1. Considerations for metagenomics data analysis and summary of workflows
Alex Mitchell

2. My background Doctorate in pharmacology (1995-1998)
Post-doc in molecular biology ( )
Bioinformatics research ( )
Co-ordinator for InterPro and EBI metagenomics databases (2011-)

3. My background

4. Overview What is metagenomics? Different types of metagenomic studies
Challenges and considerations for metagenomic data analyses

5. What is metagenomics? “Metagenomics” means literally ‘beyond genomics’
“Metagenome” used by Handelsman et al., in 1998 to describe “collective genomes of soil microflora”
“Metagenomics is the study of metagenomes, genetic material recovered directly from environmental samples.”
“Metagenomics is the study of all genomes present in any given environment without the need for prior individual identification or amplification”

6. Sampling from environment
Filtering step
Extraction of DNA
Sequencing

7. Identification and characterisation of protein coding sequences
Taxonomic analysis
16S rRNA
18S rRNA
ITS
etc
Quality control
Functional analysis
Identification and characterisation of protein coding sequences

8. Why is metagenomics exciting?
Estimated >90% of microbes are not culturable by current techniques
Can’t be accessed by traditional sequencing methods
NGS techniques allow the reading of DNA from uncloned samples
For example: the human body contains ~1014 human cells and ~1015 microbial cells
i.e. 90% of the cells in your body are not your own
massive impact on understanding health and disease

9. Applications of taxonomic analyses
Identification of new species
Diversity analysis
Comparing populations from different sites or states

10. Applications of functional analyses
Bioprospecting for novel sequences with functional applications
Reconstruction of pathways present in the community
Comparing functional activities from different sites or states

11. Why is metagenomics challenging?
Short sequence fragments are hard to characterise
Assembly can lead to chimeras
Iddo Friedberg: ‘Metagenomics is like a disaster in a jigsaw shop’
Millions of different pieces
Thousands of different puzzles
All mixed together
Most of the pieces are missing
No boxes to refer to

12. Limitations and pitfalls
Data used for analysis can have limitations:
16S rRNA genes - limited resolving power and subject to copy number variation
Viral sequences –currently no gold-standard reference database
Protist sequences – little experimentally-derived annotation of protein function in public databases

13. Additional pitfalls
Different functional and taxonomic analysis tools can give different results
The same tools can give different results depending on the version and underlying algorithm (e.g., HMMER2 vs HMMER3)
The same version of the same tools can give different results depending on the reference database used

14. Reference databases

15. Reference databases

16. Reference databases

17. Scientific workflows A number of linked components
Each component is responsible for a small fragment of overall functionality
Allow the description, management, and sharing of scientific analyses

18. Scientific workflows Analogous to laboratory protocols Lyse cells
Remove proteins
DNA precipitate
Re-suspend DNA
… buffers, enzymes, volumes, temperatures, times, centrifugation steps

19. Scientific workflows Analogous to laboratory protocols
Quality control of reads
Assembly into contigs
Prediction of rRNAs
Prediction of protein coding genes
Annotation
… software used, versions, settings, reference databases

20. Tools to create, manage and automate workflows
GALAXY:
TAVERNA:
Pipeline Pilot:

21. Tools to create, manage and automate workflows
Offer a range of components (installed locally or called via web services)
Connected together to create workflows using a drag and drop GUI

22. Workflows
CloVR - a virtual machine for automated and portable sequence analysis (
Range of protocols for microbial genomics
Specific protocol (CloVR-Metagenomics) for taxonomic and functional classifications of metagenomics shotgun sequence data

23. Workflows The CloVR-Metagenomics protocol

24. Other considerations: data analysis speed
Wetterstrand KA. DNA Sequencing Costs: Data from the NHGRI Large-Scale Genome Sequencing Available at:

25. Data analysis speed
The cost of sequencing has really gone down
Now I can do metagenomics!
Awesome!
Amount of sequence generated has increased 5,000-fold
Computational speed has increased only 10-fold
Time taken to analyse has increased 500-fold

26. Data analysis cost (~2m bp/$) 14.5 % 30 % 28 % 70 % (~80 bp/$) 14.5 %
55 %
36.5 %
14.5 %
Sboner et al. Genome Biology (2011) 12:125

27. What data to store? Raw sequence data: Analysis results ?
Important for metagenomics as some samples are hard to replicate
Large file sizes
Analysis results ?
Easiest to repeat, although it takes time & requires keeping track of analysis steps and versions
Data description including metadata
Essential: what, where, who, how and when
If absent, raw data have very limited usefulness

28. The importance of metadata
Metadata includes the in-depth, controlled description of the sample that your sequence was taken from
How was it sampled? How was it extracted? How was it stored? What sequencing platform was used?
Where did it come from? What were the environmental conditions (lat/long, depth, pH, salinity, temperature…) or clinical observations?

29. The importance of metadata
If metadata is adequately described, using a standardised vocabulary, querying and interpretation across projects becomes possible
Show the microbial species found in the North Pacific
… at depths of 50 – 100 m
… in samples taken May-June
… compared to the Indian Ocean, under the same conditions

30. Considerations: storing data
Where are you going to store this?
Locally : back-up ?
long term ?
sharing ?
access ?
Amazon, Google or specialist research clouds
Public repositories, such as ENA, NCBI or DDBJ

31. Public repositories Free! Secure long term storage
No need for local infrastructure
Enforced compliance:
Publisher requirements (accession numbers)
Institutional requirements
Funder requirements
Data are more useful:
Data are reusable and can be discovered by others
Available for re- and meta-analyses

32. Considerations: moving data
Transferring a 100 Gb NGS data file across the internet
'Normal' network bandwidth (1 Gigabit/s) ~ 1 week*
High-speed bandwidth (10 Gigabit/s) < 1 day*
Traditional methods may be the most effective!
* Stein, Genome Biol. (2010) 11:207

33. Metagenomics portals http://www.ebi.ac.uk/metagenomics

34. What do metagenomics portals offer?
Submit data
Tools to help transfer data
Tools to help capture & store metadata
Sequence archiving
Quality filtering of sequences
Sequence analysis
(prebuilt workflows)
Visualisation/Interpretation

35. Planning your experiments: things to consider
Think data volumes
Metagenomic data files can be vast (10s of Gbs/file): how are you going to store and manipulate them?
Plan ahead
In a short time-frame, data volumes could be orders of magnitude higher
Think timescales
Data analysis times >> data generation times

36. Planning your experiments: things to consider
Consider economics
Direct costs & opportunity costs
Productivity
Think communication and collaboration
Biologists
Bioinformaticians & service providers
Computer scientists

37. Your views, concerns, frustrations…