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Assessment of GiaB calls version 0.5.0
Fritz Sedlazeck
January, 25, 2018
Size and numbers of all calls 0.5.0
Size DEL DUP INV INS TRA Complex
0-50bp 22,769 0 0 21,103 0 120
50-100bp 5,357 0 0 4,636 0 102
100-1000bp 6,995 0 0 8,351 0 971
1000-10000bp 1,262 0 0 1,667 0 417
10000+bp 388 0 0 44 0 34
Total 36,771 0 0 35,801 0 1,644
Size and numbers of pass calls 0.5.0
Size DEL DUP INV INS TRA Complex
50-100bp 3,300 0 0 3,238 0 49
100-1000bp 3,747 0 0 6,493 0 361
1000-10000bp 732 0 0 1,343 0 227
10000+bp 108 0 0 38 0 19
Total 7,887 0 0 11,112 0 656
Comparison to other published call sets
Data set Distance (bp) Refound (%) DEL INS Complex
NA12878
(GiaB)
1k 67.25 4602 6986 2
NA12878
(GiaB)
100 54.13 3695 5631 2
1000
genomes
project
1k 18.54 1947 1247 2
1000
genomes
project
100 10.37 1776 11 1
Comparison to other call sets
Comparing the single call vcf files with call set 0.50
• Calls 0.5.0 supported: 16,724 (96.84%)
• 545 unique to call 0.5.0
Technologies-> 2 3 4 5 6
0.5.0 calls 2,446 9,583 2,793 1,806 96
Non 0.5.0 calls
27,175 6,941 302 2 0
Overlap with Genes:
• Overlapped passed SV calls 0.5.0
with annotation
• Counted how often a Gene is
reported.
• Gene PTPRN2 was observed 87
times.
• 56 genes were reported 10 or
more times.
Overview of SVs overlapping with genes
Genes reported in #SVs
Frequency
0 20 40 60 80
010002000300040005000
Overlap with simple repeats
• 5,931 (30.17%) SVs are within (at least 70% of their size) of simple
repeats
• 1,827 DEL
• 4,014 INS
• 90 complex
Sniffles genotyping over HG002
• Using NGMLR alignments + SVs calls 0.5.0
• Sniffles will ignore complex types
• The variant analysis over the split reads and read alignments are
carried out as before, but focusing only on the specified variants
• No quality assessment/filtering is performed
• VCF is reported either with the exact breakpoints or with redefined
breakpoints given the supported reads
Genotyping using Pacbio HG002
• Input: 7,887 del and 11,112
ins + NGMLR alignments for
HG002
• Genotype comparison
• :
Genotyping Pacbio
Allele frequency
Frequency
0 20 40 60 80 100
05001000150020002500
0/0 0/1 1/1
Svviz for
Pacbio
2,328 9,737 7,590
Not recalled DEL 1
Not recalled DEL 2
Genotyping using Pacbio HG002
• 413 were not called at all
• 76 < 2x coverage
• 125 < 3x coverage
• 183 <5x coverage
• 264 < 10x coverage
Hist over non re-called SVs
Read coverage
Frequency 0 10 20 30 40 50 60
020406080100120
CrossStitch
https://github.com/schatzlab/crossstitch
HQ Reference
my.mat.fa
my.pat.fa
In collaboration with Schatz group
Phasing
Suggestions
• SV overlap has to be revisited
• Use Genes to rank SVs for inspection
• Can we be more precise what complex type means?
• Write comparison paper!?

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Fritz sedlazeck v0.5 genes, pacbio and ont reads, and phasing genomes

  • 1. Assessment of GiaB calls version 0.5.0 Fritz Sedlazeck January, 25, 2018
  • 2. Size and numbers of all calls 0.5.0 Size DEL DUP INV INS TRA Complex 0-50bp 22,769 0 0 21,103 0 120 50-100bp 5,357 0 0 4,636 0 102 100-1000bp 6,995 0 0 8,351 0 971 1000-10000bp 1,262 0 0 1,667 0 417 10000+bp 388 0 0 44 0 34 Total 36,771 0 0 35,801 0 1,644
  • 3. Size and numbers of pass calls 0.5.0 Size DEL DUP INV INS TRA Complex 50-100bp 3,300 0 0 3,238 0 49 100-1000bp 3,747 0 0 6,493 0 361 1000-10000bp 732 0 0 1,343 0 227 10000+bp 108 0 0 38 0 19 Total 7,887 0 0 11,112 0 656
  • 4. Comparison to other published call sets Data set Distance (bp) Refound (%) DEL INS Complex NA12878 (GiaB) 1k 67.25 4602 6986 2 NA12878 (GiaB) 100 54.13 3695 5631 2 1000 genomes project 1k 18.54 1947 1247 2 1000 genomes project 100 10.37 1776 11 1
  • 5. Comparison to other call sets Comparing the single call vcf files with call set 0.50 • Calls 0.5.0 supported: 16,724 (96.84%) • 545 unique to call 0.5.0 Technologies-> 2 3 4 5 6 0.5.0 calls 2,446 9,583 2,793 1,806 96 Non 0.5.0 calls 27,175 6,941 302 2 0
  • 6. Overlap with Genes: • Overlapped passed SV calls 0.5.0 with annotation • Counted how often a Gene is reported. • Gene PTPRN2 was observed 87 times. • 56 genes were reported 10 or more times. Overview of SVs overlapping with genes Genes reported in #SVs Frequency 0 20 40 60 80 010002000300040005000
  • 7. Overlap with simple repeats • 5,931 (30.17%) SVs are within (at least 70% of their size) of simple repeats • 1,827 DEL • 4,014 INS • 90 complex
  • 8. Sniffles genotyping over HG002 • Using NGMLR alignments + SVs calls 0.5.0 • Sniffles will ignore complex types • The variant analysis over the split reads and read alignments are carried out as before, but focusing only on the specified variants • No quality assessment/filtering is performed • VCF is reported either with the exact breakpoints or with redefined breakpoints given the supported reads
  • 9. Genotyping using Pacbio HG002 • Input: 7,887 del and 11,112 ins + NGMLR alignments for HG002 • Genotype comparison • : Genotyping Pacbio Allele frequency Frequency 0 20 40 60 80 100 05001000150020002500 0/0 0/1 1/1 Svviz for Pacbio 2,328 9,737 7,590
  • 12. Genotyping using Pacbio HG002 • 413 were not called at all • 76 < 2x coverage • 125 < 3x coverage • 183 <5x coverage • 264 < 10x coverage Hist over non re-called SVs Read coverage Frequency 0 10 20 30 40 50 60 020406080100120
  • 15. Suggestions • SV overlap has to be revisited • Use Genes to rank SVs for inspection • Can we be more precise what complex type means? • Write comparison paper!?

Editor's Notes

  1. Welcome everyone. My name is Fritz Sedlazeck and I am currently working at the Human Genome Sequencing Center @ Baylor in Houston. Today I am going to talk about challenges in SV calling and our pursue of the diploid genome that we are working on. Before I dive into that let me shortly introduce myself and my scientific interest.
  2. 1000 genomes: 65889 SVs NA12878 Giab: 43156 SVs
  3. Maybe do a simple overlap and characterize how many techs are supporting the 0.5.0 calls?
  4. Using SURVIVOR to convert to bed and then bedtools with a repeat annotation
  5. Can I filter out the non HG002? Or at least compute the numer. 2328 ./.9737 0/17590 1/1 Check which SVs are getting ignored.
  6. Show a call that was not called again. (IGV)
  7. Cumulative Hist is the wrong way around!