TCCAACATGACAACCACTGTAGTGT, >84_11_1490_F3, AGGACGCTCATTACTCTCCGCGATC, >84_12_96_F3, GGTGCGGACAGCTGTGCACACGTAG, >84_12_498_F3, ACCCCCCCCCGCGTGCAAACTGATT, >84_12_895_F3
TCTTTCTTGGCAAATGGGACGTAGA, >84_12_1842_F3, TGGTGCTTGTGCCCCGATCTAATCT, >84_13_805_F3, TCCTCTTAGGCATCAACATCGAGCT, >84_13_1005_F3, CTTGGCAAAGGAACACACAAGTCGG, >84_13_1636_
ACCACCCGTGTGTGTGTGTGTGTGT, >84_14_1025_F3, CACACCCATCGTGGGTGCACATGCA, >84_14_1721_F3, TTTGGTTTGTAAAAACCTTGTTGGG, >84_14_2027_F3, TTCAAGGCTTAGTGTCGGTGCATCT, >84_15_700_
AGCCAATGAACTGGAGGCATCCTCC, >84_15_1616_F3, CTTGCTCTGCTCCCCTCTTTAAAAA, >84_15_1753_F3, CTTACGACAGCAATGGGACTAGATC, >84_16_669_F3, CACTTGTTTGGGGTGTGACATTGTG, >84_16_836_F3
CATTGCCCCGGCGAGCGCTAGCGGG, >84_16_895_F3, TGCATCGTATTGCCATGCATATGTG, >84_16_988_F3, GTTCCATTCATTGTGCTCCATGTGT, >84_16_1436_F3, CGAGTATCTTTAAATATTGTAAGAG, >84_17_1325_F3
ATTTTTCCTTCACTGCGCGATTTTT, >84_17_1428_F3, TACTCTTTCATTTGTGCTCTCCCGA, >84_17_1598_F3, TTTGCTTCTACCACAGGTGGCGCCG, >84_18_748_F3, CAAGTAACTGGCGCCGAGGTTAGCA, >84_18_1277_F
TCATTGTCGAGGTTTCCGTGAGTAG, >84_18_1445_F3, TAACCACCAATCTCTCGAAATTCAT, >84_18_1505_F3, GGTGTGGGTGCACACGTGTGCACAC, >84_18_1720_F3, ACTTTATCAGCTTAAACAAGCGAGA, >84_19_301_F
GTCCATGTTCTTCAGCGCAGATCGA, >84_19_618_F3, TGTTTGAACCGCCACCCTCAGTCGA, >84_19_1122_F3, GTTCTATGTGGCCGAGACATCGGCT, >84_20_739_F3, TCGTCCACTGAACTACCACTCCTCT, >84_20_1915_F
GGTTTAAAGTGTCGAACGAATTATT, >84_20_1927_F3, GTGCACAATGCCACATGTGTGTGTG, >84_21_1340_F3, GTTTGAAGTGTCGACAGCTGCGTAC, >84_21_1383_F3, GTACATGCCGCCAGTGTTGTACGGG, >84_21_1457
CCAAAAGAAAAAGCGGGAAATCCGG, >84_22_671_F3, TGTCCATAGGCGCTGCAGCTACGTC, >84_22_971_F3, AAACTTGGTCCCTGTTCGGTAGCTC, >84_22_1092_F3, GGTAAACACAAACACGCCACGACTG, >84_22_1137_
TTGCAGTGTTTGGCCCCAATCCCCA, >84_22_1348_F3, ATCCCCCTGGCCGAAGGTTTAGATT, >84_22_1359_F3, TTAATGAAACGGCCAGGAAATATCT, >84_22_1630_F3, CGGTGTCCGGCACTACCTGGCCGTC, >84_22_1924
TCGGCCGTGTGGTGTTATTTAGACC, >84_22_1967_F3, GTAGGGGGATAAGTCTCTAGCTCGG, >84_23_310_F3, GACCTAGACAGTGTAGTGTGTACAA, >84_23_1131_F3, TCCATCCCTTAGGTTAAGATATCGT, >84_23_1142_
GCGTCCTCGGCGGGTACTGTATTCC, >84_23_1663_F3, CTCGTATTTGTTCCAGAGGTCCCAA, >84_23_1805_F3, TTCATTCCTTTTTTGGGACCGAGCT, >84_24_675_F3, GTGTTGAACCACACAGTGTGTTGTG, >84_24_1168_F
CTTTCCGGTCACTTCCCTGAGCTAT, >84_24_1286_F3, GTCCTCAAGTTCAAGGTTCTATAAC, >84_25_590_F3, AGCTTTTCTTAGAGAAAACAGTCCA, >84_25_1419_F3, CTCGGGGGAGAAATCTATGTAGACC, >84_25_1593_F
