Take a read: mRNA abundance - University Of...

mRNA abundance estimation with RNAseq

Hector Corrada BravoCMSC858B Spring 2012

Many slides courtesy of Ben Langmead @ JHSPH

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?

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

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

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 )

Mapping


Take a read:


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

Hypothesis 1:

Hypothesis 2:

Read

Reference

Read

Reference



Q=30

Q=10

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



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)

Fast Alignment• Bowtie: ultra-fast mapping of short reads to

reference genome

• http://bowtie-bio.sourceforge.net

0 0 0 15 15 0 0 15 15 0 0 0 0 0 0 0 0 0 0 0 0 15 15 0 0 0 0 15 15

15 15 15 0 0 30 15 0 0 0 15 0 15 0 0 15 15 15 0 15 15 0 0 30 0 0 0 0 0

0 0 0 30 15 0 0 30 15 0 0 0 0 0 0 0 0 0 0 0 0 30 15 0 0 0 0 15 15

0 0 0 15 45 5 0 15 45 5 0 0 0 0 0 0 0 0 0 0 0 15 45 5 0 0 0 15 30

0 0 0 0 5 15 0 0 5 60 20 0 0 0 15 0 0 0 15 0 0 0 5 15 0 15 15 0 0

15 15 15 0 0 20 30 0 0 20 75 35 15 0 0 30 15 15 0 30 15 0 0 20 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 35 90 50 30 0 0 0 0 0 0 0 0 0 0 35 0 0 0 0

0 0 0 0 0 0 0 0 0 15 0 50 60 20 45 5 0 0 15 0 0 0 0 0 0 50 15 0 0

15 15 15 0 0 15 15 0 0 0 30 10 65 30 5 60 20 15 0 30 15 0 0 15 0 10 20 0 0

c c c t t c c t t a c g c g a c c c a c c t t c g a a t ttcttacgac

Reference (n)

Read

(m)

Dynamic programming alignment

n >> m

Fill

Smith-Waterman

d = 6 billion (2 x 109) reads m = 100 ntn = 3 billion (3 x 109) nt

!

! 1 week-long run of

Illumina HiSeq 2000

100 processors, each capable of 100 billion cell updates per second, would take 6 years

! human

Dynamic programming alignment

Total of d x m x n = 2 x 1021 cell updates

Indexing

Seeding schemes:

Map data structures:

When to extend:

Contiguous seeds, spaced seeds, various weights, spans, intervals, ...

Hash table, lookup table, sorted arrays, trie, "nite state machine, ...

Every seed hit, every N hits, q-gram "lter, ...

Maq, SOAP, ZOOM!, GSNAP, BFAST, BLAT, ELAND, MOSAIK, Novoalign, RazerS, PerM, SHRiMP, ...

Inverted index

“Su#x” index

6531042

$A$ANA$ANANA$BANANA$NA$NANA$

Su#x tree Su#x array

$ 45 GB forhuman genome

$ 12 GB forhuman genome

Burrows-Wheeler Transform

Burrows M, Wheeler DJ: A block sorting lossless data compression algorithm. Digital Equipment Corporation, Palo Alto, CA 1994, Technical Report 124; 1994

g a t a c a $ a c t g a $ aT

Burrows-Wheeler Matrix

BWT(T)All cyclic rotations

Sort

Last column

$ g a t a c aa $ g a t a ca c a $ g a ta t a c a $ gc a $ g a t ag a t a c a $t a c a $ g a

FM Index

Ferragina P, Manzini G: Opportunistic data structures with applications. Foundations of Computer Science. IEEE Computer Society; 2000.

FM Index = BWT(T) + Auxiliary data structures

Given string ! that occurs in the reference, and given character x, does x! also occur in the reference?Q(x, !):

Gives us O(1) query:

FM Index query

Read: ACATReference: CATCAT

Q(T, %): YesQ(A, T): Yes

Q(C, AT): YesQ(A, CAT): No

Read: ATCAReference: CATCAT

Q(A, %): YesQ(C, A): Yes

Q(T, CA): YesQ(A, TCA): Yes

Given string ! that occurs in the reference, and given character x, does x! also occur in the reference?Q(x, !):

No match Match

Ferragina P, Manzini G: Opportunistic data structures with applications. Foundations of Computer Science. IEEE Computer Society; 2000.

6531042

$A$ANA$ANANA$BANANA$NA$NANA$

Su#x tree Su#x array$ 45 GB $ 12 GB

FM Index

$ B A N A N AA $ B A N A NA N A $ B A NA N A N A $ BB A N A N A $N A $ B A N AN A N A $ B A

2-4 GB

FM Index is small

For human genome

Approximate matching

Approximate: match successive su#xes, try hypothetical edits

Exact: match successive su#xes

TA A Read: TAATReference: CATCAT

TNo

TA ATNo Edited read: TCATReference: CATCATTYes C

Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-e#cient alignment of short DNA sequences to the human genome. Genome Biology. 2009;10(3):R25

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





Align Aggregate

Gene 1

Sample A

Sample B

How should expression levels be estimated?

