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) =1/4100 =1/1.60694E+60
Alignment Tools
• BWA http://bio-bwa.sourceforge.net/
• Bowtie http://bowtiebio.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
• http://samtools.github.io/htsspecs/SAMv1.pdf
• Sort and index before performing analysis
• Don’t forget to perform QC on alignment
How to call SNPs
http://www.broadinstitute.org/igv/
Local Realignment
Recalibration
Why do we need realignment and recalibration for DNA but not RNA?
SNP calling
• GATK https://www.broadinstitute.org/gatk/
• Varscan http://varscan.sourceforge.net/
VCF files
Annotation using ANNOVAR
http://www.openbioinformatics.org/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
http://www.broadinstitute.org/cancer/cga/m
utect
• Varscan http://varscan.sourceforge.net/
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
Chr
Pos
depth
a1
b2
c3
d4
6
32975014
21
5
5
10
1
1
81967962
38
20 11
7
0
12
10215654
31
15
7
0
9
1. Forward strand reference allele
2. Forward strand non reference allele
3. Reverse strand reference allele
4. Reverse strand non reference allele
Forward
Strand
Genotype
Reverse
Strand
Genotype
Heterzygous
Homozygous
Heterzygous
Homozygous
Heterzygous
Homozygous
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
The known and unknown of
sequencing data
Known
Unknown
The known and unknown of
sequencing data
Known
Known Unknown
Unkown Unkown
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
Known
Known Unknown
Unkown Unkown
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: chr1 1000 1500
1000
1500
1000
1500
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 16569 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 Unknown
Exome
Samuels, et al. Trends in Genetics, 2013
RNAseq
Quality Control
Quality
Guo et al. Briefings in Bioinformatics, 2013
Quantity