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Vanderbilt Center for Quantitative Sciences Summer Institute Sequencing Analysis Yan Guo.

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Presentation on theme: "Vanderbilt Center for Quantitative Sciences Summer Institute Sequencing Analysis Yan Guo."— Presentation transcript:

1 Vanderbilt Center for Quantitative Sciences Summer Institute Sequencing Analysis
Yan Guo

2 What is Sequencing? Sequencing is the process of determining the precise order of nucleotides. Non high throughput sequencing: Sanger Sequencing: The basic chain termination method, developed by Frederick Sanger in Generates all possible single-stranded DNA molecules complementary to a given template, and beginning at a common 5' base.

3 The Pros and Cons of Sanger Sequencing
Pros: Highly accurate targetable Cons: Cost $15 per /1000 base pairs, to sequencing the whole genome will cost roughly: 30bil/1000x$15=$15m Low detection rate of alternative allele

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5 Current Generation Sequencing
Illumina ABI Solid 454 Life Science Price Low medium High Read Length 50-100 Read Depth Difficulty Easy

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7 Sequencing Type By Source
RNA: mRNA, Small RNA, Total RNA DNA: Whole Genome or targeted (Exome, mitochondrial, genes of interest, etc)

8 Sequencing Data Raw Image data is more than 2TB per sample
Raw data is about 5-15GB per single end sample or 10-30GB per pair end sample for RNAseq or Exome Sequencing. Whole genome data can easily exceed 200GB per sample. In general 5x raw data size is needed to finish processing Raw data is usually in FASTQ format, the base quality is in Phred scale Older Illumina pipeline uses Phred 64 scale, newer CASAVA 1.8 pipeline uses Sanger scale.

9 Single end vs Paired end
Paired end data has double amount of data than single end. Paired end is more expensive than single end. Paired end data is easier to do quality control (insert size, removing duplicate) Paired end data provides more opportunities to detect structural variance.

10 What can you obtain from DNAseq
SNPs (require only normal or tumor) Somatic Mutations (require tumor and normal pair) Copy Number Variation (work best with whole genome sequencing) Small Structural Variance: Insertion, deletion Large Structure Variance: (Translocation, Inversion)

11 What can you obtain from RNAseq
Gene Expression SNP (only for expressed genes) Novel Splicing Variants Genes Fusion RNAseq has been used primarily as a replacement of microarray

12 How does RNAseq compare to Microarray?
Since 2008, people has been saying that RNAseq will replace microarray for gene expression profiling. VANTAGE stopped offering microarray service earlier this year. Wang, Z., M. Gerstein, and M. Snyder, RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet, (1): p 2. Shendure, J., The beginning of the end for microarrays? Nat Methods, (7): p

13 Data Distribution Guo, Y., et al., Large Scale Comparison of Gene Expression Levels by Microarrays and RNAseq Using TCGA Data. PLoS One, (8): p. e71462.

14 Result Consistency Guo, Y., et al., Large Scale Comparison of Gene Expression Levels by Microarrays and RNAseq Using TCGA Data. PLoS One, (8): p. e71462.

15 RNAseq vs Microarray - advantages
Miroarray Result Type Rich, not limited to expression Limited to expression only Expression Can quantify expression on exon and gene level Can quantify expression on exon or gene level Novel Discovery Can be used for novel discovery Can only detect what is on the chip Analysis Difficult Easy Interpretation Price for assay Price has become comparable to microarray, however the analysis hardware and analysis time may increase the final cost Price is stable

16 Processing RNA

17 Raw data @HWI-ST508:203:D078GACXX:8:1101:1296:1011 1:N:0:ATCACG
NTGGAGTCCTAGGCACAGCTCTAAGCCTCCTTATTCGAGCCGAGCTGGGCC +

18 @EAS139:136:FC706VJ:2:2104:15343:197393 1:Y:18:ATCACG
the unique instrument name 136 the run id FC706VJ the flowcell id 2 flowcell lane 2104 tile number within the flowcell lane 15343 'x'-coordinate of the cluster within the tile 197393 'y'-coordinate of the cluster within the tile 1 the member of a pair, 1 or 2 (paired-end or mate-pair reads only) Y Y if the read fails filter (read is bad), N otherwise 18 0 when none of the control bits are on, otherwise it is an even number ATCACG index sequence

19 Phred Score Phred Quality Score Probability of incorrect base call
Base call accuracy 10 1 in 10 90 % 20 1 in 100 99 % 30 1 in 1000 99.9 % 40 1 in 10000 99.99 % 50 1 in 99.999 %

20 Quality Control Quality control should be conducted at multiple steps during sequencing data processing Raw data Alignment Results (Expression for RNA, and SNP/mutation for DNA) Guo, Y., et al., Three-stage quality control strategies for DNA re-sequencing data. Brief Bioinform, 2013.