21

• A-B are distinguished by the presence of splice junction (a) or (b).

• A-C are distinguished by the presence of splice junction (a) and change in UTR

• B-C are distinguished by the presence of splice junction (b) and change in UTR

(a)(b)

UI genes

Original transcripts

(a) UI vs. Ensembl genes

A

B

UI gene

!

!

!

!

!

!

5' 3'

(b) UI genes and read-counting

Figure SS1: Union-intersection and Ensembl gene models. Panel (a): Illustration of union-intersection(UI) and Ensembl gene definitions for two genes (pink and blue) with multiple isoforms (see Section S2).The original transcripts, as would be reported by Ensembl, are displayed in the top panel. Below are thecorresponding UI and Ensembl gene models. Note that because the genes overlap, the entire exon region isremoved, not just the overlap. Panel (b): Illustration of read-counting for a gene with two isoforms. IsoformA has a shorter 3’-most exon as compared to Isoform B. The UI gene model includes the entire 3’-most exonfor Isoform A. In addition to reads originating from the constitutive portion of the UI gene, reads emanatingexclusively from Isoform B may also be counted.

46

Union-Intersection Genes

22

Measurements

12........G

1 2 ……….N

DATA MATRIX

Samples (individuals)

UI G

enes Measurements are now

read counts (# of fragments)per UI-gene per sample

Normalization

• Sequencing depth varies a lot (you get what you pay for)

• Thus, counts must be normalized across samples to make them comparable

Normalization

• Proposals:

• total count normalization: not great, genes with very high counts can skew results

• upper-quartile normalization: about 75% of the data behaves similarly, only upper counts mess things up

Normalization

Modeling

• Three main proposals:

• Counts follow a Poisson distr.

• Works well capturing technical replicates, but, too limited to capture biological variability?

• log(Counts) follow a Normal distribution (Myrna)

• Seems to capture biological variability better, for large sample sizes

• Counts follow a negative binomial distribution (DEseq)

Poisson Distribution

f(k;λ) =e−λλk

k!Mean & Variance: λ

Overdispersion

From: Anders et al. Genome Biology 2010

Poisson

DEseq (local regression)edgeR

Negative Binomial

• Assume count data has a negative binomial distribution

• This is a common technique to model count data where over-dispersion is apparent

• This is what DESeq uses

• One interpretation: number of successful trials before r failures occur, where probability of success is p

Negative Binomial

f(k; r, p) =�

k + r − 1r − 1

�pr(1− p)k

Also parametrized by mean and variance

p =µ

σ2 r =µ2

σ2 − µ

Models (Myrna)

• Poisson

• Normal

log(E(cij |yj)) = bi0 + ηi log(qj) + bi1yj

Gene i, sample j: yj is a group indicator

E(log(cij)|yj) = bi0 + ηi log(qj) + bi1yj

Test

• Differential expression tests that the means for each group are different

• Idea: compare model where means are allowed to be different vs. model where means are constrained to be equal

• Statistic: log of likelihood ratio

• This statistic follows an asymptotic chi-square distribution

• But getting p-values by permutation is preferred

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RNAseq

• Fairly new assay, kinks are still being worked out

• The statistics for moderate sample sizes are still under-developed

• Computational requirements are going to get worse

• Cloud architectures might be the way to go for many analysts

Myrna

Gene 1GATCACAGGTCTATCACCCTATTAACCACTCACGGGAGCTCTCCATGCATTTGGTATTTTCGTCTGGGGGGTATGCACGCGATAGCATTGCGAGACGCTGGAGCCGGAGCACCCTATGTCGCAGTATCTGTCTTTGATTCCTGCCTCATCCTATTATTTATCGCACCTACGTTCAATATT

GTCGCAGTATCTGTCT GTCGCAGTATCTGTCT GTCGCAGTATCTGTCT GTCGCAGTATCTGTCT TGTCGCAGTATCTGTC TATGTCGCAGTATCTG TATATCGCAGTATCTG TATATCGCAGTATCTG TATATCGCAGTATCTG CCCTATATCGCAGTAT AGCACCCTATGTCGCA AGCACCCTATATCGCA AGCACCCTATGTCGCA GAGCACCCTATGTCGC CCGGAGCACCCTATAT CCGGAGCACCCTATATGCCGGAGCACCCTATG






Gene 1differentially expressed?: YES

p-value: 0.0012

TGTCGCAGTATCTGTC AGCACCCTATGTCGCAGCCGGAGCACCCTATG






Sample A

Sample B

Align Aggrega

te

Aggregate

Overlap

Aggregate Normalize

Aggregate Normali

ze

Aggregate

Statist

ics

Align

Myrna

Parallel by read

Parallel by genome bin

Parallel by sample

Parallel by gene

Aggregate Overlap Aggregate Normalize AggregateAlign Statistics

Take a read: mRNA abundance - University Of...

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