next-generation sequence analysis gabor t. marth boston college biology department psb 2008 january...
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Next-generation sequence analysis
Gabor T. MarthBoston College Biology Department
PSB 2008January 4-8. 2008
Read length and throughput
read length
base
s p
er
mach
ine r
un
10 bp 1,000 bp100 bp
100 Mb
10 Mb
1Mb
1Gb
Illumina/Solexa, AB/SOLiD short-read sequencers
ABI capillary sequencer
454 pyrosequencer(20-100 Mb in 100-250 bp reads)
(1-4 Gb in 25-50 bp reads)
DNA ligation DNA base extension
Church, 2005
Sequencing chemistries
Template clonal amplification
Church, 2005
Massively parallel sequencing
Church, 2005
Features of NGS data
• Short sequence reads– 100-200bp– 25-35bp (micro-reads)
• Huge amount of sequence per run– Up to gigabases per run
• Huge number of reads per run– Up to 100’s of millions
• Higher error as compared with Sanger sequencing– Error profile different to Sanger
Current and future application areas
Genome re-sequencing: somatic mutation detection, organismal SNP discovery, mutational profiling, structural variation discovery
De novo genome sequencing
Short-read sequencing will be (at least) an alternative to micro-arrays for:
• DNA-protein interaction analysis (CHiP-Seq)• novel transcript discovery• quantification of gene expression• epigenetic analysis (methylation profiling)
DELSNP
reference genome
Fundamental informatics challenges
1. Interpreting machine readouts – base calling, base error estimation
2. Dealing with non-uniqueness in the genome: resequenceability
3. Alignment of billions of reads
Informatics challenges (cont’d)
5. Data visualization
4. SNP and short INDEL, and structural variation discovery
6. Data storage & management
Challenge 1. Base accuracy and base calling
• machine read-outs are quite different
• read length, read accuracy, and sequencing error profiles are variable (and change rapidly as machine hardware, chemistry, optics, and noise filtering improves)
• what is the instrument-specific error profile?• are the base quality values satisfactory?
(1) are base quality values accurate? (2) are most called bases high-quality?
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
• error rates are nucleotide-dependent
454 base quality values
• the native 454 base caller assigns too low base quality values
PYROBAYES: determine base number
data likelihood
s
priors
posterior base number probability
New 454 base caller:
PYROBAYES: base calls and quality values
• call the most likely number of nucleotides
• produce three base quality values: QS (substitution)QI (insertion)QD (deletion)
PYROBAYES: Performance
• better correlation between assigned and measured quality values
• higher fraction of high-quality bases
Illumina/Solexa base accuracy
• error rate grows as a function of base position within the read
• a large fraction of the reads contains 1 or 2 errors
Illumina/Solexa base accuracy (cont’d)
• Actual base accuracy for a fixed base quality value is a function of base position within the read (i.e. there is need for quality value calibration)
• Most errors are substitutions PHRED quality values work
3’ 5’
N N N T G z z z
3’ 5’
N N N G A z z z
3’ 5’
N N N A T z z z
2-base, 4-color: 16 probe combinations
● 4 dyes to encode 16 2-base combinations● Detect a single color indicates 4 combinations & eliminates 12 ● Each color reflects position, not the base call● Each base is interrogated by two probes● Dual interrogation eases discrimination
– errors (random or systematic) vs. SNPs (true polymorphisms)
A C G T
A
C
G
T
2nd Base
1st
Bas
e
0
0
0
0
1
1
1
1
2
2
2
2
3
3
3
3
AB SOLiD System dibase sequencing
The decoding matrix allows a sequence of transitions to be converted to a base sequence, as long as one of two bases is known.
