1. Metagenomics Assembly Hubert DENISE (hudenise@ebi.ac.uk)

2. 2 main approaches:  building a consensus (“overlap–layout–consensus”)  generating De Bruijn k-mer graphs Metagenomics assembly

3. I_like_EBI_metagenomics Genomics assembly: building a consensus I_like_EBI_metagenomics read-depth high low I_like_EBI_met ike_EBI_metage _EBI_metagenom I_metagenomics _like_EBI_meta BI_metagenomic Based on ‘word’ overlap reads contig

4. Metagenomics assembly: building a consensus Issues: read length and repeated sequences ???... ???

5. Genomics assembly: building a consensus Practical solution : using coverage / read-depth information Coverage: ratio between contigs 3111 Allow the elimination of one of the possible assembly:

6. Genomics assembly: building a consensus Practical solution : using pair-end reads Pair-ends: Distance information between sequences Allow the identification of the correct assembly:

7. Genomics assembly: De Bruijn k-mer graphs k-mers generated by breaking reads into multiple overlapping words of fixed length (k) I_like_EBI_metagenomics k=5 e_EBI ke_EB ike_E like_ _like I_lik _EBI_ EBI_m BI_me I_met _meta metag etage tagen ageno genom enomi nomic omics

8. Branches in the graph represent partially overlapping sequences. T. Brown, 2012 Genomics assembly: using k-mers Each node represents a 14-mer; Links between each node are 13-mer overlaps 14mer k=14

9. Single nucleotide variations cause k-long branches; They don’t rejoin quickly. Genomics assembly: using k-mers T. Brown, 2012

10. Genomics assembly: De Bruijn k-mer graphs Building the graph is demanding but navigation through is quick and memory efficient.  branches : ambiguity in assembly  short dead-end branches: low coverage  bubbles: sequencing errors or polymorphism ?  converging and diverging paths: repeats therefore there is a need for biological knowledge and other sequences information to fully reconstruct a genome J.R. Miller et al. / Genomics (2010)

11. There is a number of (+/- metagenome-adapted) solutions out there:  MetaVelvet, MetaIDBA and khmer “partition” the assembly de Brujn graph into sections from different organisms, and then assemble those individually. This allows them to adjust coverage parameters “locally”.  Genovo uses a 'generative probabilistic model' to identify likely sequence reconstructions  Euler deals with repeats by identifying an Eulerian path (visiting every edge only once) in the De Bruijn graph.  and SOAPdenovo (graph), Newbler (for 454, consensus), MetAMOS… Metagenomics assembly: what to use ?

12. Butler et al., Genome Res, 2009 Genomics assembly: choosing k-mer Tools such as Velvet Advisor ( http://dna.med.monash.edu.au/~torsten/velvet_advisor/ ) are available

13. Judging genomics assembly parameters 1parameters 2 measurements: number of contigs (1) length of contigs (2) nucleotides involved (1) N50weighted median such that 50% of the entire assembly is contained in contigs equal to or larger than this value How to judge the better assembly in absence of external information ?

14. Judging metagenomics assembly parameters 1parameters 2 total length: 17 contigs: 7 N50 = 3 total length: 15.5 contigs: 5 N50 = 2 Therefore the assembly obtained with parameters 2 will be considered the best Calculating N50:- order the sequences by decreasing length, - add length until 50% of nucleotides reached

15. Judging metagenomics assembly parameters 1parameters 2 For metagenomics, in addition to N50, we can also use the fact that sequences are originating from different species -% GC will vary between species (20 to 80%) and therefore contigs from different species could be separated from each others. -all predicted CDSs from a single contig should be annotated as being from same species (using Blast for example).

16. EBI Metagenomics currently do not perform assembly Why ?  absence of reference genome  short reads make chimaera inevitable EBI Metagenomics pipeline validation: What are the consequences of not performing assembly?  cannot link taxonomy information to functional annotations  cannot currently perform viral taxonomy analysis Ex: re-analysis of Hess et al, Science (2011) 331:463

17. http://www.ebi.ac.uk/metagenomics/ http://metagenomics.anl.gov/ http://camera.calit2.net/ http://img.jgi.doe.gov/ Public Metagenomics portals Do not perform assembly but accept assembled data Perform assembly

18. Hubert DENISE (hudenise@ebi.ac.uk)