PHYTOPHTHORA GENOME SEQUENCING: A case study

1. PHYTOPHTHORA GENOME SEQUENCING: A case study Santhosh J. Eapen sjeapen@spices.res.in

2. Status of whole genome sequencing of Phytophthora spp.

3. Phytophthora Whole Genome Sequencing The sequencing platform was Illumina Genome Analyzer The sequence base calling, alignment, and variant analysis were done using CASAVA v1.7 (short for "Consensus Assessment of Sequence And VAriation“). Maq software was used for assembly and variant detection using reference genome. P. capsici genome of JGI was used as the reference genome

4. Alignment status and reports • Number of reference scaffolds : 917 • Length of reference sequences excluding gaps : 56042007 • Length of gaps in the reference sequences : 8005190 • Length of non-gap regions covered by reads : 22593594 • GC% : 50.4 • Total Reads : 15849154 • Reads Aligned : 48.8738 • Total Genome Size : 64022747 • Genome Covered : 28234853 • %Coverage : 44.1013 • Average Read Depth : 1.50491 • Average depth across all non-gap regions : 11.284 • Average depth across 24 bpunique regions : 1.565 • % Coverage at 1X : 54.8897 • Single Nucleotide Variants at 3X cutoff : 330410

5. Base composition and genome size of P. capsici Total genome size = 64022747 (64 Mb)

6. SNP and InDel details

7. Variant Annotation Output

8. Structural Annotation- • Structural Annotation was conducted using AUGUSTUS (version 2.5.5), Magnaporthe_grisea as genome model • However, we have to develop genome model for Oomycete to obtain accurate result

9. Gene annotation

10. Functional Annotation

11. Functional Annotation Result • Functional Annotation for negative strand is complete

12. Comparison with Phytophthora capsici (JGI)

13. Comparison with Phytophthora infestans (Maq)

14. Comparison with Phytophthora ramorum

15. Comparison with Phytophthora sojae

16. Comparison of P. capsici with P. capsici (JGI), P. infestans, P. ramorum& P. sojae

26. GenomeView - next-generation stand-alone genome browser Visualize and manipulate a huge number of genomics data Browse high volumes of aligned short read data, with dynamic navigation and semantic zooming, from the whole genome level to the single nucleotide Enables visualization of whole genome alignments of dozens of genomes relative to a reference sequence. Handle thousands of annotation features and millions of mapped short reads

29. Future Plans • To assign putative functions to the remaining genes • Provide a genome wide comparison with other sequenced Phytophthora species • More genomes to be sequenced

30. Data, data, everywhere but ... is it knowledge? • Five oomycete genome sequences are available and several more are on the way • The rate of new sequence generation is accelerating extraordinarily with next generation technologies • Even today the ability to generate high throughput sequencing and transcriptomic data is outstripping the ability to transform the data into knowledge • Automated data processing pipelines are not a substitute for human insight

31. Life in a data-rich environment Every experimental biologist needs to be a computational biologist too

32. Some concluding remarks • Trust but verify • Beware of gene prediction tools! • Always use more than one gene prediction tool and more than one genome when possible. • Active area of bioinformatics research, so be mindful of the new literature in this .

33. Other factors • Changing technology • New and disappearing companies? • Changing price structure • Cost of machine • Cost of operation (reagents/people) • Service from the company • 1 machine vs (2 or 3 machines) vs 40 machines. • Changing software and processing

34. What have we learned? • Sequencing technologies are changing fast • Allowing new biology to be performed, new questions to be asked • Understand the difference between some of the technologies

35. What next?

36. Phytophthora 2011: RRII, Kottayam