A C G T
A
C
G
T
2nd Base
1st
Bas
e
0
0
0
0
1
1
1
1
2
2
2
2
3
3
3
3
AA AC AC AA AG AT AA AG AG CC CA CA CC CT CG CC CT CT GG GT GT GG GA GC GG GA GA TT TG TG TT TC TA TT TC TC
A A C A A G C C T C C C A C C T A A G A G G T G G A T T C T T T G T T C G G A G
10 01 2 3 0 2 2
10 01 2 3 0 2 2
4Possible
Sequences
Converting dibase (color) into base calls
Reference
Alignment to reference in “color-space”
• Working in color space:– Reverse-complementation becomes simply reverse– Apply color transition rules to remove measurement errors from
partial assemblies– If reference of Sanger reads are combined, translate to color space
A C G G T C G T C G T G T G C G T
A C G G T C G T C G T G T G C G T
A C G G T C G C C G T G T G C G T
A C G G T C G T C G T G T G C G T
No change
SNP
Measurementerror
SOLiD error checking code (I)
G T C encodes
3 Possible Changes in
the Middle Base
3 Possible Changes in
Dibase Encoding
G A C encodes
G C C encodesG G C encodes
Allowed Transitions
Only Some Transitions Indicate a SNP in sample
SOLiD error checking code (II)
A C G G T C G T C G T G T G C G T
A C G G T C - T C G T G T G C G T
A C G G T C - - C G T G T G C G T
A C G G T C G T C G T G T G C G T
Invalid adjacent
1 base deletion
2 base deletion
SOLiD error checking code (III)
0
5
10
15
20
25
30
35
40
0 5 10 15 20 25 30 35 40
Position on Read
Mea
sure
d Q
V
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
8.00%
9.00%
10.00%
Err
or r
ate
SOLiD di-base sequencing accuracy and QV
Challenge 2. Resequenceability
• 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
• Near-perfect micro-repeats can be also a problem because we want to align reads even with a few sequencing errors and / or SNPs
0%
20%
40%
60%
80%
100%
0 1 2 3 4 5 6 7 8
Mismatches
Rea
ds
Repeats at the fragment level
“base masking”
“fragment masking”
Fragment level repeat annotation
0 1 20.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
Fra
ctio
n o
f ge
no
me
Number of mismatches allowed
• bases in repetitive fragments may be resequenced with reads representing other, unique fragments fragment-level repeat annotations spare a higher fraction of the genome than base-level repeat masking
Find perfect and near-perfect micro-repeats
• Hash based methods (fast but only work out to a couple of mismatches)• Exact methods (very slow but find every repeat copy)• Heuristic methods (fast but miss a fraction of the repeats)
Challenge 3. Read alignment and assembly
• resequencing requires reference sequence-guided read alignment
• to align billions of reads the aligner has to be fast and efficient• INDEL errors require gapped alignment• individually aligned reads must be “assembled” together• has to work for every read type (short, medium-length, and long reads)• must tolerate sequencing errors and SNPs• must work with both base-level and fragment-level repeat annotations
• transcribed sequences require additional features e.g. splice-site aware alignment capability
• most frequently used tools: BLAT (only pair-wise), SSAHA (pair-wise), MAQ (pair-wise and assembly), ELAND (pair-wise), MOSAIK (pair-wise and assembly, gapped)
MOSAIK: method
Step 1. initial short-hash based scan for possible read locations
Step 2. evaluation of candidate read locations with SW method
MOSAIK – performance
• Solexa read alignments to C. elegans genome:100 million reads aligned in 95 minutes18,000 reads / second
• 454 reads to Pichia (yeast-size) genomeGS20: 2,000 reads / secondFLX: 300 reads / second
• Solexa read alignments to masked human genome:40 seconds for 1 million reads 18,000 reads / second5.5 GB RAM used (more for longer initial hash sizes)
MOSAIK: co-assembling different read types
ABI/cap.
