Talk at the SIG M3 meeting (ISMB 2009), Stockholm June 2009 Describes an approach for the functional classification of environmental sequences of a metagenomic data set. http://www-ab.informatik.uni-tuebingen.de/software/megan/welcome.html

1. Functional Classification of
Environmental Reads using Gene Ontology
Daniel C. Richter
Daniel H. Huson
Dept. Algorithms in Bioinformatics
ZBIT Center for Bioinformatics
University of Tuebingen, Germany
www-ab.informatik.uni-tuebingen.de

2. Metagenomics - Workflow
Environmental Sample
Sequencing (Sanger/NGS)
Who is out there? How many are there? What are they doing?
Taxonomical Analysis Quantitive Analysis Functional Analysis
MEGAN
Software
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [01]

3. Metagenomics - Workflow
Environmental Sample
Sequencing (Sanger/NGS)
Who is out there? How many are there? What are they doing?
Taxonomical Analysis Quantitive Analysis Functional Analysis
MEGAN
Software
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [01]

4. MEGAN – Taxonomical Analysis
Precomputation
Reads
nr
BLAST nt
...
„Laptop
MEGAN Analysis“
NCBI Taxonomy
• >460.000 taxa
• Taxonomical Ranks:
Kingdom, Phylum, Class,
Order,..., Species
Huson et al., 2007, Genome Research
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [02]

5. Functional Metagenome Analysis

Extension of MEGAN to classify reads according to their function
• Input: BLASTX result file → homology-based approach
• Structured and interactive overview of gene products
http://www.geneontology.org
 widely used in biological databases, gene expression
and annotation studies
 >27000 GO terms (cross-specific)
DAG
 three structured vocabularies (ontologies)
molecular function
biological process
cellular component
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [03]

6. Mapping BLAST Matches to GO Terms
>gb|EAU86868.1| predicted protein [Coprinopsis cinerea okayama7#130]
>emb|CAC86119.1| putative hexose-6-phosphate transporter [Listeria monocytogenes]
>ref|ZP_00390013.1| Arabinose efflux permease [Bacillus anthracis str. A2012]
ref2go map
RefSeqID →
UniProt mapping
GO Terms
RefSeqID → GO Terms
RefSeqID → GO Terms http://pir.georgetown.edu/
RefSeqID → GO Terms
...
>3.5 Mio entries
GO:0044408
GO:0043581
GO:0032502
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [04]

7. Placing Reads onto GO Terms – LCA Approach
r
ar al la t
ul ic l u en
ec tion og ess e l on
C p
BLAST ref2go map ol ol
M un c
F
Bi roc
P Co
m GO Terms
M0 protein binding
M1 response to stress
signal transduction
Read M2
cell communication
M3
nucleus
M4 cell part
cytosol
Placement: ? ? ?
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [05]

8. Placing Reads onto GO Terms – LCA Approach
r
ar al la t
ul ic l u en
ec tion og ess e l on
C p
BLAST ref2go map ol ol
M un c Bi roc
P Co
m GO Terms
F
M0 protein binding
M1 response to stress
signal transduction
Read M2
cell communication
M3
nucleus
M4 cell part
cytosol
Placement: ? ?
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [06]

9. Placing Reads onto GO Terms – LCA Approach
r
ar al la t
ul ic l u en
ec tion og ess e l on
C p
BLAST ref2go map ol ol
M un c Bi roc
P Co
m GO Terms
F
M0 protein binding
M1 response to stress
signal transduction
Read M2
cell communication
M3
nucleus
M4 cell part
cytosol
Placement: ? ?
root root root
cellular
process
cell
communication
response signal response signal response signal
to stress transduction to stress transduction to stress transduction
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [07]

10. Placing Reads onto GO Terms – LCA Approach
r
ar al la t
ul ic l u en
ec tion og ess e l on
C p
BLAST ref2go map ol ol
M un c Bi roc
P Co
m GO Terms
F
M0 protein binding
M1 response to stress
signal transduction
Read M2
cell communication
M3
nucleus
M4 cell part
cytosol
Placement: ?
root root root
cellular
process
cell
communication
response signal response signal response signal
to stress transduction to stress transduction to stress transduction
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [08]

11. Placing Reads onto GO Terms – LCA Approach
r
ar al la t
ul ic l u en
ec tion og ess e l on
C p
BLAST ref2go map ol ol
M un c Bi roc
P Co
m GO Terms
F
M0 protein binding
M1 response to stress
signal transduction
Read M2
cell communication
M3
nucleus
M4 cell part
cytosol
Placement:
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [09]

12. Benefits and Drawbacks of the LCA Algorithm
• loss of accuracy: LCA is always less specific
• might miss gene products of interest (losing the „big picture“)
• reads with many different BLAST matches (= many GO terms)
are likely to be assigned to high level GO terms
• complexity reduction facilitates analysis and visual inspection
• memory efficient:
• need to store only three integers (GO IDs) per read
• applicable to large data sets: 5 Mio reads, 760 GB BLAST output
• loss of accuracy ≠loss of correctness (avoids false-positives)
→ balance between usability and accuracy
Calculation example „Full Approach“:
1,000,000 reads
each read: 50 BLAST matches
each match: 10 GO terms
→ 500,000,000 GO IDs
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [10]

13. GO Analyzer – Main Window
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [11]

14. GO Analyzer – Main Window
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [12]

15. GO Analyzer – Main Window
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [13]

16. GO Analyzer – Main Window
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [14]

17. GO Analyzer – Main Window
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [15]

18. GO Analyzer – Main Window
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [16]

19. GO Analyzer – Main Window
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [17]

20. GO Analyzer – Main Window
Extract reads
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [18]

21. GO Analyzer – Path Highlighting
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [19]

22. GO Analyzer – GO Slims
Gene Ontology provides subsets of GO terms
→ useful for high level view of the three ontologies
http://www.geneontology.org/GO.slims.shtml
Design your own metagenomic GO slim...
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [20]

23. GO Analyzer – Comparison View
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [21]

24. GO Analyzer – Comparison View
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [22]

25. GO Analyzer – Comparison View
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [23]

26. GO Analyzer – Comparison View
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [24]

27. GO Analyzer – Summary
• New module of MEGAN 4 to conduct functional analyses on
environmental reads
„BLAST only once, perform taxonomical and functional analysis in one step“
• Homology-based approach
• Overview tool: visual and interactive exploration of gene products
• Inspection, extraction and chart features
• Comparative mode
Installers for all operating systems will be available from:
http://www-ab.informatik.uni-tuebingen.de/software/megan
Daniel Richter – University of Tuebingen Functional Metagenome Analysis Stockholm, 09/06/27 [25]