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Comparative Assembly for Cancer Human Genome

Comparative Assembly for Cancer Human Genome. Gao Song 2010/02/03. Content. Background Knowledge Problem Description Framework of Solution Own Methods Results. Background Knowledge. Pair End Tag (PET). Background Knowledge. Concordant PET (CPET) Discordant PET (DPET)

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Comparative Assembly for Cancer Human Genome

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  1. Comparative AssemblyforCancer Human Genome Gao Song 2010/02/03

  2. Content • Background Knowledge • Problem Description • Framework of Solution • Own Methods • Results

  3. Background Knowledge • Pair End Tag (PET)

  4. Background Knowledge • Concordant PET (CPET) • Discordant PET (DPET) • Distance or orientation is incorrect • Map to different chromosomes • DPET Cluster

  5. Problem Description • Given: • Frequency of DPET and CPET along the reference genome • DPET Cluster • Requirement: • Find rearrangement of cancer genome compare to normal human genome • Now focus on Amplicons

  6. Framework of Solution • The reference genome is cut when CPET is 0 => some big contigs • According to DPET, find the breakpoints • Using CPET to check if there is connection between breakpoints • Convert DPET Cluster into edges in the graph • Using high copy edges to form subgraph of amplicons

  7. Framework of Solution Start and End Breakpoint DPET CPET Reference Genome DPET FiltedBreakPoints Original Contigs Small Contigs Nodes Edges CPET Graph

  8. Own Methods-Naive Chromosome 9 • DPET Frequency Curve • Using DPET directly choose a threshold to Select the breakpoint • Problem: • How to choose the threshold • Within amplicon region, it is hard to find the breakpoint – basic frequency is too much

  9. Own Methods - Slope Chromosome 9 • Using slope (differentiation) • Problem: • How to define threshold • Too many false positive • Also miss some DPET cluster

  10. Own Method – Consider Ratio • In breakpoint, DPET increases, CPET decreases • Can be used as another criteria • Problem • Another Parameter!

  11. New methods of finding breakpoint • Using slope to find the threshold • The previous missing point can be found

  12. Own Method – Hypothesis Testing • Localize checking • Using two consecutive windows • Each window has: μ σ • Null Hypothesis: σ2 is not significantly larger than σ1 • Using Binomial Testing: Significance level: 0.05 window1 window2

  13. Own Method – Hypothesis Testing • Some details: • Check if the cluster region is included in window • Not finished yet • Calculating σ is time-consuming - have to recalculate after each step

  14. Results(slope)

  15. One Special Case

  16. 10k Lib

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  18. Another example • 10k lib 20k lib

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