Functional Metagenome Analysis using Gene Ontology (MEGAN 4)

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    Functional Metagenome Analysis using Gene Ontology (MEGAN 4) - Presentation Transcript

    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]

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