1. MEGAN analysis of metagenomic data Daniel H. Huson, Alexander F. Auch, Ji Qi, et al. Genome Res. 2007

2. Early metagenomic  Known phylogenetic markers and subsequent sequencing of clones  Analysis of paired-end reads  Complete sequences of environmental fosmid and BAC clones  Rough annotation of the metabolic capacity  Environmental assemblies  Distinguish between discrete species and population of closely related biotypes  Problem of using proven phylogenetic markers(ribosomal genes, coding sequences)  Slow-evolving genes : distinguishing between species at large evolutionary distances

3. What is MEGAN?  Metagenome Analyzer (MEGAN)  Free software.  Deviates from the analytical pattern of previous  Built on the statistical analysis of comparing random sequence intervals with unspecified phylogenetic properties against databases  Depends on the related sequences in the databases  Providing filter to adjust the level of stringency later to an appropriate level  Laptop analysis  Comparing result (BLAST)-> laptop (MEGAN)  Graphical and statistical output

4. Pipeline  Compare against databases : BLAST  Compute, explore taxonomical content : NCBI taxonomy  Lowest common ancestor (LCA) algorithm  Data sets(Sargasso Sea, mammoth bone, Short E. coli K12 & B. bacteriovorus HD100)

5. What we can do with MEGAN  Species and strain identification through species-specific genes  Searching species or taxa by find tool  Distribution of strains of a species  Underlying sequence alignments

6. Experiments-1  Sargasso Sea  data set  Sanger sequencing  Sample 1-4 from DDBJ/EMBL/GenBank  10000 reads from Sample1  Randomly selected a pooled set of 10000 reads from samples 2-4  BLASTX->NCBI-NR  1% no hits from sample1, <3% no hits from sample 2-4  Filters  Min-score : bit-score threshold of 100  Top-percent : bit scores lie within 5% of the best score  Min-support : isolated assignments it by one read) discarded

7. Analysis-Sargasso Sea data  1.66M reads, AVG. 818bp by Sanger sequensing  Species profile of 16 taxonomical groups  Environmental assemblies  By analyzing six specific phylogenetic markers  rRNA, RecA/RadA, HSP70, RpoB, EF-Tu, and Ef-G

8. Result Sample1 ~83% reads were assigned to taxa that were more speific than the kingdom level Majority of (8298) were assigned to bacterial group Sample 2-4 ~59% reads were assigned to taxa that were more specific than the kingdom level Majority of (5709) were assigned to bacterial group Alphaproteobacteria, Gammaproteobacteria by a factor of 2-4 over the remaining 14 taxonomic groups Eukaryotes & Viruses : size filtering Archaea : May be there is 10times as much vacterial sequence information in the public databases MEGAN vs. previous (Venter et al. 2004) Specific assignment information : LCA

9. Result-cont. Averaged weighted percentage of the siz phylogenetic markers for each of the 16 taxonomic groups Easily detect sampling bias between sample1 and pooled sample 2-4

10. Experiments-2  Mammoth bone  Data set  Roche GS20 sequencing (Sequencing-by-synthesis)  Sample from 1g of mammoth bone, 28000 years  ~300,000 reads, 95bp  BLASTZ-genome sequences (elephant, human, dog)  45.4% of the reads mammoth DNA, others are environmental organisms (bacteria, fungi, amoeba, nematodes)  BLASTX–NCBI-NR for environmental sequences  Filters : bit-score threshold 30, discard isolated assignment (filtered 2086 reads)

11. Result  19841 reads to Eukaryota, of which 7969 to Gnathostomata  16972 : Bacteria, 761: Archea, 152 : Viruses

12. Experiment 3  Identifying species from various lead length  Short E. coli K12 & B. bacteriovorus HD100 simulation  5000 random shotgun reads  BLASTX-NCBI-NR  Filters  Bit-score threshold 35  20% of the best hit  Discarded isolated assignments  Result : no false-positive assignment, short read can be used for metagenomic analysis, albeit at the cost of a high rate of under- prediction

13. Experiment 3-cont.  Roche GS20 sequencing  Data set  2000 reads from random positions in the E.coli K12  ~100 bp  BALSTX – NCBI-NR  Filters  Bit-score threshold 35  20% of the best hit  Discarded isolated assignments  Result

14. Experiment 3-cont.  Roche GS20 sequencing  Data set  2000 reads from random positions in the B. bacteriovorus HD100  ~100 bp  BALSTX – NCBI-NR : A in figure  BLASTX – NCBI-NR without B.bacteriovorus HD100 : B in figure  Filters  Bit-score threshold 35  20% of the best hit  Discarded isolated assignments  Result

15. MEGAN 3(June, 2009)  Suitable for very large datasets  Advances in the throughput and cost-efficiency of sequencing technology  Interests changed  From ‘which species present’ to ‘What’s different?’  Features  Visualization technique for multiple database  New statistical method for highlighting the difference in a pairwise comparison

16. MEGAN3-cont.  Comparing 6 mouse gut with human gut  Clickable, collapsible.