How Sequencing Experiments Fail
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1 How Sequencing Experiments Fail v1.0 Simon Andrews
2 Classes of Failure Technical Tracking Library Contamination Biological Interpretation Something went wrong with a machine Samples aren t what they re supposed to be Problems during sequencing library preparation Unexpected material in your libraries Samples didn t behave the way you expected Drawing the wrong conclusion from the data
3 Technical
4 Technical Technical Failures C T A G C T A G C T A G Signal Level Signal Level Signal Level Call = T Confidence = High Call = T Confidence = Low Call = T Confidence = Low
5 Technical Phred Scores Phred = -10 log 10 p p = Probability call is incorrect 10% error Phred10 1% error Phred20 0.1% error Phred30
6 Technical Incorrect Encoding Phred64! #$%& Phred Phred33 (Sanger) Phred64 (Illumina)
7 Technical Phred Scores
8 Technical Positional Phred Scores
9 Technical Positional Phred Scores
10 Technical Biased Phred Scores HindIII sites GGATCCGTATGCGATGCTAGCGT GGATCATATATATGCTAGCGTAT GGATCTATATTGCGCGATACTGG GGATCCCGTAGCTGCGATGCTGA GGATCAAGGATAGCGCGTCTAGA GGATCTATATAGTTGCCGTATCG GGATCGGAGCGGGATCGGATGCG
11 Tracking
12 Tracking Tracking - Barcodes
13 Tracking Tracking - Barcodes
14 Tracking Tracking - Barcodes
15 Tracking Tracking Exercise You have some barcode statistics from a set of runs from the same group. Red = Expected barcode Grey = Unexpected barcode Can you see if you can spot a problem within this data set, and say how many of the lanes it might affect?
16 Tracking Tracking Swapped Samples Swapped between users Different sample type Different species See later Contamination / Biology sections Swapped within experiment Look for consistent biological signal Use other knowledge of the samples to validate
17 Tracking Tracking Sample groups
18 Tracking Tracking Sample groups
19 Tracking Tracking Sample swaps KO1 KO2 KO3 KO4 WT1 WT2 WT3 WT4
20 Library
21 Library Library Problems Material Lost Overamplification Duplication Biases in selection Priming bias GC bias Methylation bias Size selection bias Technical contamination Read through adapter Adapter dimers
22 Library Duplication Over-sequencing of library complexity Too little material or too much PCR Can be difficult to assess Why does duplication matter? Potentially biased Over-estimates measurement accuracy
23 Library Duplication
24 Library Duplication
25 Library Duplication
26 Library Duplication
27 Library Duplication - Repeats Real R1 NR R2 Mapped R1 NR R2 Deduplicated R1 NR R2 Repeat Repeat Peak callers (MACS for example) deduplicate internally, so you don t have to consciously do this. Only avoided by using uniquely mapped reads.
28 Deduplication
29 Library Priming Bias
30 Contamination
31 Contamination Contamination Different kinds Technical contamination Adapter dimers Contamination with a species you might expect E.coli in a mouse sample Contamination with something unexpected Contamination with the wrong material DNA in an RNA-Prep Mixed samples
32 Contamination Mapping Efficiency Know what to expect Data type (genomic / transcriptomic) How good / complete is the genome Distinguish unique / multi-mapped reads Understand the mapping process Reads: Input: Mapped: (82.1% of input) of these: (10.0%) have multiple alignments ( have >1) 82.1% overall read alignment rate.
33 Contamination Species Screen
34 Contamination Species Screen
35 Contamination Species Screen
36 Contamination TAGC Plots Assemble Filter contigs Plot %GC vs Coverage Sample and blast
37 Contamination Contamination
38 Contamination Internal Contamination
39 Biological
40 Biological Samples Don t Behave All samples come with a set of expectations Biological effect Sample source Rough biological behaviour If these aren t met Samples may not be what you expect Statistical analyses may be invalid Larger biological picture may be missed
41 Biological Exercise You have been given a set of QC and visualisation results for a knockout in male black6 mice (same genotype as the reference) of a single gene. Have a look through the plots and see if there is anything which would cause you concern regarding the behaviour of the samples.
42 Biological Expected effects missing
43 Biological Confounded effects
44 Biological ChIP doesn t behave
45 Biological ChIP doesn t behave
46 Biological Methylation doesn t behave
47 Biological RNA-Seq doesn t behave
48 Biological Multiple subgroups
49 Interpretation
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