second-generation sequencing introduction to second-generation
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Introduction tosecond-generation
sequencingCMSC858B Spring 2012
Many slides courtesy of Ben Langmead
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Corrada Bravo 10/30/09
Second-GenerationSequencing
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Corrada Bravo 10/30/09
Human Epigenome Project
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Methylation
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ENCODE project
http://www.genome.gov/10005107
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1000 Genomes Project
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!"#$%&'%(")*%+)#,-.)/
+01.'#..#*,
2)3)%456.),,0'3%7..#*,45'3%7..#*,
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(.#3,1.068'3
G T A A T C C T C | | | | | | | | | C A T T A G G A G
&97
G U A A U C C
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7
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<:97
1&97
C A T T A G G A G C A T T A G G A G C A T T A G G A G C A T T A G G A G
T T A G G A G
C A T T A G G A G C A T T A G G A G C A T T A G G A G C A T T A G G A G
C A T T A G G A G
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Corrada Bravo 10/30/09
Sec-gen Sequencing
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Corrada Bravo 10/30/09
Sec-gen Sequencing
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Fragmentation is random, i.e., not equal-sized (but hard to draw)
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Corrada Bravo 10/30/09
Sec-gen Sequencing
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Corrada Bravo 10/30/09
Second-Generation Sequencing
• “Ultra high throughput” DNA sequencing
• 3 gigabases / day vs.
• 3 gigabases / 13 years (human genome project, more or less)
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Platforms
• Millions of short DNA fragments (~100 bp) sequenced in parallel
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Source: Metzker ML. Sequencing technologies - the next generation. Nat Rev Genet. 2010
Source: Whiteford et al. Swift: primary data analysis for the Illumina Solexa sequencingplatform. Bioinformatics. 2009
Source: Metzker ML. Sequencing technologies - the next generation. Nat Rev Genet. 2010
namesequencequality scores
x 100s of millions
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Sequencing throughput
HiSeq 200025 billion bp per day
(2010)
GA IIx5 billion bp per day
(2009)
GA II1.6 billion bp per day
(2008)
Images: www.illumina.com/systems
Numbers: www.politigenomics.com/next-generation-sequencing-informatics
Dates: Illumina press releases
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Sequencing throughput
HiSeq 250060 billion bp per day
(2012)
GA IIx5 billion bp per day
(2009)
GA II1.6 billion bp per day
(2008)
Images: www.illumina.com/systems
Numbers: www.politigenomics.com/next-generation-sequencing-informatics
Dates: Illumina press releases
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Sequencing throughput
Mid 2010End of 2009
Up to 50 Gb
SOLiD 3+ System
Source: www3.appliedbiosystems.com/cms/groups/mcb_marketing/documents/generaldocuments/cms_061241.pdf
Late 2011/Early 2012
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Computational throughput
The number of transistors that can be placed inexpensively on an integrated circuit doubles approximately every two years.
Moore’s Law:
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Computational throughput
386
Pentium
Core 2 Duo
Source: en.wikipedia.org/wiki/Moore%27s_law
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Throughput growth gap
4-5x per year 2x per 2 years
>
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ionTorrent
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Oxford Nanopore
• Nanopore technology
• ultralong reads (48kb genome sequenced as one read)
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Source: Metzker ML. Sequencing technologies - the next generation. Nat Rev Genet. 2010
Source: Whiteford et al. Swift: primary data analysis for the Illumina Solexa sequencingplatform. Bioinformatics. 2009
Source: Metzker ML. Sequencing technologies - the next generation. Nat Rev Genet. 2010
namesequencequality scores
x 100s of millions
(slide courtesy of Ben Langmead)23
!
Corrada Bravo 10/30/09
Sec-gen Sequencing
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!
Corrada Bravo 10/30/09
Sec-gen Sequencing
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Corrada Bravo 10/30/09
Sec-gen Sequencing
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Corrada Bravo 10/30/09
Sec-gen Sequencing
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Corrada Bravo 10/30/09
Sec-gen Sequencing
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Corrada Bravo 10/30/09
Sec-gen Sequencing
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Corrada Bravo 10/30/09
Sec-gen Sequencing
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Image Analysis
Figure 2.2: Image analysis stages in Firecrest
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Figure 2.3: An example input image and magnified selection.
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An input image and zoomed in section
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Image Analysis
• 4 images per cycle
• ~100 tiles
• Analysis:
• Filtering
• Background subtraction
• Thresholding
• Each image analysis independent (so can parallelize)
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Image Analysis
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Figure 2.10: Image after object deblending, gray pixels indicate the maximum pixel in this object. Splitobjects no longer have boundaries, and so only the maximum pixel is shown.
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Image after processing. This is old, cluster density is much higher now
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!
Corrada Bravo 10/30/09
Sec-gen Sequencing
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First Cycle
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Corrada Bravo 10/30/09
Sec-gen Sequencing
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Second Cycle
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Corrada Bravo 10/30/09
A Thought Experiment
Color coded by call made: A, C, G, T
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Corrada Bravo 10/30/09
Fluorescence Intensity
Color coded by call made: A, C, G, T
!"#$ %&'(()*+,*#"$)-%)(%)+.(/)(-./01+%0%*)+2
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Corrada Bravo 10/30/09
Fluorescence Intensity
Color coded by call made: A, C, G, T
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MORE ON THIS LATER IN THE
COURSE!
