Read Mapping - EMBL

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• Set of perl scripts Annovar Annotation • Based on the UCSC annotation database • Generic across different species • SNP Calling + Annotation is easy to get running • VCF is supported as input Easy to use SNP annotation tool
• Drosophila Generic Annotation chrX.fa 2527302 A T hom 222 48 60 exonic CG10260 nonsynonymous SNV CG10260:NM_130658:exon10:c.6509A>T:p.Q2170L, • Human chr10.fa 93603 C T hom 43.3 17 42 exonic TUBB8 synonymous SNV TUBB8:NM_177987:exon4:c.729G>A:p.P243P, • Format chr pos ref alt hom/het snp qual. depth mq …
Page 1 and 2: Read Mapping Tobias Rausch July 201
Page 3 and 4: Target genome Fragments Paired-End
Page 5 and 6: Paired-End Libraries R1 R2
Page 7 and 8: Mate-Pair Libraries R1 R2 R2 R1
Page 9 and 10: Read Read Mapping Reference Referen
Page 11 and 12: Genome Filtering Preprocess Index
Page 13 and 14: Read Genome Filter Algorithm Exact
Page 15 and 16: Simple k-mer Index, k=3 S = ACGAAAA
Page 17 and 18: Simple k-mer Index, k=3 S = ACGAAAA
Page 19 and 20: Searching a Read Hitlist Hitlist Hi
Page 21 and 22: • Needleman-Wunsch Pairwise Align
Page 23 and 24: • Smith-Waterman Pairwise Alignme
Page 25 and 26: Banded Alignment
Page 27 and 28: Read Mapping Reference Source: illu
Page 29 and 30: Variant Calling SNPs Short Indels S
Page 31 and 32: Set of reads SNP Calling
Page 33 and 34: Set of reads SNP Calling Reverse
Page 35 and 36: SNP Calling Variations: Indels & SN
Page 37 and 38: Variant Calling SNPs Short Indels S
Page 39 and 40: • Differentiating SNP Annotation
Page 41 and 42: GATK • Java library • Easily ex
Page 43: SAMtools mpileup • Call & Filter
Page 47 and 48: • Human Exonic Annotation chr10.f
Page 49 and 50: Variant Calling by Consensus
Page 51 and 52: SNP Call Overlap • Sample1 - 6968
Page 53 and 54: Study Design
Page 55 and 56: Raw SNP Calls, Human Genome Sample
Page 57 and 58: Computational Methods to Detect Gen
Page 59 and 60: Genomic Rearrangements • 1 Kb to
Page 65 and 66: Technologies to Discover Genomic Re
Page 67 and 68: Technologies • Fluorescent in sit
Page 69 and 70: Technologies • Fluorescent in sit
Page 71 and 72: 10 0 Focus on NGS 10 2 Sanger seque
Page 73 and 74: Mate-pair or paired-end mapping abn
Page 95 and 96:
Deletion Short insertion (< Insert
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• Technical Validations Validatio
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… CATTTT [C/ T] TTTGAA … …CAT
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Target Enrichment Tobias Rausch Jul
Page 104 and 105:
Technologies
Page 106 and 107:
Individual Baits vs. Target Regions
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GA Lane, 50MB Kit • 37 million re
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GA Lane, 50MB Kit
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HiSeq Lane, 50MB Kit
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Independent Comparison to RefSeq
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1000 Genomes Project
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Allelic Balance chr1, exon capture
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Allelic Balance for InDels 1bp-5bp
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Copy Number Variants
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Log2 ratio Copy Number Variants •
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Case Study HapMap Trio
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HapMap Trio • NA12878 and NA12891
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Exon Length Distribution • All ba
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NA12878 NA12891 • Mapped SOLiD re
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Avg. Coverage for each Target Illum
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GC-Content Distribution
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GC-Content Distribution
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Histogram of GC-Content of Unmapped
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59% On-target Ratio 84%
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Insert Size is very important!
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Uniform Coverage across Targets? NA
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50 random targets on chrX
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To be or not to be … the same sam
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Creating a SNP Profile EnsemblDB AP
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Read-depth Plot Comparisons • Are
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General Hints • Blood/Tumor sampl
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Illumina HiSeq 2000 DNA sequencer E
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Data Processing and Alignment: Cust
Page 162 and 163:
Computed balanced barcode distribut
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• Balanced base composition • P
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Variant Calling SNPs Short Indels S
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Short InDels • Index-based read m
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Short InDels - Tools • Open-sourc
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Raw Short InDel Calls Sample #Total
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Realignment Reference: ..GACTG--TAC
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Realignment • Improves a crude, i
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ReAligner: Anson and Myers • Obje
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Derive a consensus
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Cut it out of the consensus
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ReAlignment • Using a weighted sc
Page 186 and 187:
ReAlignment • Using a weighted sc
Page 188 and 189:
Consensus Score
Page 190 and 191:
Assembly Algorithms Tobias Rausch J
Page 192 and 193:
Assembly Types • Whole genome ass
Page 194 and 195:
Whole Genome Sequencing Costs
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Assembly • Input: Set of paired-e
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Dynamic programming
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Seed and Extend Approach
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Layout Phase • A simple heuristic
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Consensus Phase • Scaffold the co
Page 206 and 207:
Celera Assembler: Some Statistics
Page 208 and 209:
de Bruijn Graph • Reads are too s
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de Bruijn Graph - Local Sequence Si
Page 212 and 213:
Choice of k • “It’s a trade-o
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K-mer Uniqueness Ratio
Page 216 and 217:
• Oases Transcriptome Assembly -
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• MIRA3 Reference-based Assembly
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Read Mapping - EMBL

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