TCTCTTGGTATGGATGGACATGAAG, >84_26_789_F3, TGGATACAACGACACGGACAGCAGT, >84_26_929_F3, ACCGATCTCTTACTCTACCGGCGAA, >84_26_1317_F3, TTTGTTTAAGGTTGAGTCACTCTGT, >84_26_1587_F3
GGTTCAGCACTCTGCGTGGCGATGT, >84_27_871_F3, TATCACTTCCTTCTTCACCCGGAGA, >84_27_1066_F3, CGGCGCGCACTTAAACAATTATACC, >84_27_1128_F3, TGTTGTTCTAGGCTATGATGACAGG, >84_27_1164_
TGTCCGTCAGCCACCCTCTGCAAGA, >84_27_1481_F3, TACGAAATCTATTTCCCTCCGAGAA, >84_27_1609_F3, GTTGTACACCAGTGTACACATACAC, >84_27_1632_F3, TCTTTTATAATGTTCGAGGGCTTTT, >84_27_1726_F
CCTTTTTCTGCCCAGCCTTCGAGCC, >84_28_964_F3, GTGCAGAGTATCTCATCTGAGTATA, >84_28_1061_F3, GTAGTGTCCAGCGCTATCTCGAGAG, >84_28_1288_F3, CGGGATTAACGTTGTAGCACTGAAC, >84_29_617_F
AGAGATTGTCGGTGACACATGTGCT, >84_29_944_F3, TTCTGGATCTCTAAAAGACCCCATC, >84_29_1211_F3, GAATTTACACACCCCCAGTGCAAAC, >84_29_1452_F3, GTGACAGACAAACACACACATGTGT, >84_29_1666_F
CTTCTGTAAGCCCCTCGAGGACTGA, >84_29_1679_F3, CCTCCTTGCTAGGGGGGCTCTCTCT, >84_30_1335_F3, GGTCAGAATTTGGGGAGAGTATCTC, >84_30_1413_F3, TTTTCTCCAATTTGGAAGTTAGCTC, >84_30_1507
CATCAGACATAACATCAGGATCCCG, >84_30_1856_F3, GGCCTCAATACCTCTAGCTCCTATC, >84_31_693_F3, GCACACAGACACCACGTTTGTGTCA, >84_31_1296_F3, AGCGGTTTCCCGCGTATCGTATAGG, >84_31_1522_
TGGTATCATTAACCAGGGGTAGAGT, >84_31_1713_F3, CCTTTCTGATACTTGAGCAATGACA, >84_32_702_F3, TTGTTTCTTTAAAAAGAGTGACTAC, >84_32_1157_F3, GTGTGTTGTGTGTGTGTGTGTGTGT, >84_32_1362_F3
GAACTCTTCAGGGGCTCTTTACATC, >84_32_1457_F3, CTATTCCTTTAGGTTAAGTTCTCCG, >84_33_568_F3, GCATGCGCGTAATGATGTACGTAAG, >84_33_1086_F3, TCCTACCACAATGTGTGTGGCGTAA, >84_33_1373_F
TGAGCTCTCTGTTTACACACACAAC, >84_34_619_F3, CTGACATTACTCTTTCAGGGCTCCT, >84_34_634_F3, TTTTTTTTTTTTTTTTTAAAGAGCC, >84_34_1180_F3, GTGACTCTGTCCTGTCTACAGCTGT, >84_34_1348_F3,
GGCGCTTTCTTTTCCTTCTGCCTCC, >84_34_1492_F3, TGTTGTTTTGTCAGAACTCATGGTT, >84_35_732_F3, TTCGTGTAATTTAACAGTGTAGAAC, >84_35_1200_F3, TTTACCACATTGATTACTAATTTCT, >84_35_1738_F3,
TTGCCTTGGGTTTTTCCTCCCAGTC, >84_35_1876_F3, CACACCCACATAGGTCGAGGCATCA, >84_35_2001_F3, TCCAAAAACAAAGTCCGTTGCAGTT, >84_36_812_F3, TTGACGCAAAGTTAACAGTGCCACA, >84_36_925_F3
CGGGCCCTGCGTGTGTTACACCTGA, >84_36_1010_F3, GACCTCGGGACCGTGTGACGTTGTG, >84_36_1079_F3, CCACATTTCTCCTGGTTTGATGAAC, >84_36_1465_F3, ACTACCGCTGGAGACGGGGTATCTT, >84_36_160
TGGGGGTGCCCAAAAACTCCTGCTT, >84_36_1764_F3, TTTGGTGACCGGATAGAGATAAATC, >84_36_1993_F3, GCCGCATTAGCCCAGCGCTCTATAT, >84_37_863_F3, TGTATTGAAATGTGTGTAACGTGTT, >84_37_1135_F
