1. NGS Data Generation Dr Laura Emery

2. Overview The NGS data explosion Sequencing technologies An example of a sequencing workflow Bioinformatics challenges

3. The NGS data explosion

4. EBI biological data TB of data

5. Bottlenecks to biological research Source: Qiagen

6. NGS Technologies A variety of platforms available Differ in: Library preparation Sequencing chemistry

7. Comparison of NGS Technologies Library preparation Sequencing chemistry Features Roche 454Emulsion PCR PyrosequencingLonger read length, only available until 2016 Illumina HiSeq Solid phase amplification Reversible terminator Best output to cost ratio, low error rates Applied Biosciences SOLiD Emulsion PCR Sequencing by ligation Highest accuracy Pacific Biosciences RS II Single molecule Real timeVery long read lengths, highest error rates

8. Example: Illumina NGS workflow 4. Data Analyses 3. Sequencing 2. Hybridisation and Amplification 1. Library Preparation

9. 1. Library preparation RNA extraction Fragmentation and size selection cDNA synthesis Adapter ligation RNA only

10. 1. Library preparation Alternative library preparation methods: Mate pairTargetedStrand specific

11. 1. Library preparation Multiplexing (optional)

12. Example: Illumina NGS workflow 4. Data Analyses 3. Sequencing 2. Hybridisation and Amplification 1. Library Preparation

13. 2. Hybridisation and Amplification

14. Example: Illumina NGS workflow 4. Data Analyses 3. Sequencing 2. Hybridisation and Amplification 1. Library Preparation

15. 3. Sequencing Errors!

16. 3. Sequencing (Paired-end)

17. Example: Illumina NGS workflow 4. Data Analyses 3. Sequencing 2. Hybridisation and Amplification 1. Library Preparation

18. 4. Data analyses: generalised pipeline Data submission to public repository Downstream analyses Alignment and/or assembly Filtering QC FASTQ Other data

19. Bioinformatics challenges Library preparation biases Random hexamer priming GC content Data storage Data analysis Errors Mapping/assembly uncertainty

20. Bioinformatics challenges Library preparation biases Random hexamer priming GC content Data storage Data analysis Errors Mapping/assembly uncertainty Sequence bias in the first 13 nucleotides Methods for correction: Cufflinks, mmseq

21. Bioinformatics challenges Library preparation biases Random hexamer priming GC content Data storage Data analysis Errors Mapping/assembly uncertainty GC-rich or AT-rich fragments have been found to be over/underrepresented Methods for correction: EDASeq, CG correct

22. Bioinformatics challenges Library preparation biases Random hexamer priming GC content Data storage Data analysis Errors Mapping/assembly uncertainty

23. Conclusions NGS technologies provide us with new opportunities but new challenges You will learn more about overcoming these challenges during this course Furthermore, other omics technologies will be introduced

24. So over to Bernardo…