1. SNP Discovery in Whole-Genome Light-Shotgun 454 Pyrosequences Aaron Quinlan 1, Andrew Clark 2, Elaine Mardis 3, Gabor Marth 1 (1) Department of Biology, Boston College (2) Departments of Molecular Biology and Genetics, Cornell University (3) Departments of Genetics and Molecular Microbiology, Washington University AGBT 2007. Marco Island, FL. February 9, 2007

2. 454 machines have been proven for several applications genome sequencing microRNA discovery mutation detection in cancer tissue

3. 454 machines trade off throughput with read length read length bases per run 10 bp 1Gb 1,000 bp100 bp 100 Mb 10 Mb 1Mb

4. 454 shotgun reads for SNP discovery genome size bases per run 1 Mb1 Gb100 Mb 10 Mb 10 Gb for 100Mb genomes a few 454 runs produce ~ 1x coverage at ~ 1x the genome is fairly densely covered still, most 454 reads align as singletons

5. Are single-coverage 454 reads resulting from light- shotgun sequencing accurate enough for SNP discovery? melanogster reference genome sequence (iso-1 strain) 454 shotgun reads from an African melanogaster isolate (strain id 46-2) African melanogaster strain courtesy of Dr. Charles Langley, UC Davis 454 sequencing at the Washington University Genome Sequencing Center

6. Steps of SNP discovery Sequence clustering and organization Multiple fragment alignment SNP detection Paralog identification

7. SNP discovery in capillary traces hinges on base quality most errors come from substitutions, i.e. calling the wrong base in Sanger-principle capillary sequences the number of bases is generally well resolved substitution errors are well described by the PHRED base quality values allowing us to distinguish between sequencing error and true polymorphism, detect and score candidate SNPs

8. Most 454 errors are over-calls or under-calls in 454 reads one the identity of the nucleotide is usually accurate, but the number of bases is often unclear most errors are over-calls or under-calls errors don’t necessarily occur in “low quality” regions of the read, and PHRED base quality values do not describe over- and under-call errors Separate out alignments!!!

9. How many bases were incorporated? nucleotide incorporation tests light signal 0.091.5 ? the number of bases in a mono-nucleotide run has to be inferred from the signal intensity, but this inference is often not trivial a signal is also produced when, in fact, no nucleotide is incorporated signal intensity is variable for a given # incorporated bases Add cartoon scale on sides!!!

10. The base number probabilities conversely, for a given signal intensity (e.g. 1.5), the true number of incorporated nucleotides is either 1 or 2 (and sometimes even 3 or 0) histogram of observed signal intensities for different numbers of actually incorporated bases our base caller calculates and reports the base number probabilities i.e. the (posterior) probability that given the observed incorporation signal 0, 1, 2, …, etc. bases were incorporated, e.g. P(0C), P(1C), Pr(2C), …, etc. these base number probabilities address under- and over-calls and replace the PHRED base quality values for 454 reads Annotate 0, 1, 2!!! Figga Mo’ bigga!!!

11. PyroBayes – our 454 base caller Use data likelihood from last page!!! Add Bayesian equation!!!

12. Mapping / sequence alignment simple BLAT approach to map 454 reads ACGACAGGGATGCGTGGGA TTGATGACTAGTAACGACAGGGACGCGTGGGAAGGTTAGTACCGTAC unique pair-wise alignments kept 454 reads that align to multiple locations in the genome (paralogous sequences) are removed

13. SNP calling for 454 reads the genome reference allele (C) is wrong and, in fact, the reference allele is T (from PHRAP base quality value) the 454 allele (T) is the result of over-call, and one of the C nucleotide tests just before or after was an under-call… Given an apparent mismatch between the genome reference sequence (C allele) and the 454 read (T allele) we have to consider the possibility that: The result is a SNP probability score that our SNP caller reports ACGACAGGGATGCGTGGGA ACGACAGGGACGCGTGGGA ACGACAGGGATGCGTGGGA ACGACAGGGACGCGTGGGA … we use the base number probabilities To evaluate sequence differences… P(0C) would not be available from PHRED

14. The SNP discovery pipeline ACGACAAGGCGTGGGA 454 base calling read mapping ACGACAGGGATGCGTGGGA TTGATGACTAGTAACGACAGGGACGCGTGGGAAGGTTAGTACCGTACTGGGA SNP calling + thresholding Pr(C/T) (341,600 reads called) (220,121 reads uniquely mapped) (41,265 candidate SNPs)

15. SNP candidate validation we attempted experimental validation for 1,549 randomly chosen candidates each candidate was PCR-amplified and sequenced on ABI capillary machines. 1,114 of 1,231 candidates were confirmed (318 could not be assayed). 90.5% true positive rate

16. Melanogaster SNPs from a single 454 run SNPs were evenly distributed on melanogaster autosomes (chr. 4 is almost completely heterochromatic) Average density: 1 SNP per 2.9 kb melanogaster genome sequence 81.4% of SNPs were discovered in a single 454 read vs. the genome reference 1 SNP per 530 bp aligned 454 sequence

17. SNPs for a melanogaster genotyping chip some SNP alleles we discovered are likely singletons (alleles only present in the reference or the African strain, but not in the entire melanogaster “population”) but we know from population genetic theory that SNP discovery (ascertainment) in a pair of chromosomes enriches for common variants most useful as genetic markers 40K SNPs with 90%+ validation rate from a single 454 run probably sufficient for a genotyping chip for larger genomes / denser maps multiple 454 runs will be needed

18. Ongoing 454 data mining projects 10 different melanogaster strains mammalian projects: larger genome size requires reduced genome representation strategy (RRS) RRS shotgun reads provide deeper sequence coverage in “target” regions

19. Refinements of the 454 data analysis pipeline improved base calling gives higher accuracy effective anchored aligners and SNP callers for deep alignments address more data and deeper alignments from RRS strategies extended SNP calls for all substitutions and INDELs gives more SNPs

20. Thanks Elaine Mardis Wash. U. Andy Clark Cornell University Eric Tsung Chip Stewart Michael Stromberg Tony Nguyen Aaron Quinlan Boston College Weichun Huang Michele Busby Damien Croteau- Chonka bioinformatics.bc.edu/marthlab

21. base callers for 454 and short-read sequencing machines reference guided, “anchored” alignment programs SNP callers for deep 454 alignments and for short read alignments

22. SNP calling – filters TCGCGTATGCG TCTCGTATGCG Reference Afr. 454 seq. TCGCGTATGCG TCCCGTATGCG Reference Afr. 454 seq. TCGCCTACGCG TCGCGTTCGCG Reference Afr. 454 seq. only considered candidate SNPs that were the least likely the result of a 454 over-call or under-call only considered candidate SNPs with SNP probability score > 0.9