RNA-Seq data analysis Xuhua Xia University of Ottawa [email protected] .

RNA-Seq data analysis

Xuhua XiaUniversity of Ottawa

[email protected]://dambe.bio.uottawa.ca

RNA-Seq

Gene2Gene1 Gene3Genome

Transcriptome

FASTQ files:@SEQ_ID1.1GATTTGGGGTTCAAAGCA...+!''*((((***+))%%%+...@SEQ_ID2.1GATTTGGGGTTCAAAGCA...+!''*((((***+))%%%+.........

RNA-Seq

SRA files for Data storage, transmission and analysis

Next-Generation sequencing

FASTQ files:@SEQ_ID1.1NATTTGGGGTTCAAAGCA...+!''*((((***+))%%%+...@SEQ_ID2.1GATTTGGGGTTCAAAGCA...+%''*((((***+))%%%+.........

De novo genome assembly

Sequence reads matching/aligning against a known genome

Key research objectives:Differential gene expressionRibosomal profilingAlternative splicingGene discoverySignal at TSS and TTS……

Submission to one of the three data centers (NCBI, DDBJ, EBI):SRA (sequence read archive) compressed files

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Storage, transmission and analysisQuality assessment

Phred quality score Q=-10log10p, where p is base-calling error probability.

1. Global quality assessment

2. Read-specific quality assessment

3. Site-specific quality assessment

Quality assessment: Nucleotide

36 40 44 48 52 56 60 64 68 720

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Quality score

Per

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36 40 44 48 52 56 60 64 68 720

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Count.1 Count.2

Quality score

Per

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SRR1536586: Single, ReadLen = 50 SRR892245: Paired, ReadLen = 100 SRR2056426: Paired, ReadLen = 250

45 47 49 51 53 55 57 59 61 63 65 67 69 71 730

5

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Count.1 Count.2

Base quality scorePe

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Read-based quality

36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 720

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Resolved Has N

Quality score

Perc

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45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 720

50000

100000

150000

200000

250000

Count.1 Count.2

Sequence quality score

Perc

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SRR2056426: Paired, excluding N-containing readSRR1536586: Single, ReadLen = 50

Site-specific quality by nucleotide

0 50 100 150 200 25060

62

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A C G T

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0 50 100 150 200 25050

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A C G T

Site

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0 50 100 150 200 25052

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A C G T

Site

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0 50 100 150 200 25045

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A C G T

Site

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Fully resolved paired reads Paired reads containing unresolved nucleotides

SRR2056426

Read 1

Read 2

Read 1

Read 2

Gene expression

Gene2Gene1 Gene3Genome

Transcriptome

Count N1 = 6 N2 = 29 N3 = 4

NTMR = 2230000 (TMR: total mapped reads); L1 = 500 nt, L2 = 3000 nt, L3 = 400 nt

GE: FPKM1 = (1000*N1/L1)*(1000000/NTMR) FPKM2 = (1000*29/3000)/2.23 FPKM3 = (1000*4/400)/2.23 = 5.38 = 4.33 = 4.48

FPKM: Fragments Per Kilobase of exon per Million reads: "per kilobase": fair comparison among genes; "per million reads": fair comparison among samples

BLAST, FASTA, etc. (more details later)

Paralogue B

Identical segmentDifferent but with clear homology

Homology lost in evolution

Paralogue A

NA.H= 6

NB.H = 3

NA.U= 4

NB.U = 3

NI = 29

PA = (NA.H + NA.U)/(NA.H + NB.H + NA.U + NB.U) = (6+4)/(6+4+3+3) = 0.625NA = NA.H + NA.U + NI*PA = 6+4+29*0.625 = 28.125NB = NB.H + NB.U + NI*(1-PA) = 3+3+29*0.375 = 16.875

Scale NA and NB to FPKMSubscripts: H - different but homologous; I - identical segment; U - unique/divergent segment

Gene expression with duplicated genes

Paralogue B

Identical segment

Different but with clear homology


Paralogue A

NA.H= 6

NB.H = 3

NA.U= 4

NB.U = 3

NI = 29

Two alternatives:1. PA = NA.H /(NA.H + NB.H) = 6/(6+3) = 0.666672. nA.H = 6/LH; nB.H = 3/LH; nA.U = 4/LA.U; nB.U = 3/LB.U

PA = (nA.H + nA.U)/(nA.H + nB.H + nA.U + nB.U)

NA = NA.H + NA.U + NI*PA NB = NB.H + NB.U + NI*(1-PA)

Duplicated genes of different lengths

LB.U

LA.U

Paralogue B

Identical segmentDifferent but with clear homology


Paralogue A

NA.H= 6NB.H = 2NC.H = 1

NA.U= 4NB.U = 2NC.U = 1

NI = 29

PA = (NA.H + NA.U)/(NA.H + NB.H + NA.U + NB.U+ NB.H + NA.U + NB.U) = (6+4)/(6+4+2+2+1+1) = 0.625NA = NA.H + NA.U + NI*PA = 6+4+29*0.625 = 28.125NB = NB.H + NB.U + NI*PB

NC = NC.H + NC.U + NI*PC

Subscripts: H - different but homologous; I - identical segment; U - unique/divergent segment