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Ion Torrent
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Ion Torrent
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Ion Torrent
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Ion Torrent
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Ion Torrent
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Source: Metzker ML. Sequencing technologies - the next generation. Nat Rev Genet. 2010
Source: Whiteford et al. Swift: primary data analysis for the Illumina Solexa sequencingplatform. Bioinformatics. 2009
Source: Metzker ML. Sequencing technologies - the next generation. Nat Rev Genet. 2010
namesequencequality scores
x 100s of millions
(slide courtesy of Ben Langmead)44
From reads to evidence
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From reads to evidence1. Comparative
Sequence-wise, individuals of a species are nearly identical
Well curated, annotated “reference” genomes exist
D. melanogaster, Science, 2000 H. sapiens, Nature, 2000 M. musculus, Nature, 2002and Science, 2000
Idea: “Map” reads to their point of origin with respect to a reference, then study differences
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From reads to evidence2. de novo
Assume nothing! - let reads tell us everything
Reads with overlapping sequence probably originate from overlapping portionsof the subject genome
Encode overlap relationships as a graph
The full genome sequence is a “tour” of the graph
Source: De Novo Assembly Using Illumina Reads. Illumina. 2010
Source: De Novo Assembly Using Illumina Reads. Illumina. 2010http://www.illumina.com/Documents/products/technotes/technote_denovo_assembly.pdf
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Mapping
CTCAAACTCCTGACCTTTGGTGATCCACCCGCCTNGGCCTTC
Take a read:
And a reference sequence:>MT dna:chromosome chromosome:GRCh37:MT:1:16569:1GATCACAGGTCTATCACCCTATTAACCACTCACGGGAGCTCTCCATGCATTTGGTATTTTCGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTCGCAGTATCTGTCTTTGATTCCTGCCTCATCCTATTATTTATCGCACCTACGTTCAATATTACAGGCGAACATACTTACTAAAGTGTGTTAATTAATTAATGCTTGTAGGACATAATAATAACAATTGAATGTCTGCACAGCCACTTTCCACACAGACATCATAACAAAAAATTTCCACCAAACCCCCCCTCCCCCGCTTCTGGCCACAGCACTTAAACACATCTCTGCCAAACCCCAAAAACAAAGAACCCTAACACCAGCCTAACCAGATTTCAAATTTTATCTTTTGGCGGTATGCACTTTTAACAGTCACCCCCCAACTAACACATTATTTTCCCCTCCCACTCCCATACTACTAATCTCATCAATACAACCCCCGCCCATCCTACCCAGCACACACACACCGCTGCTAACCCCATACCCCGAACCAACCAAACCCCAAAGACACCCCCCACAGTTTATGTAGCTTACCTCCTCAAAGCAATACACTGACCCGCTCAAACTCCTGGATTTTGGATCCACCCAGCGCCTTGGCCTAAACTAGCCTTTCTATTAGCTCTTAGTAAGATTACACATGCAAGCATCCCCGTTCCAGTGAGTTCACCCTCTAAATCACCACGATCAAAAGGAACAAGCATCAAGCACGCAGCAATGCAGCTCAAAACGCTTAGCCTAGCCACACCCCCACGGGAAACAGCAGTGATTAACCTTTAGCAATAAACGAAAGTTTAACTAAGCTATACTAACCCCAGGGTTGGTCAATTTCGTGCCAGCCACCGCGGTCACACGATTAACCCAAGTCAATAGAAGCCGGCGTAAAGAGTGTTTTAGATCACCCCCTCCCCAATAAAGCTAAAACTCACCTGAGTTGTAAAAAACTCCAGTTGACACAAAATAGACTACGAAAGTGGCTTTAACATATCTGAACACACAATAGCTAAGACCCAAACTGGGATTAGATACCCCACTATGCTTAGCCCTAAACCTCAACAGTTAAATCAACAAAACTGCTCGCCAGAACACTACGAGCCACAGCTTAAAACTCAAAGGACCTGGCGGTGCTTCATATCCCTCTAGAGGAGCCTGTTCTGTAATCGATAAACCCCGATCAACCTCACCACCTCTTGCTCAGCCTATATACCGCCATCTTCAGCAAACCCTGATGAAGGCTACAAAGTAAGCGCAAGTACCCACGTAAAGACGTTAGGTCAAGGTGTAGCCCATGAGGTGGCAAGAAATGGGCTACATTTTCTACCCCAGAAAACTACGATAGCCCTTATGAAACTTAAGGGTCGAAGGTGGATTTAGCAGTAAACTAAGAGTAGAGTGCTTAGTTGAACAGGGCCCTGAAGCGCGTACACACCGCCCGTCACCCTCCTCAAGTATACTTCAAAGGACATTTAACTAAAACCCCTACGCATTTATATAGAGGAGACAAGTCGTAACCTCAAACTCCTGCCTTTGGTGATCCACCCGCCTTGGCCTACCTGCATAATGAAGAAGCACCCAACTTACACTTAGGAGATTTCAACTTAACTTGACCGCTCTGAGCTAAACCTAGCCCCAAACCCACTCCACCTTACTACCAGACAACCTTAGCCAAACCATTTACCCAAATAAAGTATAGGCGATAGAAATTGAAACCTGGCGCAATAGATATAGTACCGCAAGGGAAAGATGAAAAATTATAACCAAGCATAATATAGCAAGGACTAACCCCTATACCTTCTGCATAATGAATTAACTAGAAATAACTTTGCAAGGAGAGCCAAAGCTAAGACCCCCGAAACCAGACGAGCTACCTAAGAACAGCTAAAAGAGCACACCCGTCTATGTAGCAAAATAGTGGGAAGATTTATAGGTAGAGGCGACAAACCTACCGAGCCTGGTGATAGCTGGTTGTCCAAGATAGAATCTTAGTTCAACTTTAAATTTGCCCACAGAACCCTCTAAATCCCCTTGTAAATTTAACTGTTAGTCCAAAGAGGAACAGCTCTTTGGACACTAGGAAAAAACCTTGTAGAGAGAGTAAAAAATTTAACACCCATAGTAGGCCTAAAAGCAGCCACCAATTAAGAAAGCGTTCAAGCTCAACACCCACTACCTAAAAAATCCCAAACATATAACTGAACTCCTCACACCCAATTGGACCAATCTATCACCCTATAGAAGAACTAATGTTAGTATAAGTAACATGAAAACATTCTCCTCCGCATAAGCCTGCGTCAGATTAAAACACTGAACTGACAATTAACAGCCCAATATCTACAATCAACCAACAAGTCATTATTACCCTCACTGTCAACCCAACACAGGCATGCTCATAAGGAAAGGTTAAAAAAAGTAAAAGGAACTCGGCAAATCTTACCCCGCCTGTTTACCAAAAACATCACCTCTAGCATCACCAGTATTAGAGGCACCGCCTGCCCAGTGACACATGTTTAACGGCCGCGGTACCCTAACCGTGCAAAGGTAGCATAATCACTTGTTCCTTAAATAGGGACCTGTATGAATGGCTCCACGAGGGTTCAGCTGTCTCTTACTTTTAACCAGTGAAATTGACCTGCCCGTGAAGAGGCGGGCATAACACAGCAAGACGAGAAGACCCTATGGAGCTTTAATTTATTAATGCAAACAGTACCTAACAAACCCACAGGTCCTAAACTACCAAACCTGCATTAAAAATTTCGGTTGGGGCGACCTCGGAGCAGAACCCAACCTCCGAGCAGTACATGCTAAGACTTCACCAGTCAAAGCGAACTACTATACTCAATTGATCCAATAACTTGACCAACGGAACAAGTTACCCTAGGGATAACAGCGCAATCCTATTCTAGAGTCCATATCAACAATAGGGTTTACGACCTCGATGTTGGATCAGGACATCCCGATGGTGCAGCCGCTATTAAAGGTTCGTTTGTTCAACGATTAAAGTCCTACGTGATCTGAGTTCAGACCGGAGTAATCCAGGTCGGTTTCTATCTACNTTCAAATTCCTCCCTGTACGAAAGGACAAGAGAAATAAGGCCTACTTCACAAAGCGCCTTCCCCCGTAAATGATATCATCTCAACTTAGTATTATACCCACACCCACCCAAGAACAGGGTTTGTTAAGATGGC
How do we determine the read’s point of origin with respect to the reference?
