal databases
COG
RefSeq
Catalog of reference genes in the human gut microbiome from
Li et al. (2014)
Greengenes

33. Documentation
http://asaim.readthedocs.org/

34. 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

36. Thank You.
Questions?
http://asaim.github.io
bebatut.fr
github.com/bebatut
twitter.com/bebatut