CTTCATCTGTGCCCTCTGGTACTTT, >84_37_1891_F3, ACTCGTGAGATGCTAAGAAGCTAGA, >84_38_502_F3, CTGGGTTAACAGAAGGATGCGATAA, >84_38_900_F3, CGCTATGCAATTACCTCCCACAGAG, >84_39_398_F3
ACTCATATTCTAGAATCTGCGCGCC, >84_39_926_F3, CTGAATAGGAGTAAAGTGGTAGATG, >84_39_948_F3, TTGAACACAAAGTGTACACATGTGT, >84_39_1022_F3, ATGAAAGGTGCTCAGAACACATCGG, >84_39_1125_F
TTCCAGTCCGTCCCGTCAATATGCG, >84_39_1367_F3, GAGTCTCGCCCTTCTCTCATATTTT, >84_39_1595_F3, GCTCTGTGTGACACACACACACACA, >84_40_1252_F3, GTGTTGTGGTACCTTCCTTTGGCCT, >84_40_1390_
CTGAGAGCGAAAAGTTAGAAATAGG, >84_40_1449_F3, TCTTATCACCTTTAGCTCCCGAGAG, >84_41_728_F3, AGTCTCCTGCCTTCTCATGAGTTTG, >84_41_889_F3, CATCGCTAAGCTGATAAGGAGTGCG, >84_41_1236_F
CGCTGTTGTTCCTGACTCAGCATGG, >84_41_1317_F3, GGCAGTGGAACGGGGCGACTACCGA, >84_41_1971_F3, GTGGGAAGGAGGCCCCAGAAGTGAA, >84_42_346_F3, GTTCCTCTCCTATAATAGCTCTAAG, >84_42_564
ATGTCGTAGAGCGTGTGCACGCGTG, >84_43_1064_F3, TATCGCTGGCTCCGGTGTGTCAGTG, >84_43_1072_F3, AAGTTTCTCCGAGGAAAGAGCTTAG, >84_43_1241_F3, GGGGACTTGATCCTTCCTGGAGATT, >84_43_154
TGCTTTCCTTTTTTCCGGGGGGCTT, >84_44_926_F3, TAACACACACTCTGTTCACATGGTG, >84_44_1057_F3, TAAATAGAACGTTCGGTCAATGGGA, >84_45_871_F3, CTGTGTACAGTGTGTGTGTGTGTGT, >84_45_877_F3,
TTTCTTCAGGGCAGTGGCACACGTG, >84_45_1590_F3, AGAGACGTGCACACCACACATGTGA, >84_46_770_F3, GGGGTGTAGTGGCACGAGGTACAAC, >84_46_997_F3, GTGCGTAACCGTACAACTCACGACC, >84_46_1102
TGCGGGAATTACGACGAGACTGTCC, >84_46_1124_F3, CCGGATTTACTTCTTTTATGCCACC, >84_46_1187_F3, TATCCTTTGGAAGTCGGAACGCTCT, >84_46_1245_F3, AGGCTTTTGGGTTCCCCTCGCTCTC, >84_46_1713_
TGGGCTGGAAGGGGCGGAACGAGCT, >84_46_1794_F3, ACGTCCTTGGTTTCTAGACATGGGT, >84_46_1834_F3, GCTTCCTTTCTTCTGCTAGTTCTCT, >84_46_1898_F3, TGGTTGGTCTTAACTCTGTGACGCA, >84_46_2008
GGTGTCTTCTCCGACGGAGATAGAT, >84_47_674_F3, GTTGTTCTCTCCAGGCACAGTGGTC, >84_47_857_F3, GGCAGTACAGGAAGAAAGCTCTCCT, >84_47_925_F3, CGCCTTGTGGTTTGTAAGCATCGTC, >84_47_1004_F
CACAGTTGTGTCACACTCACAGACA, >84_47_1167_F3, GGAGACAGTCTTTTTCGAGGCGCGA, >84_47_1228_F3, TCACTTTATCTTGGGGGAAAGAGCT, >84_47_1504_F3, CCCACCCCCCCCCAAAACCGCCCCC, >84_47_202
AGGTGAAATATAGTGCGACATGACT, >84_48_1111_F3, ACTCTCTTCTGACTGGGGAAGGAAG, >84_48_1123_F3, CCGGATCCCAGGACCAAGCACACGG, >84_48_1134_F3, TTTTGTCACAGGGCTGAGTTTCGCA, >84_48_151
TCCATGTAACTCTTGAAGATAATCT, >84_48_1574_F3, GGTCATCTTTTCCTCCCTCTAGGCA, >84_48_1716_F3, AGATTTCGTCTGTCTGCATCCCCGA, >84_49_1375_F3, CATTTTTTCCTTGGGGAAAATCTTC, >84_49_1567_F3
