Vanderbilt Center for Quantitative Sciences Summer Institute Sequencing Analysis (DNA) Yan Guo

Alignment ATCGGGAATGCCGTTAACGGTTGGCGT Reference genome Human genome is about 3 billion base pair (3,000,000,000)in length. If read is 100 bp long, what is the probability of unique alignment? 1/(4x4x4…4) =1/4 100 =1/ E+60

Alignment Tools BWA Bowtie bio.sourceforge.net/index.shtmlhttp://bowtie- bio.sourceforge.net/index.shtml Doing accurate alignment for a 30 million reads will take 30 million x 3billion time units. Both are based on Borrows-Wheeler Algorithm

Alignment Results – Bam files SAM – uncompressed Bam – compressed specs/SAMv1.pdf specs/SAMv1.pdf Sort and index before performing analysis Don’t forget to perform QC on alignment

How to call SNPs

Local Realignment

Recalibration Why do we need realignment and recalibration for DNA but not RNA?

SNP calling GATK Varscan

VCF files

Annotation using ANNOVAR

Somatic Mutation Different from SNP (not germline) Both tumor and normal samples are needed to accurately define a somatic mutation Tumor sample is almost never 100% tumor

Somatic mutation callers MuTect utect utect Varscan

Quality Control on SNPs Number of Novel Non-synonymous SNP ~ 100 – 200 Transition / transversion ratio Heterozygous / non reference homozygous ratio Heterozygous consistency Strand Bias Cycle Bias

Ti/Tv ratio

Heterozygous / non reference homozygous ratio

Ti/Tv ratio by race and regions

Heterozygous / non reference homozygous ratio by race and regions

Heterozygous Genotype Consistency

Strand Bias Table 1. Strand bias examples from real data ChrPosdeptha1a1 b2b2 c3c3 d4d4 Forward Strand Genotype Reverse Strand Genotype HeterzygousHomozygous HeterzygousHomozygous HeterzygousHomozygous 1. Forward strand reference allele 2. Forward strand non reference allele 3. Reverse strand reference allele 4. Reverse strand non reference allele

Cycle Bias

Pooled Analysis Pool samples together without barcode Save money Can only be used to evaluate allele frequency

Pooled Analysis - Conclusion

Advanced Data Mining

The known and unknown of sequencing data

Known – Things we always know that Sequencing data can do SNV, mutation CNV Xie et al. BMC Bioinformatics 2009 Structural Variants Alkan et al. Nature Review Genetics, 2011

Known Unknown – Other information we found that sequencing data contain

How is additional data mining possible? Data mining is possible because capture techniques are not perfect.

Capture Efficiency of The Three Major Capture Kits

Potential Functions of Intron and Intergenic ENCODE suggested that over 80% human genome maybe functional. Majority of the GWAS SNPs are not in coding regions (706 exon, 3986 intron, 3323 intergenic)

Coverage of the Unintended Regions The coverage don’t just drop off suddenly after the capture region end. Capture region example: chr

Reads Aligned to Non Target Regions Can Be Used to Detect SNPs Tibetan exome study : Through exome sequencing of 50 Tibetan subjects, 2 intron SNPs were identified to be associated with high altitude. (Yi, et al. Science 2010) Non capture region study: Non capture region’s reads were studied to show they can infer reliable SNPs. (Guo, et al BMC Genomics)

Known unknown - Mitochondria However, mitochondria is only BP Assumptions: 40 mil reads 100BP long read

Dealing with nuMTs

Alignment Results

Extract mitochondria from exome sequencing Tools: Picardi et al. Nature Methods 2012 Guo et al. Bioinformatics, 2013 (MitoSeek) Diagnosis: Dinwiddie et al. Genmics 2013 Nemeth et al, Brain 2013

Virus Virus sequences can be captured through high throughput sequencing of human samples HBV in liver cancer samples (Sung, et al. Nature Genetics, 2012) (Jiang, et al. Genome Research, 2012) HPV in head and neck cancer (Chen, et al. Bioinformatics, 2012)

HPV AlignmentExample

Tools for Detecting Virus from Sequencing data PathSeq (Kostic, et al. Nature, 2011 Biotechnology) VirusSeq (Chen, et al. Bioinformatics, 2012) ViralFusionSeq (Li, et al. Bioinformatics, 2012) VirusFinder (Wang, et al. PlOS ONE, 2013)

The Data Mining Ideas applied to RNA RNAseq has been used a replacement of microarray. Other application of RNAseq include dection of alternative splicing, and fusion genes. Additional data mining opportunities also available for RNAseq data

SNV and Indel Difficulty due to high false positive rate RNAMapper (Miller, et al. Genome Research, 2013) SNVQ (Duitama, et al. (BMC Genomics, 2013) FX (Hong, et al. Bioinformatics, 2012) OSA (Hu, et al. Binformatics, 2012)

Microsatellite instability Examples: Yoon, et al. Genome Research 2013 Zheng, et al. BMC Genomics, 2013

RNA Editing and Allele-specific expression RNA editing tools and database DARNED, REDidb, dbRES, RADAR Allele-specific expression asSeq (Sun, et al. Biometrics, 2012) AlleleSeq (Rozowsky, et al. Molecular Systems Biology, 2011)

Exogenous RNA Virus (Same as DNA) Food RNA (you are what you eat) Wang, et al. PLOS ONE, 2012

nonCoding RNA

Unknown

Exome Samuels, et al. Trends in Genetics, 2013

RNAseq

Quality Control QualityQuantity Guo et al. Briefings in Bioinformatics, 2013