21 Raw Data QC - Tools FAST QC FASTX-Toolkit QC3 https://github.com/slzhao/QC3 NGS QC Toolkit

22 Raw Data QC - What to Look For

23 Alignment QC - Tools QC3 https://github.com/slzhao/QC3
Qqplot SAMStat

24 Alignment QC - What to Look For

25 Expression QC - Tools MultiRankSeq https://github.com/slzhao/MultiRankSeq

26 Clustering Algorithms
Start with a collection of n objects each represented by a p–dimensional feature vector xi , i=1, …n. The goal is to divide these n objects into k clusters so that objects within a clusters are more “similar” than objects between clusters. k is usually unknown. Popular methods: hierarchical, k-means, SOM, mixture models, etc.

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33 Distance Calculation in Sequencing Smith-Waterman algorithm
Sequence 1 = ACACACTA Sequence 2 = AGCACACA w(gap) = 0 w(match) = +2 w(a, − ) = w( − ,b) = w(mismatch) = − 1

34 Distance Calculation in Microarray
Pearson Correlation Two profiles (vectors) and +1  Pearson Correlation  – 1

35 Similarity Measurements
Euclidean Distance

36 Linkage Single Linkage: D(X, Y) = min(d(x, y)), x ϵ X, y ϵ Y
Complete Linkage: D(X, Y) = max(d(x, y)), x ϵ X, y ϵ Y Average Linkage:

37 Experssion QC - What to Look For

38 Batch Effect

39 Correction of Batch Effect
Guo, Y., et al., Statistical strategies for microRNAseq batch effect reduction. Translational Cancer Research, (3): p

40 Normalization of RNAseq
Reads Per Kilo base per Million reads (RPKM)

41 RNAseq Data Alignment TopHat2 MapSplice

42 Gene Quantification CufflInks for RPKM http://cufflinks.cbcb.umd.edu/
HTSeq for read count

43 Data Gene Symbol 1 2 3 4 5 6 DDR1 RFC2 HSPA6 PAX8 GUCA1A UBE1L THRA PTPN21 CCL5 CYP2E1 EPHB3 ESRRA CYP2A6 4.5123 SCARB1 TTLL12 9.1008 C2orf59 WFDC2 MAPK1 ADAM32

44 Example of Quantile Normalization
Red = G1; Green = G2; Blue = G3; Yellow = G4; Black = G5 Original Original Sort S1 Sort S2 Sort S3 Sorted S1 S2 S3 G1 2 4 G2 5 14 G3 6 8 G4 3 G5 9 S1 2 3 4 5 S2 3 4 5 6 S3 4 8 9 14 S1 S2 S3 G1 2 3 4 G2 8 G3 G4 5 9 G5 6 14

45 Take Average for Each Row
Sorted S1 S2 S3 2 3 4 8 5 9 6 14 S1 S2 S3 3 S1 S2 S3 3 5 S1 S2 S3 3 5 S1 S2 S3 3 5 6 Averaged S1 S2 S3 3 5 6 8

46 Reorder Red = G1; Green = G2; Blue = G3; Yellow = G4; Black = G5
Averaged S1 S2 S3 3 5 6 8 S1 S2 S3 3 5 S1 S2 S3 3 5 8 S1 S2 S3 3 5 8 6 S1 S2 S3 3 5 8 6 S1 S2 S3 3 5 8 6

47 Differential Expression Analysis
Cuffdiff from Cufflinks package Trapnell, C., et al., Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc, (3): p DESeq EdgeR NBPSeq TSPM baySeq

48 Which Method Is the Best?
Guo, Y., et al., Evaluation of read count based RNAseq analysis methods. BMC Genomics, Suppl 8: p. S2.

