1. Next generation sequencing Why? What? How? Marcel Dinger Developmental Biology Divisional Seminar 7 October 2010

2. Applications of Next-Generation Sequencing

3. Next-Generation Sequencing Workflow Illumina, Roche 454 or ABI SOLiD? Illumina, Roche 454 or ABI SOLiD?

4. Sample generation and cluster generation 200,000 clusters per tile 62.5 million reads per lane 100 bp reads -> 12.5 Gb per lane 200,000 clusters per tile 62.5 million reads per lane 100 bp reads -> 12.5 Gb per lane

5. Cluster generation and preparation for sequencing

6. Sequencing by Synthesis (SBS)

8. Base Calling

9. Terminology Single end - sequence of the first 50-100 nt of DNA fragments Paired end - sequence ~75 nt from each end of the fragment. Fragment length can be adjusted from 200-500 nt. Mate pair - sequence of the first 50-100 nt of DNA fragments

10. cDNA normalization One disadvantage of RNA-seq is the “diminishing returns” with increased sequencing depth i.e. majority of reads represent common RNAs. To detect rare transcripts, very deep sequencing is necessary. One disadvantage of RNA-seq is the “diminishing returns” with increased sequencing depth i.e. majority of reads represent common RNAs. To detect rare transcripts, very deep sequencing is necessary. Ribo-minus, capture arrays and cDNA normalization reduce this problem. cDNA normalization can now be achieved simply with a duplex-specific nuclease based approach. Normalized RNA can no longer be used for quantitative expression studies, but is essential for rare transcript discovery and characterization

11. Target enrichment (sequence capture)

12. Where do I start? Commercial Commercial Advantages Quality guarantee Access to cutting-edge Advantages Quality guarantee Access to cutting-edge Disadvantages Expensive Disadvantages Expensive Collaborate Collaborate Advantages Cheaper Free support and expertise Advantages Cheaper Free support and expertise Disadvantages No guarantees Slower Share authorship Disadvantages No guarantees Slower Share authorship

13. Commercial providers Contact: Mark Crowe http://www.agrf.org.au Platform: Illumina GAIIx, Roche 454 and 2 HiSeq 2000 (coming soon) Contact: Rob King http://geneworks.com.au Platform: Illumina GAIIx Contact: Karolina Janitz http://ramaciotti.unsw.edu.au Platform: Illumina GAIIx and Roche 454

14. Collaborative options qbi Contact: Vikki Marshall Platform: Illumina HiSeq 2000 (from December) Contact: Evgeny Glazov Platform: Illumina GAIIx, 2-3 HiSeq 2000 (coming soon) Contact: Peter Wilson / Sean Grimmond Platform: 11 ABI SOLiD (committed to ICGC till at least 3Q 2011), other technology, e.g. IonTorrent forthcoming? Platform: 11 ABI SOLiD (committed to ICGC till at least 3Q 2011), other technology, e.g. IonTorrent forthcoming?

15. What will it cost? At least two stages in next-gen sequencing: library preparation and sequencing Extra costs for capture arrays, normalization etc At least two stages in next-gen sequencing: library preparation and sequencing Extra costs for capture arrays, normalization etc

16. What will it cost? Costs can be dramatically lower if library preparation is down in-house and if working with a collaborator such as Diamantina or QBI (<$2,500 per lane) Total RNA required depends on protocol - count on at least 100 ng (but as low as 1 ng is possible!). Small RNA needs to be considered independently of long RNA.

17. Data analysis Next generation sequencing data is really BIG. Genomes typically need to be covered 30-fold to get good assembly and be able to detect SNPs For RNA-sequencing 30-100 million tags are necessary for each time point for differential expression studies and for coverage of rare transcripts and isoforms As well as a server with a lot of memory and processors, terabytes of space are required for analysis and organization of next-gen sequencing data Cost of data analysis will be much greater than the cost of the sequencing (consider expertise in the lab, collaborate with an informatics group or engage with commercial service, such as QFAB) Software for next-gen sequence analysis is improving, but still in its infancy. Considerable computional expertise is necessary to get the most out of the data.

18. Summary Next generation sequencing (NGS) is transforming molecular biology NGS can intersect and contribute to (and even revolutionize!) practically any research program NGS is not prohibitively expensive... but does require some bioinformatics expertise to get the most from the data (remember to factor this into your grant applications!) Experimental planning is critical: before embarking on an NGS experiment, be sure to talk with the people that are going to be analyzing the data.

19. Acknowledgments Mark Crowe (AGRF) Evgeny Glazov (DI) Karolina Janitz (Ramaciotti) Rob King (GeneWorks) Arjuna Kumarasuriyar (Illumina) Vikki Marshall (QBI) Peter Wilson (QCMG) Mark Crowe (AGRF) Evgeny Glazov (DI) Karolina Janitz (Ramaciotti) Rob King (GeneWorks) Arjuna Kumarasuriyar (Illumina) Vikki Marshall (QBI) Peter Wilson (QCMG) Questions?? Questions??