CTCAAAGACCTGACCTTTGGTGATCCACCC-----GCCTNGGCCTTC|||||| |||| |||| ||||||||| |||| |||||CTCAAACTCCTGGATTTTG--GATCCACCCAGCTGGCCTTGGCCTAA
Hypothesis 1:
Hypothesis 2:
CTCAAACTCCTGACCTTTGGTGATCCACCCGCCTNGGCCTTC|||||||||||| ||||||||||||||||||||| ||||| |CTCAAACTCCTG-CCTTTGGTGATCCACCCGCCTTGGCCTAC
Answer: sequence similarity
Read
Reference
Read
Reference
Say hypothesis 2 is correct. Why are there still mismatches and gaps?
Which hypothesis is better?
48
Mapping
CTCAAACTCCTGACCTTTGGTGATCCACCCGCCTNGGCCTTC
>MT dna:chromosome chromosome:GRCh37:MT:1:16569:1GATCACAGGTCTATCACCCTATTAACCACTCACGGGAGCTCTCCATGCATTTGGTATTTTCGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTCGCAGTATCTGTCTTTGATTCCTGCCTCATCCTATTATTTATCGCACCTACGTTCAATATTACAGGCGAACATACTTACTAAAGTGTGTTAATTAATTAATGCTTGTAGGACATAATAATAACAATTGAATGTCTGCACAGCCACTTTCCACACAGACATCATAACAAAAAATTTCCACCAAACCCCCCCTCCCCCGCTTCTGGCCACAGCACTTAAACACATCTCTGCCAAACCCCAAAAACAAAGAACCCTAACACCAGCCTAACCAGATTTCAAATTTTATCTTTTGGCGGTATGCACTTTTAACAGTCACCCCCCAACTAACACATTATTTTCCCCTCCCACTCCCATACTACTAATCTCATCAATACAACCCCCGCCCATCCTACCCAGCACACACACACCGCTGCTAACCCCATACCCCGAACCAACCAAACCCCAAAGACACCCCCCACAGTTTATGTAGCTTACCTCCTCAAAGCAATACACTGACCCGCTCAAACTCCTGGATTTTGTGATCCACCCAGCGCCTTGGCCTAACTAGCCTTTCTATTAGCTCTTAGTAAGATTACACATGCAAGCATCCCCGTTCCAGTGAGTTCACCCTCTAAATCACCACGATCAAAAGGAACAAGCATCAAGCACGCAGCAATGCAGCTCAAAACGCTTAGCCTAGCCACACCCCCACGGGAAACAGCAGTGATTAACCTTTAGCAATAAACGAAAGTTTAACTAAGCTATACTAACCCCAGGGTTGGTCAATTTCGTGCCAGCCACCGCGGTCACACGATTAACCCAAGTCAATAGAAGCCGGCGTAAAGAGTGTTTTAGATCACCCCCTCCCCAATAAAGCTAAAACTCACCTGAGTTGTAAAAAACTCCAGTTGACACAAAATAGACTACGAAAGTGGCTTTAACATATCTGAACACACAATAGCTAAGACCCAAACTGGGATTAGATACCCCACTATGCTTAGCCCTAAACCTCAACAGTTAAATCAACAAAACTGCTCGCCAGAACACTACGAGCCACAGCTTAAAACTCAAAGGACCTGGCGGTGCTTCATATCCCTCTAGAGGAGCCTGTTCTGTAATCGATAAACCCCGATCAACCTCACCACCTCTTGCTCAGCCTATATACCGCCATCTTCAGCAAACCCTGATGAAGGCTACAAAGTAAGCGCAAGTACCCACGTAAAGACGTTAGGTCAAGGTGTAGCCCATGAGGTGGCAAGAAATGGGCTACATTTTCTACCCCAGAAAACTACGATAGCCCTTATGAAACTTAAGGGTCGAAGGTGGATTTAGCAGTAAACTAAGAGTAGAGTGCTTAGTTGAACAGGGCCCTGAAGCGCGTACACACCGCCCGTCACCCTCCTCAAGTATACTTCAAAGGACATTTAACTAAAACCCCTACGCATTTATATAGAGGAGACAAGTCGTAACCTCAAACTCCTGGCCTTTGGTGATCCACCCGCCTTGGCCTACCTGCATAATGAA AAGCACCCAACTTACACTTAGGAGATTTCAACTTAACTTGACCGCTCTGAGCTAAACCTAGCCCCAAACCCACTCCACCTTACTACCAGACAACCTTAGCCAAACCATTTACCCAAATAAAGTATAGGCGATAGAAATTGAAACCTGGCGCAATAGATATAGTACCGCAAGGGAAAGATGAAAAATTATAACCAAGCATAATATAGCAAGGACTAACCCCTATACCTTCTGCATAATGAATTAACTAGAAATAACTTTGCAAGGAGAGCCAAAGCTAAGACCCCCGAAACCAGACGAGCTACCTAAGAACAGCTAAAAGAGCACACCCGTCTATGTAGCAAAATAGTGGGAAGATTTATAGGTAGAGGCGACAAACCTACCGAGCCTGGTGATAGCTGGTTGTCCAAGATAGAATCTTAGTTCAACTTTAAATTTGCCCACAGAACCCTCTAAATCCCCTTGTAAATTTAACTGTTAGTCCAAAGAGGAACAGCTCTTTGGACACTAGGAAAAAACCTTGTAGAGAGAGTAAAAAATTTAACACCCATAGTAGGCCTAAAAGCAGCCACCAATTAAGAAAGCGTTCAAGCTCAACACCCACTACCTAAAAAATCCCAAACATATAACTGAACTCCTCACACCCAATTGGACCAATCTATCACCCTATAGAAGAACTAATGTTAGTATAAGTAACATGAAAACATTCTCCTCCGCATAAGCCTGCGTCAGATTAAAACACTGAACTGACAATTAACAGCCCAATATCTACAATCAACCAACAAGTCATTATTACCCTCACTGTCAACCCAACACAGGCATGCTCATAAGGAAAGGTTAAAAAAAGTAAAAGGAACTCGGCAAATCTTACCCCGCCTGTTTACCAAAAACATCACCTCTAGCATCACCAGTATTAGAGGCACCGCCTGCCCAGTGACACATGTTTAACGGCCGCGGTACCCTAACCGTGCAAAGGTAGCATAATCACTTGTTCCTTAAATAGGGACCTGTATGAATGGCTCCACGAGGGTTCAGCTGTCTCTTACTTTTAACCAGTGAAATTGACCTGCCCGTGAAGAGGCGGGCATAACACAGCAAGACGAGAAGACCCTATGGAGCTTTAATTTATTAATGCAAACAGTACCTAACAAACCCACAGGTCCTAAACTACCAAACCTGCATTAAAAATTTCGGTTGGGGCGACCTCGGAGCAGAACCCAACCTCCGAGCAGTACATGCTAAGACTTCACCAGTCAAAGCGAACTACTATACTCAATTGATCCAATAACTTGACCAACGGAACAAGTTACCCTAGGGATAACAGCGCAATCCTATTCTAGAGTCCATATCAACAATAGGGTTTACGACCTCGATGTTGGATCAGGACATCCCGATGGTGCAGCCGCTATTAAAGGTTCGTTTGTTCAACGATTAAAGTCCTACGTGATCTGAGTTCAGACCGGAGTAATCCAGGTCGGTTTCTATCTACNTTCAAATTCCTCCCTGTACGAAAGGACAAGAGAAATAAGGCCTACTTCACAAAGCGCCTTCCCCCGTAAATGATATCATCTCAACTTAGTATTATACCCACACCCACCCAAGAACAGGGTTTGTTAAGATGGC
This is an alignment:
Software programs that compare reads to references and find alignments are aligners.
Read
Reference
Read
Reference
Alignment is computationally difficult because references (e.g. human) are very long (more than 1M times longer than what’s shown to the left) and sequencers produce data very rapidly, e.g. up to 25 billion bases per day in 2010.
Sequencing throughput increases by ~5x per year, whereas computers get faster at a rate closer to ~2x every 2 years.
CTCAAAGACCTGACCTTTGGTGATCCACCC-----GCCTNGGCCTTC|||||| |||| |||| ||||||||| |||| |||||CTCAAACTCCTGGATTTTG--GATCCACCCAGCTGGCCTTGGCCTAA
49
Mapping
CTCAAACTCCTGACCTTTGGTGATCCA
Take a read:
And a reference sequence:>MT dna:chromosome chromosome:GRCh37:MT:1:16569:1GATCACAGGTCTATCACCCTATTAACCACTCACGGGAGCTCTCCATGCATTTGGTATTTTCGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTCGCAGTATCTGTCTTTGATTCCTGCCTCATCCTATTATTTATCGCACCTACGTTCAATATTACAGGCGAACATACTTACTAAAGTGTGTTAATTAATTAATGCTTGTAGGACATAATAATAACAATTGAATGTCTGCACAGCCACTTTCCACACAGACATCATAACAAAAAATTTCCACCAAACCCCCCCTCCCCCGCTTCTGGCCACAGCACTTAAACACATCTCTGCCAAACCCCAAAAACAAAGAACCCTAACACCAGCCTAACCAGATTTCAAATTTTATCTTTTGGCGGTATGCACTTTTAACAGTCACCCCCCAACTAACACATTATTTTCCCCTCCCACTCCCATACTACTAATCTCATCAATACAACCCCCGCCCATCCTACCCAGCACACACACACCGCTGCTAACCCCATACCCCGAACCAACCAAACCCCAAAGACACCCCCCACAGTTTATGTAGCTTACCTCCTCAAAGCAATACACTGACCCGCTCAAACTCCTGGATTTTGTGATCCACCCAGCGCCTTGGCCTAACTAGCCTTTCTATTAGCTCTTAGTAAGATTACACATGCAAGCATCCCCGTTCCAGTGAGTTCACCCTCTAAATCACCACGATCAAAAGGAACAAGCATCAAGCACGCAGCAATGCAGCTCAAAACGCTTAGCCTAGCCACACCCCCACGGGAAACAGCAGTGATTAACCTTTAGCAATAAACGAAAGTTTAACTAAGCTATACTAACCCCAGGGTTGGTCAATTTCGTGCCAGCCACCGCGGTCACACGATTAACCCAAGTCAATAGAAGCCGGCGTAAAGAGTGTTTTAGATCACCCCCTCCCCAATAAAGCTAAAACTCACCTGAGTTGTAAAAAACTCCAGTTGACACAAAATAGACTACGAAAGTGGCTTTAACATATCTGAACACACAATAGCTAAGACCCAAACTGGGATTAGATACCCCACTATGCTTAGCCCTAAACCTCAACAGTTAAATCAACAAAACTGCTCGCCAGAACACTACGAGCCACAGCTTAAAACTCAAAGGACCTGGCGGTGCTTCATATCCCTCTAGAGGAGCCTGTTCTGTAATCGATAAACCCCGATCAACCTCACCACCTCTTGCTCAGCCTATATACCGCCATCTTCAGCAAACCCTGATGAAGGCTACAAAGTAAGCGCAAGTACCCACGTAAAGACGTTAGGTCAAGGTGTAGCCCATGAGGTGGCAAGAAATGGGCTACATTTTCTACCCCAGAAAACTACGATAGCCCTTATGAAACTTAAGGGTCGAAGGTGGATTTAGCAGTAAACTAAGAGTAGAGTGCTTAGTTGAACAGGGCCCTGAAGCGCGTACACACCGCCCGTCACCCTCCTCAAGTATACTTCAAAGGACATTTAACTAAAACCCCTACGCATTTATATAGAGGAGACAAGTCGTAACCTCAAACTCCTGGCCTTTGGTGATCCACCCGCCTTGGCCTACCTGCATAATGAA AAGCACCCAACTTACACTTAGGAGATTTCAACTTAACTTGACCGCTCTGAGCTAAACCTAGCCCCAAACCCACTCCACCTTACTACCAGACAACCTTAGCCAAACCATTTACCCAAATAAAGTATAGGCGATAGAAATTGAAACCTGGCGCAATAGATATAGTACCGCAAGGGAAAGATGAAAAATTATAACCAAGCATAATATAGCAAGGACTAACCCCTATACCTTCTGCATAATGAATTAACTAGAAATAACTTTGCAAGGAGAGCCAAAGCTAAGACCCCCGAAACCAGACGAGCTACCTAAGAACAGCTAAAAGAGCACACCCGTCTATGTAGCAAAATAGTGGGAAGATTTATAGGTAGAGGCGACAAACCTACCGAGCCTGGTGATAGCTGGTTGTCCAAGATAGAATCTTAGTTCAACTTTAAATTTGCCCACAGAACCCTCTAAATCCCCTTGTAAATTTAACTGTTAGTCCAAAGAGGAACAGCTCTTTGGACACTAGGAAAAAACCTTGTAGAGAGAGTAAAAAATTTAACACCCATAGTAGGCCTAAAAGCAGCCACCAATTAAGAAAGCGTTCAAGCTCAACACCCACTACCTAAAAAATCCCAAACATATAACTGAACTCCTCACACCCAATTGGACCAATCTATCACCCTATAGAAGAACTAATGTTAGTATAAGTAACATGAAAACATTCTCCTCCGCATAAGCCTGCGTCAGATTAAAACACTGAACTGACAATTAACAGCCCAATATCTACAATCAACCAACAAGTCATTATTACCCTCACTGTCAACCCAACACAGGCATGCTCATAAGGAAAGGTTAAAAAAAGTAAAAGGAACTCGGCAAATCTTACCCCGCCTGTTTACCAAAAACATCACCTCTAGCATCACCAGTATTAGAGGCACCGCCTGCCCAGTGACACATGTTTAACGGCCGCGGTACCCTAACCGTGCAAAGGTAGCATAATCACTTGTTCCTTAAATAGGGACCTGTATGAATGGCTCCACGAGGGTTCAGCTGTCTCTTACTTTTAACCAGTGAAATTGACCTGCCCGTGAAGAGGCGGGCATAACACAGCAAGACGAGAAGACCCTATGGAGCTTTAATTTATTAATGCAAACAGTACCTAACAAACCCACAGGTCCTAAACTACCAAACCTGCATTAAAAATTTCGGTTGGGGCGACCTCGGAGCAGAACCCAACCTCCGAGCAGTACATGCTAAGACTTCACCAGTCAAAGCGAACTACTATACTCAATTGATCCAATAACTTGACCAACGGAACAAGTTACCCTAGGGATAACAGCGCAATCCTATTCTAGAGTCCATATCAACAATAGGGTTTACGACCTCGATGTTGGATCAGGACATCCCGATGGTGCAGCCGCTATTAAAGGTTCGTTTGTTCAACGATTAAAGTCCTACGTGATCTGAGTTCAGACCGGAGTAATCCAGGTCGGTTTCTATCTACNTTCAAATTCCTCCCTGTACGAAAGGACAAGAGAAATAAGGCCTACTTCACAAAGCGCCTTCCCCCGTAAATGATATCATCTCAACTTAGTATTATACCCACACCCACCCAAGAACAGGGTTTGTTAAGATGGC
CTCAAACTCCTGACCTTTGGTGATCCA|||||||||||| ||||||||||||||CTCAAACTCCTGCCCTTTGGTGATCCA
Hypothesis 1:
Hypothesis 2:
Read
Reference
Read
Reference
Is there any way to break the tie?
Which hypothesis is better?
CTCAAACTCCTGACCTTTGGTGATCCA|||||||||||||||||| ||||||||CTCAAACTCCTGACCTTTCGTGATCCA
50
Mapping
Recall that reads come with per-cycle quality values (in red)
In FASTQ format (left), qualities are encoded as ASCII characters like B, = or %, but really they’re integers [0, 40]
A quality value Q is a function of the probability P that the sequencing machine called the wrong base:
Q = 10: 1 in 10 chance that base was miscalledQ = 20: 1 in 100 chanceQ = 30: 1 in 1000 chance
Higher is “better.”