454/FLX
Illumina
454/GS20
Challenge 4. Polymorphism discovery
• shallow and deep read coverage
• most candidates will never be “checked” only very low error rates are acceptable
• we updated PolyBayes to deal with new read types • made the new software (PBSHORT) much more efficient
Structural variation discovery
• copy number variations (deletions & amplifications) can be detected from variations in the depth of read coverage
• structural rearrangements (inversions and translocations) require paired-end read data
Challenge 5. Data visualization
1. aid software development: integration of trace data viewing, fast navigation, zooming/panning
2. facilitate data validation (e.g. SNP validation): co-viewing of multiple read types, quality value displays
3. promote hypothesis generation: integration of annotation tracks
Challenge 6. Massive data volumes
Short-read format working [email protected](Asim Siddiqui, UBC)
Assembly format working groupBoston College
http://assembly.bc.edu
• two connected working groups to define standard data formats
Next-generation sequencing software
http://bioinformatics.bc.edu/marthlab/Mosaik
http://sourceforge.net/projects/maq/
Machine manufacturers’ sites plus third-party developers’ sites, e.g.:
Applications in various discovery projects
1. SNP discovery in shallow, single-read 454 coverage(Drosophila melanogaster)
2. Mutational profiling in deep 454 data(Pichia stipitis)
3. SNP and INDEL discovery in deep Illumina / Solexa short-read coverage(Caenorhabditis elegans)
(image from Nature Biotech.)
SNP calling in single-read 454 coverage
• collaborative project with Andy Clark (Cornell) and Elaine Mardis (Wash. U.)• goal was to assess polymorphism rates between 10 different African and American melanogaster isolates• 10 runs of 454 reads (~300,000 reads per isolate) were collected• key informatics question: can we detect SNPs with high accuracy in low-coverage, survey-style 454 reads aligned to finished reference genome sequence?
DNA courtesy of Chuck Langley, UC Davis
• reads were base-called with PyroBayes and aligned to the 180Mb reference melanogaster genome sequence with Mosaik 0.16 x nominal read coverage most reads are singletons• SNP detection with PolyBayes
SNP calling success rates
iso-1 reference
46-2 454 read
46-2 ABI reads (2 fwd + 2 rev)
• 92.9 % validation rate (1,342 / 1,443)single-read coverage: 92.9% (1,275 /
1,372 )double-read coverage: 94.3% (67 / 71)
• 2.0% missed SNP rate (25 / 1247)single-read coverage: 2.12% (25 /
1176)double-read coverage: 0% (0 / 59)
Genome variation in melanogaster isolates
• 658,280 SNPs discovered among all 10 lines.
• Nucleotide diversity Ѳ ≈ 5x10-3 (1 SNP / 200 bp) between each line and reference (in line with expectations).
• 20.2% (133,264 sites) polymorphic among two or more lines. The 1 SNP / 900 bp nominal density is sufficient for high-resolution marker mapping
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
• collaboration with Doug Smith at Agencourt
• 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
Mutational profiling: comparisons
Technology Coverage Nominal coverage FP FN Total error
454/FLX 2 runs 12.9x 1 0 1
454/FLX 1 run 9.8x 6 1 7
Illumina 7 lanes 53.5x 0 0 0
Illumina 3 lanes 23.4x 0 0 0
Illumina 2 lanes 15.6x 2 0 2
Illumina 1 lane 7.6x 2 2 2
SOLiD - 30.0X 0 0 0
SOLiD - 20.0X 0 0 0
SOLiD - 10.0X 0 0 0
SOLiD - 8.0X 0 4 4
SOLiD - 6.0X 0 6 6
Informatics of transcriptome sequencing
0
2000
4000
6000
8000
10000
12000
1 2-25 25-50 50-75 75-100 101-200 201-300 301-400 401-500 >500
Fre
qu
en
cy
Count Of Sage Tag
Counts Per Transcript Based On SAGE Data Of C. elegans Adult Worm (Jones et al. 2001, GEO 24438)
• measuring gene expression levels by sequence tag counting requires SAGE informatics-like approaches
• novel transcript discovery
Inferred Exon 1 Inferred Exon 2
Inferred Exon 1 Inferred Exon 2
new genes & exons
novel transcripts in known genes
Protein-DNA interactions: CHiP-Seq
Protein-bound DNA fragments are isolated with chromatin immunoprecipitation (ChIP) and then sequenced (Seq) on a high-throughput sequencing platform. Sequences are mapped to the genome sequence with a read alignment program. Regions over-represented in the sequences are identified.
Johnson et al. Science, 2007
Protein-DNA interactions: CHIP-SEQ
Mikkelsen et al. Nature 2007.
ChIP-Seq scales well for simultaneous analysis of binding sites in the entire genome.