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Finding the Lost Treasure of NGS Data

Finding the Lost Treasure of NGS Data. Yan Guo, PhD. Modules Overview for DNA-sequence Exome / whole-Genome . f astq files. gene coding changes. FastQC. r ealignment. bamQC. r ecalibration. somatic mutation. bwa alignment. d bsnp / indel resources. best practice filter.

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Finding the Lost Treasure of NGS Data

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  1. Finding the Lost Treasure of NGS Data Yan Guo, PhD

  2. Modules Overview for DNA-sequence Exome / whole-Genome fastq files gene coding changes FastQC realignment bamQC recalibration somaticmutation bwaalignment dbsnp / indel resources best practice filter mark-duplication Bam files GATK refinement gene associates SNP/INDEL vcf files gene-level analysis Translocation, inversion, copy number variants structural variant analysis

  3. RNAseq fastq files genes identifying FastQC cluster cuffdiff comparisons tophatalignment functional/ pathway SeQC Gene List cufflinks annotations Bam files Refinement novel genes discovery gene quantification gene-fusion analysis cufflinks annotations cuffmerge cuffdiff comparisons

  4. What do you expect to find in NGS data? DNAseq RNAseq Gene expression difference Splicing Variants Fusion Genes • SNPs • Somatic Mutations • Small Indels • Large Structural Change • CNV

  5. What you don’t expect to find in NGS data? Exome sequencing reads Virus/Microbe DNA Is mapped? No Unmapped DNA reads Contamination Yes Mapped reads Intronic DNA Is targeted? No Untargeted DNA Intergenic DNA Yes Mitochondrial DNA Targeted DNA

  6. Exome Capture

  7. Why do we care about intron and intergenic regions • some introns can encode specific proteins and can be processed after splicing to form noncoding RNA molecules. (Rearick, Prakash et al. 2011) • Majority of the GWAS SNPs are not in coding regions (706 exon, 3986 intron, 3323 intergenic) • The ENCODE Project: ENCyclopedia Of DNA Elements

  8. GWAS catalog SNPs

  9. Mitochondria • Mitochondria play an important role in cellular energy metabolism, free radical generation, and apoptosis (Andrews, Kubacka et al. 1999; Verma and Kumar 2007). • Mitochondrial DNA (mtDNA) is a maternally-inherited 16,569-bp closed-circle genome that encodes two rRNAs, 22 tRNAs, and 10 polypeptides. • Dysfunctions in mitochondrial function are an important cause of many neurological diseases (Fernandez-Vizarra, Bugiani et al. 2007) and drug toxicities (Lemasters, Qian et al. 1999; Wallace and Starkov 2000) and may contribute to carcinogenesis and tumor progression (Modica-Napolitano and Singh 2004; Chen 2012).

  10. Mitochondria Extraction Strategy

  11. Results

  12. Example

  13. Virus • Known oncogenic viruses are estimated to cause 15 to 20 percent of all cancers in humans (Parkin 2006). • Understanding the viral integration pattern of cancer-associated viruses may uncover novel oncogenes and tumor suppressors that are associated with cellular transformation. • Viral genomes have been detected using off-target exome sequencing reads (Barzon, Lavezzo et al. 2011; Li and Delwart 2011; Chevaliez, Rodriguez et al. 2012; Radford, Chapman et al. 2012; Capobianchi, Giombini et al. 2013).

  14. One example using HNSCC

  15. Virus Detection in HNSCC in TCGA

  16. Existing Tools • PathSeq (Kostic, Ojesina et al. 2011) • VirusSeq (Chen, Yao et al. 2012) • ViralFusionSeq (Li, Wan et al. 2013)

  17. SNP and Somatic Mutation Identification using RNAseq Data • Traditionally, somatic mutations are detected using Sanger sequencing or RT-PCR by comparing paired tumor and normal samples. One obvious limitation of such methods is that we have to limit our search to a certain genomic region of interest. • With the maturity of next generation sequencing, we can now screen all coding genes or even the whole genome for somatic mutations at a reasonable cost.

  18. Why do we want to detect mutation in RNAseq data? • You don’t have DNA sequencing data • Detecting mutation was not the original goal, but why not • There are much more RNAseq data than DNAseq data • A mutation in RNA is more relevant than a mutation in DNA

  19. Difficulties • Not enough depth in the non-expressed genes to detect mutation • Reverse transcribe RNA to cDNA introduce more error • Hard to distinguish mutation from RNA editing • In summary, somatic mutation detection using RNAseq data contains much more false positives.

  20. Somatic Mutation Caller Designed Specifically for RNAseq Data

  21. Other Ways you can mine your data

  22. Summary • Get your priority right, never design a study just for secondary analysis targets • If you have old data, think about else you can do with it, try to maximize the full potential of your data • At VANGARD, we help you with your basic genomic data analysis needs • Advanced data analysis can be done through collaboration.

  23. Acknowledgement • Yu Shyr • Tiger Sheng • Chung-I Li • Jiang Li • Mike Guo • David Samuels • Chun Li

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