Qs are estimated by the sequencer’s software and aren’t necessarily accurate
Q = −10 · log10(P )
51
Mapping
CTCAAACTCCTGACCTTTGGTGATCCA
Take a read:
And a reference sequence:>MT dna:chromosome chromosome:GRCh37:MT:1:16569:1GATCACAGGTCTATCACCCTATTAACCACTCACGGGAGCTCTCCATGCATTTGGTATTTTCGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTCGCAGTATCTGTCTTTGATTCCTGCCTCATCCTATTATTTATCGCACCTACGTTCAATATTACAGGCGAACATACTTACTAAAGTGTGTTAATTAATTAATGCTTGTAGGACATAATAATAACAATTGAATGTCTGCACAGCCACTTTCCACACAGACATCATAACAAAAAATTTCCACCAAACCCCCCCTCCCCCGCTTCTGGCCACAGCACTTAAACACATCTCTGCCAAACCCCAAAAACAAAGAACCCTAACACCAGCCTAACCAGATTTCAAATTTTATCTTTTGGCGGTATGCACTTTTAACAGTCACCCCCCAACTAACACATTATTTTCCCCTCCCACTCCCATACTACTAATCTCATCAATACAACCCCCGCCCATCCTACCCAGCACACACACACCGCTGCTAACCCCATACCCCGAACCAACCAAACCCCAAAGACACCCCCCACAGTTTATGTAGCTTACCTCCTCAAAGCAATACACTGACCCGCTCAAACTCCTGGATTTTGTGATCCACCCAGCGCCTTGGCCTAACTAGCCTTTCTATTAGCTCTTAGTAAGATTACACATGCAAGCATCCCCGTTCCAGTGAGTTCACCCTCTAAATCACCACGATCAAAAGGAACAAGCATCAAGCACGCAGCAATGCAGCTCAAAACGCTTAGCCTAGCCACACCCCCACGGGAAACAGCAGTGATTAACCTTTAGCAATAAACGAAAGTTTAACTAAGCTATACTAACCCCAGGGTTGGTCAATTTCGTGCCAGCCACCGCGGTCACACGATTAACCCAAGTCAATAGAAGCCGGCGTAAAGAGTGTTTTAGATCACCCCCTCCCCAATAAAGCTAAAACTCACCTGAGTTGTAAAAAACTCCAGTTGACACAAAATAGACTACGAAAGTGGCTTTAACATATCTGAACACACAATAGCTAAGACCCAAACTGGGATTAGATACCCCACTATGCTTAGCCCTAAACCTCAACAGTTAAATCAACAAAACTGCTCGCCAGAACACTACGAGCCACAGCTTAAAACTCAAAGGACCTGGCGGTGCTTCATATCCCTCTAGAGGAGCCTGTTCTGTAATCGATAAACCCCGATCAACCTCACCACCTCTTGCTCAGCCTATATACCGCCATCTTCAGCAAACCCTGATGAAGGCTACAAAGTAAGCGCAAGTACCCACGTAAAGACGTTAGGTCAAGGTGTAGCCCATGAGGTGGCAAGAAATGGGCTACATTTTCTACCCCAGAAAACTACGATAGCCCTTATGAAACTTAAGGGTCGAAGGTGGATTTAGCAGTAAACTAAGAGTAGAGTGCTTAGTTGAACAGGGCCCTGAAGCGCGTACACACCGCCCGTCACCCTCCTCAAGTATACTTCAAAGGACATTTAACTAAAACCCCTACGCATTTATATAGAGGAGACAAGTCGTAACCTCAAACTCCTGGCCTTTGGTGATCCACCCGCCTTGGCCTACCTGCATAATGAA AAGCACCCAACTTACACTTAGGAGATTTCAACTTAACTTGACCGCTCTGAGCTAAACCTAGCCCCAAACCCACTCCACCTTACTACCAGACAACCTTAGCCAAACCATTTACCCAAATAAAGTATAGGCGATAGAAATTGAAACCTGGCGCAATAGATATAGTACCGCAAGGGAAAGATGAAAAATTATAACCAAGCATAATATAGCAAGGACTAACCCCTATACCTTCTGCATAATGAATTAACTAGAAATAACTTTGCAAGGAGAGCCAAAGCTAAGACCCCCGAAACCAGACGAGCTACCTAAGAACAGCTAAAAGAGCACACCCGTCTATGTAGCAAAATAGTGGGAAGATTTATAGGTAGAGGCGACAAACCTACCGAGCCTGGTGATAGCTGGTTGTCCAAGATAGAATCTTAGTTCAACTTTAAATTTGCCCACAGAACCCTCTAAATCCCCTTGTAAATTTAACTGTTAGTCCAAAGAGGAACAGCTCTTTGGACACTAGGAAAAAACCTTGTAGAGAGAGTAAAAAATTTAACACCCATAGTAGGCCTAAAAGCAGCCACCAATTAAGAAAGCGTTCAAGCTCAACACCCACTACCTAAAAAATCCCAAACATATAACTGAACTCCTCACACCCAATTGGACCAATCTATCACCCTATAGAAGAACTAATGTTAGTATAAGTAACATGAAAACATTCTCCTCCGCATAAGCCTGCGTCAGATTAAAACACTGAACTGACAATTAACAGCCCAATATCTACAATCAACCAACAAGTCATTATTACCCTCACTGTCAACCCAACACAGGCATGCTCATAAGGAAAGGTTAAAAAAAGTAAAAGGAACTCGGCAAATCTTACCCCGCCTGTTTACCAAAAACATCACCTCTAGCATCACCAGTATTAGAGGCACCGCCTGCCCAGTGACACATGTTTAACGGCCGCGGTACCCTAACCGTGCAAAGGTAGCATAATCACTTGTTCCTTAAATAGGGACCTGTATGAATGGCTCCACGAGGGTTCAGCTGTCTCTTACTTTTAACCAGTGAAATTGACCTGCCCGTGAAGAGGCGGGCATAACACAGCAAGACGAGAAGACCCTATGGAGCTTTAATTTATTAATGCAAACAGTACCTAACAAACCCACAGGTCCTAAACTACCAAACCTGCATTAAAAATTTCGGTTGGGGCGACCTCGGAGCAGAACCCAACCTCCGAGCAGTACATGCTAAGACTTCACCAGTCAAAGCGAACTACTATACTCAATTGATCCAATAACTTGACCAACGGAACAAGTTACCCTAGGGATAACAGCGCAATCCTATTCTAGAGTCCATATCAACAATAGGGTTTACGACCTCGATGTTGGATCAGGACATCCCGATGGTGCAGCCGCTATTAAAGGTTCGTTTGTTCAACGATTAAAGTCCTACGTGATCTGAGTTCAGACCGGAGTAATCCAGGTCGGTTTCTATCTACNTTCAAATTCCTCCCTGTACGAAAGGACAAGAGAAATAAGGCCTACTTCACAAAGCGCCTTCCCCCGTAAATGATATCATCTCAACTTAGTATTATACCCACACCCACCCAAGAACAGGGTTTGTTAAGATGGC
CTCAAACTCCTGACCTTTGGTGATCCA|||||||||||| ||||||||||||||CTCAAACTCCTGCCCTTTGGTGATCCA
Hypothesis 1:
Hypothesis 2:
Read
Reference
Read
Reference
Which hypothesis is better?
