1. Analysis and comparison of very large metagenomes with fast clustering and functional annotation Weizhong Li, BMC Bioinformatics 2009 Present by Chuan-Yih Yu

2. Outline Rapid Analysis of Multiple Metagenomes with a Clustering and Annotation Pipeline (RAMMCAP) – Goal – Methodology – Metagenome comparison – Conclusion Discussion

3. Goal Reduce computation time – Global Ocean Survey(GOS): 1 M CPU Hours = 144 yrs Discover the novel gene or protein families – Metagenomic Profiling of Nice Biomes(BIOME) : ~90% sequences unknown – GOS: double the protein families Compare metagenome data – Clustering-based – Protein family-based

4. RAMMCAP

5. RNA

6. RAMMCAP

7. Meta_RNA & tRNA‐scan High sensitivity, Low specificity(Except 16S) “Identification of ribosomal RNA genes in metagenomic fragments.“, Huang, Y., Gilna, P. & Li, W. Z. Bioinformatics “tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence.“, Lowe, T.M. and Eddy, S.R. Nucleic Acids Res

8. CLUSTERING CD-HIT

9. RAMMCAP

10. CD-HIT Greedy incremental clustering algorithm Whole pairwise alignment avoid Short word (2~5) Index table "Clustering of highly homologous sequences to reduce the size of large protein database", Weizhong Li, et al. Bioinformatics, (2001) "Tolerating some redundancy significantly speeds up clustering of large protein databases", Weizhong Li, et al. Bioinformatics, (2002) "Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences", Weizhong Li, et al. Bioinformatics, (2006).

12. Limitation of CD-HIT Evenly distributed mismatches Greedy issue – Group in first meet cluster

13. CD-HIT Performance

14. ORF S CLUSTERING

15. RAMMCAP

16. Why Cluster ORFs Function studies Novel genes finding

17. ORF Prediction ORF_finder Metagene

18. ORF Prediction Performance MetaSim – Average 100, 200, 400, 800 bp, 1 million reads True ORF (sensitivity) – Overlap 30 AA with NCBI annotated ORF Predicted ORF (specificity) – 50% overlap with true ORF

19. ORF Clustering Run 1 clustering – 90~95% identity Run 2 clustering – 60% identity over 80% of length (454) – 30% identity over 80% of length (Sanger) Merge run 1 & 2 result

20. Clustering Evaluation Test sets – GOS-ORF (30%),BIOME (95%),BIOME-ORF (60%)

21. BIOME Microbiomes & Viromes Microbial sequences are more conserved than viral sequences.

22. Clustering Quality Need conservative threshold Use only >30 AA Pfam sequence Discard short sequence in overlapping Pfam sequence Place into different cluster – Sequence in the same Pfam, place into different cluster.

23. Clustering Validation Generate a clusters whose sequences from the same Pfam Minimize the number of clusters Good clusters : >95% members from the same Pfam – >97% sequences are in good clusters – ~30 times more than bad clusters Number of sequences Number of clusters Cluster Size

24. RAMMCAP

25. Protein Family Annotation Pfam (24.0, Oct. 2009, 11912 families) – textual descriptions, other resources and literature references TIGRFAMs (9.0, Nov. 2009, 3808 models) – GO, Pfam and InterPro models COG(2003, 4873 clusters of orthologous groups) – 3 lineages and ancient conserved domain – RPS‐BLAST(Reverse psi-blast) E values ≤ 0.001

26. Novel Protein Families Discovery Spurious ORFs in a large size of cluster without homology match may contain novel protein families. In GOS only 1.3% of clusters with cluster size ≧ 10 map to 93% of true ORFs In BIOME only 1.0% of clusters with cluster size ≧ 5 map to 28% of true ORFs

27. METAGENOME COMPARISON

28. Statistical Comparison of Metagenomics Occurrence profile coefficient z score, why? (not Rodriguez-Brito's require 10 5 simulated samples) Low occurrence cut off H A =4 (0.95) z=1.96 H A =7 (0.99) z=2.58 1.z> cut off 2.P A ≧ f x P B

29. Comparison between Rodriguez-Brito's method and z test method.

30. Clustering-based Comparison GOS ORF clusters r AB No. of cluster

31. Clustering-based Comparison BIOME samples are more diverse than GOS BIOME clusters

32. Protein Family-based Comparison Merge Pfam, Tigrfam and COG into super families – Pfam- clans, Tigrfam- role categories, and COG- functional classes Compare with a specific super family

33. Protein Family-based Comparison (a) GOS on COG Class F, (b) GOS on COG Class T, (c) BIOME on COG Class F, (d) BIOME on COG Class T

34. Conclusion RAMMCAP improve performance – CD-HIT – z test Novel protein families discovery – ORFs clustering Metagenome comparison – Cluster-based – Protein family-based

35. Discussion How much improvement when apply RNA prediction before raw reads? How to determine significant factor? – P A ≧ f * P B (f>1)