SNP Discovery in Whole-Genome Light-Shotgun 454 Pyrosequences

Aaron Quinlan1, Andrew Clark2, Elaine Mardis3, Gabor Marth1

(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

454 machines have been proven for several applications

• genome sequencing

• microRNA discovery

• mutation detection in cancer tissue

454 machines trade off throughput with read length

read length

base

s per

run

10 bp

1Gb

1,000 bp100 bp

100 Mb

10 Mb

1Mb

454 shotgun reads for SNP discovery

genome size

base

s per

run

1 Mb 1 Gb100 Mb

100 Mb

10 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

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

Steps of SNP discovery

Sequence clustering and organization

Multiple fragment alignment

SNP detection

Paralog identification

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

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!!!

How many bases were incorporated?

nucleotide incorporation tests

lig

ht

sig

nal

0.09 1.5

?• 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!!!

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!!!

PyroBayes – our 454 base caller

Use data likelihood from last page!!!Add Bayesian equation!!!

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

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

The SNP discovery pipeline

ACGACAAGGCGTGGGA 454 base calling

read mappingACGACAGGGATGCGTGGGA

TTGATGACTAGTAACGACAGGGACGCGTGGGAAGGTTAGTACCGTACTGGGA

SNP calling + thresholdingPr(C/T)

(341,600 reads called)

(220,121 reads uniquely mapped)

(41,265 candidate SNPs)

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

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

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

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

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

Thanks

Elaine MardisWash. U.

Andy ClarkCornell University

Eric Tsung

Chip StewartMichael Stromberg

Tony Nguyen

Aaron QuinlanBoston College

Weichun Huang

Michele Busby Damien Croteau-Chonka

bioinformatics.bc.edu/marthlab

• 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

SNP calling – filters

TCGCGTATGCGTCTCGTATGCG

Reference

Afr. 454 seq.

TCGCGTATGCGTCCCGTATGCG

Reference

Afr. 454 seq.

TCGCCTACGCGTCGCGTTCGCG

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