49 Consistency

50 Consistency

51 Inconsistency Method Adj pvalue Log2FC Rank DESeq 0.278 3.00 2572
edgeR 0.047 2.92 712 baySeq 0.907 NA 24962 Cuffdiff <0.001 5.83 13 Disease1 Disease2 Disease3 Control1 Control2 Control3 Read count (IGHG2) 391 2038 338 634 10282 1764 Total Read count Adjusted Read Count 78 311 47 178 2292 360

52 Combined Approach log2FoldChange(DESeq2) pValue(DESeq2) pAdj(DESeq2) log2FoldChange(edgeR) pValue(edgeR) pAdj(edgeR) log2FoldChange(raw) 1-Likelihood(baySeq) AdjLikelihood(baySeq) rank(DESeq) rank(edgeR) rank(baySeq) rankMethod1 ENSMUSG _Rps13 7.02E-210 1.07E-205 1.80E-109 4.01E-105 4.21E-07 1.81E-07 1 4 6 ENSMUSG _Rpl23a 3.27E-140 2.49E-136 2.14E-58 2.38E-54 2.53E-05 5.21E-06 2 5 9 ENSMUSG _Gm8841 1.91E-72 7.27E-69 1.60E-50 8.90E-47 4.71E-05 1.70E-05 8 16 ENSMUSG _Atp5g2 4.86E-69 1.48E-65 4.06E-50 1.81E-46 7.54E-05 2.83E-05 10 20 ENSMUSG _Gm12913 8.13E-80 4.13E-76 4.01E-52 2.98E-48 9.17E-05 3 14 ENSMUSG _Gm10075 7.59E-64 1.65E-60 2.30E-42 6.41E-39 2.68E-05 8.81E-06 7 21 ENSMUSG _Rpl5 8.73E-68 2.22E-64 1.29E-42 4.12E-39 29 ENSMUSG _Rpl27 2.86E-62 5.45E-59 2.48E-43 9.20E-40 18 32 ENSMUSG _Rpl31 4.03E-41 4.72E-38 7.47E-33 1.67E-29 13 19 42 ENSMUSG _Gm15965 1.41E-33 1.34E-30 3.47E-24 4.29E-21 7.18E-05 2.31E-05 43 ENSMUSG _Mup9 8.13E-32 6.19E-29 5.65E-22 5.47E-19 1.71E-13 23 44 ENSMUSG _Mir5109 9.06E-36 9.20E-33 3.04E-32 6.16E-29 15 11 22 48 ENSMUSG _Rps23 3.15E-44 4.37E-41 9.73E-29 1.67E-25 25 49 Guo, Y., et al., MultiRankSeq: Multiperspective Approach for RNAseq Differential Expression Analysis and Quality Control. BioMed Research International, : p. 8.

53 Presentation Using Heatmap and Cluster
Zhao, S., et al., Advanced Heat Map and Clustering Analysis Using Heatmap3. BioMed Research International, : p. 6.

54 Difference Between Heatmaps

55 Questions We Can Answer with Cluster
Microarray data quality checking Does replicates cluster together? Does similar conditions, time points, tissue types cluster together?

56 Presentation Using Volcano Plot

57 Presentation Using Circos Plot

58 Test Your Hypothesis Without Performing Any Analysis
GEO

59 Test Your Hypothesis Without Performing Any Analysis

60 Functional Analysis Samples Space n F M
Suppose in a study, we are trying to find out if the proportion of smoking individual is significantly different between men and women. Smoking d c b a

61 Fisher’s Exact Test Male Female Total Smoking a b a + b Nonsmoking c d
b + d a+b+c+d=n H0 : The proportion of smoking in male == the proportion of smoking in female H1 : The proportion of smoking in male != the proportion of smoking in female

62 Fisher’s Exact Test – in Functional Analysis
All Genes Winner Genes Non Winner Genes Breast Cancer Genes a b Non Brest Cancer Genes c d d Winner Genes Breast Cancer Genes a c b

63 Analogy There are 18000 Balls: 200 + 17800 in a box.
Blindfolded, you randomly draw 100 balls. What is the probability that you draw less than 50

64 WebGestalt

65 Gene Set Enrichment Analysis
KS test based analysis (Ref) GSEA does not need a winner list first

66 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)

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

68 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)

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

70 nonCoding RNA


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