second-generation sequencing introduction to second-generation

Introduction tosecond-generation

sequencingCMSC858B Spring 2012

Many slides courtesy of Ben Langmead

1

!

Corrada Bravo 10/30/09

Second-GenerationSequencing

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2

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Human Epigenome Project

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Methylation

3

ENCODE project

http://www.genome.gov/10005107

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1000 Genomes Project

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6

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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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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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Sec-gen Sequencing

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Sec-gen Sequencing

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Fragmentation is random, i.e., not equal-sized (but hard to draw)

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!


Sec-gen Sequencing

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11

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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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12

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


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

15


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

16


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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x 100s of millions

(slide courtesy of Ben Langmead)23

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Sec-gen Sequencing

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Sec-gen Sequencing

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Sec-gen Sequencing

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Sec-gen Sequencing

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Sec-gen Sequencing

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Sec-gen Sequencing

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Sec-gen Sequencing

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Image Analysis

Figure 2.2: Image analysis stages in Firecrest

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11000

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450 460 470 480 490 500 510 520 530 540 550

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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Sec-gen Sequencing

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First Cycle

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Sec-gen Sequencing

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Second Cycle

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A Thought Experiment

Color coded by call made: A, C, G, T

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Fluorescence Intensity


!"#$ %&'(()*+,*#"$)-%)(%)+.(/)(-./01+%0%*)+2

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37

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Fluorescence Intensity


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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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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

46

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

47

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?


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


Take a read:


CTCAAACTCCTGACCTTTGGTGATCCA|||||||||||| ||||||||||||||CTCAAACTCCTGCCCTTTGGTGATCCA

Hypothesis 1:

Hypothesis 2:

Read

Reference

Read

Reference



Q=30

Q=10

52

Mapping


Take a read:


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



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

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