CTCAAACTCCTGACCTTTGGTGATCCA|||||||||||||||||| ||||||||CTCAAACTCCTGACCTTTCGTGATCCA
Q=30
Q=10
52
Mapping
CTCAAACTCCTGACCTTTGGTGATCCA
Take a read:
And a reference sequence:>MT dna:chromosome chromosome:GRCh37:MT:1:16569:1GATCACAGGTCTATCACCCTATTAACCACTCACGGGAGCTCTCCATGCATTTGGTATTTTCGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTCGCAGTATCTGTCTTTGATTCCTGCCTCATCCTATTATTTATCGCACCTACGTTCAATATTACAGGCGAACATACTTACTAAAGTGTGTTAATTAATTAATGCTTGTAGGACATAATAATAACAATTGAATGTCTGCACAGCCACTTTCCACACAGACATCATAACAAAAAATTTCCACCAAACCCCCCCTCCCCCGCTTCTGGCCACAGCACTTAAACACATCTCTGCCAAACCCCAAAAACAAAGAACCCTAACACCAGCCTAACCAGATTTCAAATTTTATCTTTTGGCGGTATGCACTTTTAACAGTCACCCCCCAACTAACACATTATTTTCCCCTCCCACTCCCATACTACTAATCTCATCAATACAACCCCCGCCCATCCTACCCAGCACACACACACCGCTGCTAACCCCATACCCCGAACCAACCAAACCCCAAAGACACCCCCCACAGTTTATGTAGCTTACCTCCTCAAAGCAATACACTGACCCGCTCAAACTCCTGGATTTTGTGATCCACCCAGCGCCTTGGCCTAACTAGCCTTTCTATTAGCTCTTAGTAAGATTACACATGCAAGCATCCCCGTTCCAGTGAGTTCACCCTCTAAATCACCACGATCAAAAGGAACAAGCATCAAGCACGCAGCAATGCAGCTCAAAACGCTTAGCCTAGCCACACCCCCACGGGAAACAGCAGTGATTAACCTTTAGCAATAAACGAAAGTTTAACTAAGCTATACTAACCCCAGGGTTGGTCAATTTCGTGCCAGCCACCGCGGTCACACGATTAACCCAAGTCAATAGAAGCCGGCGTAAAGAGTGTTTTAGATCACCCCCTCCCCAATAAAGCTAAAACTCACCTGAGTTGTAAAAAACTCCAGTTGACACAAAATAGACTACGAAAGTGGCTTTAACATATCTGAACACACAATAGCTAAGACCCAAACTGGGATTAGATACCCCACTATGCTTAGCCCTAAACCTCAACAGTTAAATCAACAAAACTGCTCGCCAGAACACTACGAGCCACAGCTTAAAACTCAAAGGACCTGGCGGTGCTTCATATCCCTCTAGAGGAGCCTGTTCTGTAATCGATAAACCCCGATCAACCTCACCACCTCTTGCTCAGCCTATATACCGCCATCTTCAGCAAACCCTGATGAAGGCTACAAAGTAAGCGCAAGTACCCACGTAAAGACGTTAGGTCAAGGTGTAGCCCATGAGGTGGCAAGAAATGGGCTACATTTTCTACCCCAGAAAACTACGATAGCCCTTATGAAACTTAAGGGTCGAAGGTGGATTTAGCAGTAAACTAAGAGTAGAGTGCTTAGTTGAACAGGGCCCTGAAGCGCGTACACACCGCCCGTCACCCTCCTCAAGTATACTTCAAAGGACATTTAACTAAAACCCCTACGCATTTATATAGAGGAGACAAGTCGTAACCTCAAACTCCTGGCCTTTGGTGATCCACCCGCCTTGGCCTACCTGCATAATGAA AAGCACCCAACTTACACTTAGGAGATTTCAACTTAACTTGACCGCTCTGAGCTAAACCTAGCCCCAAACCCACTCCACCTTACTACCAGACAACCTTAGCCAAACCATTTACCCAAATAAAGTATAGGCGATAGAAATTGAAACCTGGCGCAATAGATATAGTACCGCAAGGGAAAGATGAAAAATTATAACCAAGCATAATATAGCAAGGACTAACCCCTATACCTTCTGCATAATGAATTAACTAGAAATAACTTTGCAAGGAGAGCCAAAGCTAAGACCCCCGAAACCAGACGAGCTACCTAAGAACAGCTAAAAGAGCACACCCGTCTATGTAGCAAAATAGTGGGAAGATTTATAGGTAGAGGCGACAAACCTACCGAGCCTGGTGATAGCTGGTTGTCCAAGATAGAATCTTAGTTCAACTTTAAATTTGCCCACAGAACCCTCTAAATCCCCTTGTAAATTTAACTGTTAGTCCAAAGAGGAACAGCTCTTTGGACACTAGGAAAAAACCTTGTAGAGAGAGTAAAAAATTTAACACCCATAGTAGGCCTAAAAGCAGCCACCAATTAAGAAAGCGTTCAAGCTCAACACCCACTACCTAAAAAATCCCAAACATATAACTGAACTCCTCACACCCAATTGGACCAATCTATCACCCTATAGAAGAACTAATGTTAGTATAAGTAACATGAAAACATTCTCCTCCGCATAAGCCTGCGTCAGATTAAAACACTGAACTGACAATTAACAGCCCAATATCTACAATCAACCAACAAGTCATTATTACCCTCACTGTCAACCCAACACAGGCATGCTCATAAGGAAAGGTTAAAAAAAGTAAAAGGAACTCGGCAAATCTTACCCCGCCTGTTTACCAAAAACATCACCTCTAGCATCACCAGTATTAGAGGCACCGCCTGCCCAGTGACACATGTTTAACGGCCGCGGTACCCTAACCGTGCAAAGGTAGCATAATCACTTGTTCCTTAAATAGGGACCTGTATGAATGGCTCCACGAGGGTTCAGCTGTCTCTTACTTTTAACCAGTGAAATTGACCTGCCCGTGAAGAGGCGGGCATAACACAGCAAGACGAGAAGACCCTATGGAGCTTTAATTTATTAATGCAAACAGTACCTAACAAACCCACAGGTCCTAAACTACCAAACCTGCATTAAAAATTTCGGTTGGGGCGACCTCGGAGCAGAACCCAACCTCCGAGCAGTACATGCTAAGACTTCACCAGTCAAAGCGAACTACTATACTCAATTGATCCAATAACTTGACCAACGGAACAAGTTACCCTAGGGATAACAGCGCAATCCTATTCTAGAGTCCATATCAACAATAGGGTTTACGACCTCGATGTTGGATCAGGACATCCCGATGGTGCAGCCGCTATTAAAGGTTCGTTTGTTCAACGATTAAAGTCCTACGTGATCTGAGTTCAGACCGGAGTAATCCAGGTCGGTTTCTATCTACNTTCAAATTCCTCCCTGTACGAAAGGACAAGAGAAATAAGGCCTACTTCACAAAGCGCCTTCCCCCGTAAATGATATCATCTCAACTTAGTATTATACCCACACCCACCCAAGAACAGGGTTTGTTAAGATGGC
CTCAAACTCCTGACCTTTGGTGATCCA|||||||||||| ||||||||||||||CTCAAACTCCTG-CCTTTGGTGATCCA
Hypothesis 1:
Hypothesis 2:
Read
Reference
Read
Reference
Is there any way to break the tie?
Hint: In Illumina sequencing, sequencing errors almost never manifest as gaps
Which hypothesis is better?
CTCAAACTCCTGACCTTTGGTGATCCA|||||||||||||||||| ||||||||CTCAAACTCCTGACCTTTCGTGATCCA
53
Mapping
CTCAAACTCCTGACCTTTGGTGATCCA|||||||||||| ||||||||||||||CTCAAACTCCTG-CCTTTGGTGATCCA
Read
Reference
Aligners can employ penalties to account for the relative probability of seeing different dissimilarities
Estimates vary, but small gaps (“indels”) occur in humans at 1 in ~10-100K positions.
SNPs occur in humans at 1 in ~1K positions, but depending on Q, sequencing error may be more likely
Penalty = 45
CTCAAACTCCTGACCTTTGGTGATCCA||||| |||||||||||||| ||||||CTCAA-CTCCTGACCTTTGGCGATCCA
Read
Reference
Penalty = 55
Q=10
CTCAAACTCCTGACCTTTGGTGATCCA|||||||||||||||||||||| ||||CTCAAACTCCTGACCTTTGGTGCTCCA
Read
Reference
Penalty = 30
Q=40
Pengap ≡ −10 log10(Pgap)
= −10 log10(0.00005)
≈ 45
Penmm ≡ argmin(−10 log10(Pmiscall),−10 log10(PSNP))
= argmin(Q,−10 log10(0.001))
= argmin(Q, 30)
54
Resources• Bowtie: ultra-fast mapping of short reads to
reference genome
• http://bowtie-bio.sourceforge.net
55
RNA-seq differential expression
GATCACAGGTCTATCACCCTATTAACCACTCACGGGAGCTCTCCATGCATTTGGTATTTTCGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTCGCAGTATCTGTCTTTGATTCCTGCCTCATCCTATTATTTATCGCACCTACGTTCAATATT
GTCGCAGTATCTGTCT GTCGCAGTATCTGTCT GTCGCAGTATCTGTCT GTCGCAGTATCTGTCT GTCGCAGTATCTGTCT TGTCGCAGTATCTGTC TATGTCGCAGTATCTG TATATCGCAGTATCTG TATATCGCAGTATCTG TATATCGCAGTATCTG CCCTATATCGCAGTAT AGCACCCTATGTCGCA AGCACCCTATATCGCA AGCACCCTATGTCGCA GAGCACCCTATGTCGC CCGGAGCACCCTATAT CCGGAGCACCCTATATGCCGGAGCACCCTATG
GTCGCAGTANCTGTCT||||||||| ||||||GTCGCAGTATCTGTCT
GGATCTGCGATATACC|||||| |||||||||GGATCT-CGATATACC
AATCTGATCTTATTTT||||||||||||||||AATCTGATCTTATTTT
ATATATATATATATAT||||||||||||||||ATATATATATATATAT
TCTCTCCCANNAGAGC||||||||| |||||TCTCTCCCAGGAGAGC
Align Aggregate
Statistics
Gene 1differentially expressed?: YES
p-value: 0.0012
TGTCGCAGTATCTGTC AGCACCCTATGTCGCAGCCGGAGCACCCTATGGTCGCAGTANCTGTCT
||||||||| ||||||GTCGCAGTATCTGTCT
GGATCTGCGATATACC|||||| |||||||||GGATCT-CGATATACC
AATCTGATCTTATTTT||||||||||||||||AATCTGATCTTATTTT
ATATATATATATATAT||||||||||||||||ATATATATATATATAT
TCTCTCCCANNAGAGC||||||||| |||||TCTCTCCCAGGAGAGC
Align Aggregate
Gene 1
Sample A
Sample B
56
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