Gene expression with duplicated genes

Paralogue C

Multiple paralogues

PG1 309

PG2 204

PG3 101

Gene NH NI NU

PG3PG2PG1

600

102

510

N3 = 102+101+600*P3

N2 = 204+510*204/(204+309)+600*P2

N1 = 309+510*309/(204+309)+600*P1

P3 = (102+101)/(102+101+510+204+309) 0.1656P2 = (1-P3)*204/(204+309) = 0.3318P1 = (1-P3)*309/(204+309) = 0.5026

N3 = 302.35N2 = 605.90N1 = 917.75

More details later on tree reconstruction

Ribosomal density

Xuhua Xia Slide 12

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

<=3 4 5 6 7 8 9 10-11 >=12

Poly(A) Length

Me

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Mean density adjusted for mRNA length. The confounding effect of elongation efficiency

Xia et al. 2011 Genetics

Transcription: TSS and TTS

AUG… …UAATSS1 TSS2 TTS1 TTS2

Exp. 1

Exp. 2

Alternative splicing

E3E1 E2

I1I2

5'SS 5'SS3'SS 3'SS

E3E1 E2E3E1

Alternative splicing

Cell type 1 Cell type 2

New approaches in data analysis• RNA-Seq data files are too large:

– Among the 4717 RNA-Seq studies on human, available at NCBI on Jun. 10, 2015, 141 studies each contributed more than 1TB of nucleotide bases.

– Even NCBI has found it difficult to keep pace with the explosive growth of RNA-Seq data.

• The RNA-Seq data do not need to be so huge.– SRR1536586.sra (E. coli K12) contains 6,503,557

sequences of 50 nt each, but 195310 sequences are all identical, all from sites 929-978 in E. coli 23S rRNA genes. There is no information lost if all these 195310 identical sequences is listed by a single sequence with a sequence ID such as SeqID_195310.

Xuhua Xia Slide 16

Most frequent 50-mers in SRR1536586.sraGene Ncopy Gene Ncopy

LSU rRNA 195310 LSU rRNA 14193

LSU rRNA 86308 hisR(2) 13720

5S rRNA 73440 hisR(2) 13618


SSU rRNA 47323 LSU rRNA 13012

LSU rRNA 45695 5S rRNA 13001


5S rRNA 33674 LSU rRNA 12695

SSU rRNA 30417 LSU rRNA 12523

LSU rRNA 29508 SSU rRNA 11696

5S rRNA 28187 LSU rRNA 11298

LSU rRNA 24982 glnX_V(1) 11081

SSU rRNA 23286 5S rRNA 10968

LSU rRNA 19991 5S rRNA 10890

SSU rRNA 19268 5S rRNA 10750

glnX_V(1) 18652 b3555|b3556(3) 10513


hisR(2) 18354 LSU rRNA 10164


LSU rRNA 17113 trpT 9955

glnX_V(1) 16902 rpsE(4) 9877


LSU rRNA 14642 rplV(4) 9071

Next-Generation sequencing

FASTQ files:@SEQ_ID1.1NATTTGGGGTTCAAAGCA...+!''*((((***+))%%%+...@SEQ_ID2.1GATTTGGGGTTCAAAGCA...+%''*((((***+))%%%+.........

De novo genome assembly

Sequence reads matching/aligning against a known genome

Key research objectives:Differential gene expressionRibosomal profilingAlternative splicingGene discoverySignal at TSS and TTS……

Submission to one of the tree data centers (NCBI, DDBJ, EBI):SRA (sequence read archive) compressed files

FASTAQ+ file:>SeqGroup1_3GATTTGGGGTTCA>SeqGroup2_391GATTTGGGGTTCAAAGCA>SeqGroup3_92GATTTGGGGTTCAAAGCA>SeqGroup4_512GATTTGGGGTTCAAAGCA......

Downl

oad

by re

sear

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s

Subm

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Submission

Download

Storage, transmission and analysis

Formatted BLAST output

Xuhua Xia Slide 19

b0001|190_255,SeqGr49062_16,100.00,49,0,0,18,66,1,49,3e-019,91.6b0001|190_255,SeqGr382517_1,100.00,48,0,0,19,66,1,48,1e-018,89.8b0001|190_255,SeqGr536414_1,100.00,46,0,0,21,66,1,46,2e-017,86.1b0001|190_255,SeqGr181138_10,100.00,45,0,0,22,66,1,45,5e-017,84.2b0001|190_255,SeqGr138539_1,100.00,44,0,0,23,66,1,44,2e-016,82.4b0001|190_255,SeqGr297866_1,100.00,42,0,0,25,66,1,42,3e-015,78.7b0002|337_2799,SeqGr935243_1,100.00,50,0,0,185,234,1,50,4e-018,93.5b0002|337_2799,SeqGr925087_1,100.00,50,0,0,1398,1447,1,50,4e-018,93.5b0002|337_2799,SeqGr922536_1,100.00,50,0,0,2050,2099,1,50,4e-018,93.5b0002|337_2799,SeqGr918509_1,100.00,50,0,0,201,250,1,50,4e-018,93.5……

RNA-Seq data analysis Xuhua Xia University of Ottawa [email protected] .

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Transcript of RNA-Seq data analysis Xuhua Xia University of Ottawa [email protected] .