Future applications of full length virus genome sequencing

Transcription

1 Future applications of full length virus genome sequencing Paul Kellam Virus Genomics

2 Revisiting early HIV resistance ideas AIDS 1991 Nature 1993

3 Virus genome sequencing Population or single genome

4 Whole genome sequencing Primer-walking with M13 adaptors, capillary sequencing

5

6 Population biology The consensus sequence The treatment The minority species

7 2nd generation: 454 sequencing Throughput: 500 Mb/run (GSFLX) 1 M reads/run Read length: ~500 bp

8 HIV 2 nd generation sequencing Drug resistance mutations Drug Resistance population structure ~400b.p (inc V3) Fragmented (nebulised 454 library)

9 Direct indexing for 454 b)

10

11 Compound errors in 2 nd generation sequencing Process cdna synthesis error rate Library representation PCR error rate for clusters Sequencing error rates Technical Sampling efficiency Robustness of process Cost effectiveness Mutliplexing/logistics

12 Coverage and errors in 454 sequences Wang et al, Genome Res :

13 2 nd generation ATCATCTTCCTCACGACGTTTGCCAATTTAGCCTTCTTCTCNTCCCCTCCGACT + ATAATGGATAAAACCATCATATTGAAAGCAAACTTCAGTGTGATTTTTGACCGG + CCCBC?BCCCCACCCCCCCCCCCCCCC ATATTCTGGAGCAATGAAATTTCCATTACTCTCGAAGTTGATTGCATCATTCGG + BCBCCBCBCBCCCCCACCCCCCCBCC@ ATTTGGCGTCAAGCGAACAATGGAGAGGACGCAACTGCTGGTCTTACCCACCTG + =BBBBBBB@B>?B6BB=B>BBBBB?>A CCTGATGTGTATTTCTTGGTTATGGCCATCTGGTCCACAGTGGTTTTTGTTAGT + ADA>2????:>BBAB>BBBA?.?<?BB BB1?????BAB9A89;0:??>B;;8B6 Etc to ~ 1-3 million? means 99.9% accurate

14 Phred QPHRED = -10 x Log10 (Pe)

15 Phred scores Phred Qual score Prob that base is called wrongly Accuracy of base call ASCI code 10 1 in 10 90% in % in %? 40 1 in % I 50 1 in % S

16 assemblypipeline_splitqa.sh Script splitfastq_by_mid 454FastaFromSFF FASTQ SFF JPEG Fastq_QA SAM/BAM assemblypipeline_qcqa.sh Fastq_QC Fastq_QA SSAHA2 assemblypipeline_ssahamap.sh SAMTools pileupconsensu s

17 1/48 th of a 454 plate (1/12 th of a ¼ plate)

18

19 0 560

20 Phred 25

21 Sequencing whole virus genomes Bluetongue Virus Influenza Virus Varicella Zoster Virus

22 2nd generation: Illumina (Solexa)) sequencing Fragment DNA and add adapters Bind fragments to flow cell Bridge amplification Denature to return to single stranded DNA Images from

23 2nd generation: Illumina (Solexa)) sequencing mages from Produces millions of DNA clusters Add all 4 labelled terminators Lazer excitation causes flourescence which is photographed Repeat these sequencing cycles

24 Illumina Influenza H5N1Pre- & Post Quality Filtering

25 454-Illumina Coverage Comparison PB2 PB1 PA HA NP NA M1/M2 NS1/NS2

26 Dataset Platform Total Reads Mean Read Length Ref Coverage % Min Coverage Max Coverage , , H5N1 Illumina 1,669, ,013 61,435

27 Comparison of platforms consensus sequences Reference Position Called Base 454 Illumina 5615 A R (A or G) 5621 C Y (C or T) 5624 T W (A or T) 6900 G S (C or G) 8472 A W (A or T) 8477 G R (A or G) 8715 A G (difference) 8937 T K (G or T) 9623 G K (G or T) T K (G or T) T K (G or T) A W (A or T) 12 differences / 13,500 bp genome = 0.089%

28 Consensus and population structure Frequency Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 Patient 7 Patient 8

29 The problem of linkage

30 Diversity and phenotypic potential Kellam & Larder, J.Virol 1995, 69(2); 669

31 Quasispecies haplotype reconstruction Eriksson et al, Plos Comp Biol May 2008, 4(5); e

32 Conclusions Move towards many more consensus whole genomes and abstraction of population structure Drive down/control/filter for sequencing errors. 3 rd generation (end 2010) will produce longer reads Learn more from ecologists Considerations of genome to infectivity ratio s

33 Virus Genomics Team Astrid Gall Anne Palser Rachael Chiam Greg Baillie HIV; Myra McClure, Deenan Pillay Influenza; James Wood, Maria Zambon BTV; Massimo Palmarini & Peter Mertens VZV; Judy Breuer Presented at the 8th European HIV Drug Resistance Workshop, March , Sorrento, Italy Simon Watson