Informatics tools for next-generation sequence analysis Gabor T. Marth Boston College Biology Department University of Michigan October 20, 2008
Next-gen. sequencers offer vast throughput read length bases per machine run 10 bp1,000 bp100 bp 1 Gb 100 Mb 10 Mb 10 Gb Illumina, AB/SOLiD short-read sequencers ABI capillary sequencer 454 pyrosequencer ( Mb in bp reads) (5-15Gb in bp reads) 1 Mb
Next-gen sequencing enables new applications Meissner et al. Nature 2008 Ruby et al. Cell, 2006 Jones-Rhoades et al. PLoS Genetics, 2007 organismal resequencing & de novo sequencing transcriptome sequencing for transcript discovery and expression profiling epigenetic analysis (e.g. DNA methylation)
Large-scale individual human resequencing
Technologies
Roche / 454 system pyrosequencing technology variable read-length the only new technology with >100bp reads
Illumina / Solexa Genome Analyzer fixed-length short-read sequencer very high throughput read properties are very close to traditional capillary sequences
AB / SOLiD system ACGT A C G T 2 nd Base 1 st Base fixed-length short-reads very high throughput 2-base encoding system color-space informatics
Helicos / Heliscope system short-read sequencer single molecule sequencing no amplification variable read-length error rate reduced with 2- pass template sequencing
Data characteristics
Read length read length [bp] ~ (variable) (fixed) (fixed) (variable) 400
Representational biases this affects genome resequencing (deeper starting read coverage is needed) will have major impact is on counting applications “dispersed” coverage distribution
Amplification errors many reads from clonal copies of a single fragment early PCR errors in “clonal” read copies lead to false positive allele calls early amplification error gets propagated into every clonal copy
Read quality
Error rate (Illumina)
Error rate (454)
Per-read errors (Solexa)
Per read errors (454)
Base quality values not well calibrated
Tools for genome resequencing
The resequencing informatics pipeline (iii) read assembly REF (ii) read mapping IND (i) base calling IND (iv) SNP and short INDEL calling (vi) data validation, hypothesis generation (v) SV calling
The variation discovery “toolbox” base callers read mappers SNP callers SV callers assembly viewers
1. Base calling base sequence base quality (Q-value) sequence diverse chemistry & sequencing error profiles
454 pyrosequencer error profile multiple bases in a homo-polymeric run are incorporated in a single incorporation test the number of bases must be determined from a single scalar signal the majority of errors are INDELs
454 base quality values the native 454 base caller assigns too low base quality values
PYROBAYES: determine base number
PYROBAYES: Performance assigned quality values predict measured error rate better higher fraction of bases are high quality
Base quality value calibration
Recalibrated base quality values (Illumina)
… and they give you the picture on the box 2. Read mapping Read mapping is like doing a jigsaw puzzle… …you get the pieces… Unique pieces are easier to place than others…
Non-uniqueness of reads confounds mapping Reads from repeats cannot be uniquely mapped back to their true region of origin RepeatMasker does not capture all micro-repeats, i.e. repeats at the scale of the read length
Strategies to deal with non-unique mapping Non-unique read mapping: optionally either only report uniquely mapped reads or report all map locations for each read (mapping quality values for all mapped reads are being implemented) read mapping to multiple loci requires the assignment of alignment probabilities
Paired-end reads help unique read placement fragment amplification: fragment length bp fragment length limited by amplification efficiency Korbel et al. Science 2007 circularization: 500bp - 10kb (sweet spot ~3kb) fragment length limited by library complexity PE MP PE reads are now the standard for genome resequencing
MOSAIK
INDEL alleles/errors – gapped alignments 454
Aligning multiple read types together ABI/capillary 454 FLX 454 GS20 Illumina Alignment and co- assembly of multiple reads types permits simultaneous analysis of data from multiple sources and error characteristics
Aligner speed
3. Polymorphism / mutation detection sequencing error polymorphism
New challenges for SNP calling deep alignments of 100s / 1000s of individuals trio sequences
Rare alleles in 100s / 1,000s of samples
Allele discovery is a multi-step sampling process Population SamplesReads
Capturing the allele in the sample
Allele calling in the reads base call sample size individual read coverage base quality
Allele calling in deep sequence data aatgtagtaAgtacctac aatgtagtaCgtacctac aatgtagtaAgtacctac Q30Q40Q50Q
More samples or deeper coverage / sample? Shallower read coverage from more individuals … …or deeper coverage from fewer samples? simulation analysis by Aaron Quinlan
Analysis indicates a balance
SNP calling in trios the child inherits one chromosome from each parent there is a small probability for a mutation in the child
SNP calling in trios aatgtagtaAgtacctac aatgtagtaCgtacctac aatgtagtaAgtacctac aatgtagtaCgtacctac mother father child P=0.79 P=0.86
Determining genotype directly from sequence AACGTTAGCATA AACGTTCGCATA AACGTTAGCATA individual 1 individual 3 individual 2 A/C C/CC/C A/A
4. Structural variation discovery
SV events from PE read mapping patterns
Deletion: Aberrant positive mapping distance
Copy number estimation from depth of coverage
Alignability – read coverage normalization
Het deletion “revealed” by normalization
Tandem duplication: negative mapping distance
Spanner – a hybrid SV/CNV detection tool Navigation bar Fragment lengths in selected region Depth of coverage in selected region
5. Data visualization 1.aid software development: integration of trace data viewing, fast navigation, zooming/panning 2.facilitate data validation (e.g. SNP validation): simultanous viewing of multiple read types, quality value displays 3.promote hypothesis generation: integration of annotation tracks
Data visualization
Our software tools for next-gen data
Data mining projects
SNP calling in short-read coverage C. elegans reference genome (Bristol, N2 strain) Pasadena, CB4858 (1 ½ machine runs) Bristol, N2 strain (3 ½ machine runs) goal was to evaluate the Solexa/Illumina technology for the complete resequencing of large model-organism genomes 5 runs (~120 million) Illumina reads from the Wash. U. Genome Center, as part of a collaborative project lead by Elaine Mardis, at Washington University primary aim was to detect polymorphisms between the Pasadena and the Bristol strain
Polymorphism discovery in C. elegans SNP calling error rate very low: Validation rate = 97.8% (224/229) Conversion rate = 92.6% (224/242) Missed SNP rate = 3.75% (26/693) SNP INS INDEL candidates validate and convert at similar rates to SNPs: Validation rate = 89.3% (193/216) Conversion rate = 87.3% (193/221) MOSAIK aligned / assembled the reads (< 4 hours on 1 CPU) PBSHORT found 44,642 SNP candidates (2 hours on our 160-CPU cluster) SNP density: 1 in 1,630 bp (of non-repeat genome sequence)
Mutational profiling: deep 454/Illumina/SOLiD data Pichia stipitis converts xylose to ethanol (bio-fuel production) one mutagenized strain had especially high conversion efficiency determine where the mutations were that caused this phenotype we resequenced the 15MB genome with 454 Illumina, and SOLiD reads 14 true point mutations in the entire genome Pichia stipitis reference sequence Image from JGI web site
Technology comparisons
Thanks
Credits Elaine Mardis Andy Clark Aravinda Chakravarti Doug Smith Michael Egholm Scott Kahn Francisco de la Vega Kristen Stoops Ed Thayer
Lab
Recruitment