Read Mapping - EMBL

More documents

Recommendations

Info

Mpileup/Annovar SNP Calls, chr19 Sample #Total #dbSNPs #Novel_SNVs #Synonymous_SNVs #Nonsynonymous_SNVs #Novel_synonymous_SNVs #Novel_nonsynonymous_SNVs Sample1 69682 66122 3560 895 880 31 62 Sample2 69092 65590 3502 897 869 28 56 GATK Sample #Total #dbSNPs #Novel_SNPs #Missense_SNPs #Nonsense_SNPs #Novel_missense_SNPs #Novel_nonsense_SNPs Sample1 80188 71113 9075 879 6 86 1 Sample2 80008 71133 8875 884 5 76 1
SNP Call Overlap • Sample1 – 69682 Mpileup/Annovar Calls – 80188 GATK Calls – 67149 calls in common (96%, 83%) • Sample2 – 69092 Mpileup/Annovar Calls – 80008 GATK Calls – 66855 calls in common (97%, 84%)
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 and 44: SAMtools mpileup • Call & Filter
Page 45 and 46: • Drosophila Generic Annotation c
Page 47 and 48: • Human Exonic Annotation chr10.f
Page 49: Variant Calling by Consensus
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
Page 97 and 98: • Technical Validations Validatio
Page 99 and 100: … CATTTT [C/ T] TTTGAA … …CAT
Page 101:
Target Enrichment Tobias Rausch Jul
Page 104 and 105:
Technologies
Page 106 and 107:
Individual Baits vs. Target Regions
Page 108 and 109:
GA Lane, 50MB Kit • 37 million re
Page 110 and 111:
GA Lane, 50MB Kit
Page 112 and 113:
HiSeq Lane, 50MB Kit
Page 114 and 115:
Independent Comparison to RefSeq
Page 116 and 117:
1000 Genomes Project
Page 118 and 119:
Allelic Balance chr1, exon capture
Page 120 and 121:
Allelic Balance for InDels 1bp-5bp
Page 122 and 123:
Copy Number Variants
Page 124 and 125:
Log2 ratio Copy Number Variants •
Page 126 and 127:
Case Study HapMap Trio
Page 128 and 129:
HapMap Trio • NA12878 and NA12891
Page 130 and 131:
Exon Length Distribution • All ba
Page 132 and 133:
NA12878 NA12891 • Mapped SOLiD re
Page 134 and 135:
Avg. Coverage for each Target Illum
Page 136 and 137:
GC-Content Distribution
Page 138 and 139:
GC-Content Distribution
Page 140 and 141:
Histogram of GC-Content of Unmapped
Page 142 and 143:
59% On-target Ratio 84%
Page 144 and 145:
Insert Size is very important!
Page 146 and 147:
Uniform Coverage across Targets? NA
Page 148 and 149:
50 random targets on chrX
Page 150 and 151:
To be or not to be … the same sam
Page 152 and 153:
Creating a SNP Profile EnsemblDB AP
Page 154 and 155:
Read-depth Plot Comparisons • Are
Page 156 and 157:
General Hints • Blood/Tumor sampl
Page 158 and 159:
Illumina HiSeq 2000 DNA sequencer E
Page 160 and 161:
Data Processing and Alignment: Cust
Page 162 and 163:
Computed balanced barcode distribut
Page 164 and 165:
• Balanced base composition • P
Page 166 and 167:
Variant Calling SNPs Short Indels S
Page 168 and 169:
Short InDels • Index-based read m
Page 170 and 171:
Short InDels - Tools • Open-sourc
Page 172 and 173:
Raw Short InDel Calls Sample #Total
Page 174 and 175:
Realignment Reference: ..GACTG--TAC
Page 176 and 177:
Realignment • Improves a crude, i
Page 178 and 179:
ReAligner: Anson and Myers • Obje
Page 180 and 181:
Derive a consensus
Page 182 and 183:
Cut it out of the consensus
Page 184 and 185:
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
Page 196 and 197:
Assembly • Input: Set of paired-e
Page 198 and 199:
Dynamic programming
Page 200 and 201:
Seed and Extend Approach
Page 202 and 203:
Layout Phase • A simple heuristic
Page 204 and 205:
Consensus Phase • Scaffold the co
Page 206 and 207:
Celera Assembler: Some Statistics
Page 208 and 209:
de Bruijn Graph • Reads are too s
Page 210 and 211:
de Bruijn Graph - Local Sequence Si
Page 212 and 213:
Choice of k • “It’s a trade-o
Page 214 and 215:
K-mer Uniqueness Ratio
Page 216 and 217:
• Oases Transcriptome Assembly -
Page 218 and 219:
• MIRA3 Reference-based Assembly
show all

Read Mapping - EMBL

Create successful ePaper yourself

Delete template?

Save as template?