Dr Rick Tearle Senior Applications Specialist, EMEA Complete Genomics Complete Genomics, Inc.

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1 Dr Rick Tearle Senior Applications Specialist, EMEA Complete Genomics

2 Topics Overview of Data Processing Pipeline Overview of Data Files 2

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4 DNA Nano-Ball (DNB) Read Structure Genome : acgtacatgcattcacacatgcttagctatctctcgccag Read r1 : acgta Read r2 : tacatgcatt Read r3 : cacatgctta Gap Gap Gap Read r4 : [-2] [+2] [+5] ctctcgccag Paired-end : acgtacatgcatt cacatgctta ctctcgccag 4

5 Genome well-covered by mapped bases with high Phred scores 100% 90% 80% 70% 60% 50% 40% 30% Phred 20 Phred 17 Phred 13 Phred 10 All 20% 10% 0% Percentage of reference genome covered (y-axis) at minimum depth (x-axis) where only bases with a minimum phred score are counted. Excludes duplicate clones. Phred scores are empirically calibrated by concordance with reference. NA19240 data from Drmanac et al. Science

6 Assembly Process Applies Increasingly Sensitive and Specific (and Slow) Methods 1. Initial Mappings: Least sensitive method Limited use of mate-pairs 2a. Recruit reads via mate-pair mappings Reference 2b. RefScore Re-Alignment: allow single base changes and indels 3. Diploid de novo Assembly: Most sensitive Allows full variations Reference Score: 6

7 Example of a Assembly with a Variant (Heterozygous 5bp Deletion) 7

8 Mapping, Alignment, and Assembly Stages are Different Initial Mappings RefScore Alignments de novo Assemblies Genome-wide Genome-wide Likely variant regions only Pairwise alignment Very limited mismatches allowed. No indels allowed in k-mer seeds. Multiple sequence alignment More sensitive. Single base indels allowed at center position. Multiple sequence alignment Most sensitive: Many possible variations allowed at all positions Not quality score aware Quality score aware Quality score aware Mate-pairs can identify unique pairings Mappings are saved Mate-pair recruitment Temporary: Only used to flag regions for de novo assembly Mate-pair recruitment Diploid contigs are saved Provided with reads and mappings in MAP directories RefScore (only) provided with all assembly results in REF dir Provided with all assembly results in EVIDENCE dir 8

9 Mapping and Assembly Steps In Slightly More Detail Initial Mapping Align reads to reference Calibrate base call quality scores Empirically measure gap sizes Resolve (some) ambiguous mappings using mate-pairs Assemble Recruit reads using mate pairs Find sites of potential variation a. Compute reference score b. De Bruijn graph (indels, etc.) de novo assemble alleles in potentially variant sites according to either Call Variants Compare each allele sequence to reference sequence Define locus boundaries Compute scores Reject highly correlated loci (dups) Determine local phasing Annotate and Deliver Compare to dbsnp Compare to refseq genes Compute summary statistics Generate customer files Ship 9

10 ASM Data Files and Links Between Them Gene-Var Summary Gene Symbol Gene annotations Typically Start Here Sometimes Here Locus ID Coverage RefScore Position Variations (assembly results) Position dbsnp annotations Position Evidence DNBs Evidence Interval ID Evidence Intervals Position Correlations 10

11 Main Assembly Results Files Variations file: variant and non-variant alleles found!asm/var-asm_id.tsv! Gene annotations: variations in known protein coding genes ASM/gene-ASM_ID.tsv! Gene annotations summary: counts of variations in known genes!asm/gene-var-summary-asm_id.tsv! dbsnp annotations: genotypes of known variations from dbsnp!asm/dbsnpannotated-asm_id.tsv! Assembly consensus sequences: ASM/EVIDENCE/evidenceIntervals-CHROM-ASM_ID.tsv! Read alignments in assemblies: ASM/EVIDENCE/evidenceDNBs-CHROM-ASM_ID.tsv! Coverage and reference score: ASM/REF/coverageRefScore-CHROM-ASM_ID.tsv! Also Correlations and Summary files 11

12 Evolution of the CG assembly files: Do Check your Release Notes! Modest changes from CG software version 1.5 to 1.7.4* (present): Naming of variant types changed Minor changes to file formats Addition of weighted coverage depth (1.7.2) in addition to unique coverage depth in coveragerefscore file** Removal of poorly aligned RefSeq genes (to add back in 1.8) More changes since CG software v1.4: Annotation of no-ref regions Correlation filter to reduce false positive calls in seg-dups (et al.) Addition of EVIDENCE files Very substantial changes since CG software 1.3 * Corresponds to Format versions 0.4 to 1.2 (present) ** However, we still caution users on using depth as a quality metric 12

13 Five Take-Home Lessons (particularly for those familiar with other NGS data sets) 1. CG uses initial mappings only to identify regions of potential variation and to identify informative reads for each such region CG initial mappings and coverage depth are thus very different in character and uses than those some other pipelines produce 2. Diploid local de novo assembly of reads in potentially variant regions generates allele sequences Phase may or may not be known Coverage before and after assembly is different (add and subtract reads) Alignments before and after assembly are different 3. Independent comparison of each allele sequence to the reference identifies specific variations Allele variants can be simple or complex; symmetric or asymmetric Variants are not well described by a list of SNP positions 4. Partial information for loci is reported but a conservative approach may elect to disregard it 5. A call of homozygous reference and a no-call are very different! 13