AATGCCACGGGCCCGGTCTTAAAGG, >84_49_1699_F3, AGCGTAAGAACTTGAGAGAGAAGTT, >84_49_1780_F3, TTCCCTTTGCATGAGAGCGTAAAAC, >84_50_1092_F3, TGTTCTTCTCCAATTTTACTTAAAT, >84_50_1146_
TCGCGCCCCCAACGGGCTTATTTAT, >84_50_1186_F3, CTTGGACCAAGTAACAATGACTCGC, >84_50_1674_F3, CCCTTTCAAACATTGGTGTGGGTGT, >84_51_519_F3, GGCAAGTCTCAACAATAGATGAAGA, >84_51_578_F
CCTATGACTATTTGCTCCGGGCTTT, >84_51_851_F3, ATACAGCTACGGAGATCGCGTATCT, >84_51_882_F3, CTTATTCCGTATGTCCGAGGGCCGA, >84_51_1016_F3, ATCTTGACGAGCAGGACTTTACGAG, >84_51_1173_F
CTTCGAAAGTTGTGTTTCTTCCGCG, >84_51_1198_F3, AATTCTTCTTCTTCTTCTTCTTCTT, >84_51_1226_F3, TTGGTGTGTGTGTGTACTGTACATG, >84_51_1301_F3, TGTTGGGTGTTTGACTTCACGTATG, >84_51_1542_F3
GTACTCTGGTACACGGGTGTCTGGT, >84_52_688_F3, GCACAGTGTGCACACGTGTGTGTGT, >84_52_1530_F3, TCCCCCCGTCCCAATCTCACGGCTG, >84_52_1599_F3, CCCACAGTGATTTGCGAAAATCTGA, >84_53_372_
GCCGTGTGACACTGTCGTGTATCTG, >84_53_587_F3, GATGTTGCACTAACGCTCGCGTATC, >84_53_982_F3, CTCTAAGAGCTCCCTACTTTCTCGA, >84_54_254_F3, CGCTCTTTTCTAAACGTACACGTAA, >84_54_610_F3,
TGTCTGGTAGAGAGTATCTATATCT, >84_54_641_F3, CCTCAGAAGCGTCCAGAGAGCCGGA, >84_54_1419_F3, CGACAGAGAATTCTTAAATATCTCT, >84_54_1455_F3, TCACTAGCTGCCCGGAAGTAGAGGC, >84_54_1512_
GCTCGCGGACCCCTCGCTGCGCGCT, >84_54_1744_F3, TGAACTAACACCACACAGTGCACAC, >84_54_1950_F3, CCATCACGCCGAGCGAAGAATTCGA, >84_55_726_F3, TGACACAAACGGAAGAGAAAGAGGG, >84_55_104
TTGTCCTCCTCTGTAGTGCCCCTCT, >84_55_1864_F3, TGCACTAGGTAGTCTTCGCACCCAC, >84_56_595_F3, TTATCCTGTACCTCTCTCGAGCTAA, >84_56_892_F3, TATGCTACCTTTGCACAGCCCAGTC, >84_57_580_F3,
CCGGCGTGGTAACATAGGCGCTCTC, >84_57_1100_F3, GGGACCTGTCTTTCCCTTTGCTAAG, >84_57_1167_F3, GGTGCTGGAGAACTGTGTGTACACA, >84_57_1186_F3, ATTTGCTACATCACAGGTGTACCTG, >84_57_1244
CTCAGGTTTCAGTTTGTTACTCTGT, >84_57_1572_F3, GGGACTCCACTTACTTGATGACTGG, >84_57_1691_F3, TCCTGCGGGGAAACCGGAAATCCGG, >84_58_356_F3, CACTCCCTCCTCGGCGCATTCAGTT, >84_58_841_F
CGTCCTGGCTTCATCGTCGATGATT, >84_58_1531_F3, GGGTCAGGGGTACAGCGAGATATCG, >84_58_1583_F3, GGGGGAGCCATTAACCCTTTAGAAA, >84_59_644_F3, GTGTGTCACACTGTGGTGTGTGTGT, >84_59_707_
TTGGTCTTGTCTGGTAGAACCACGT, >84_59_1296_F3, ACCCAGAGATATCTTAATCGATTAG, >84_60_567_F3, CAGGATGCAACAGAGTGTGTACACA, >84_60_925_F3, TACCTTCTAATCTCTCCCGAGAGGG, >84_60_1094_F3
ACACTTTCACAACATTTCTCCGGAA, >84_60_1722_F3, CTATTTCCTTTCCAAACTTATGGTC, >84_61_778_F3, AATCGCGGTAGTAGTCCTCAGCTCG, >84_61_880_F3, CGTCTCAATGTGGCAGGAAAGTAGA, >84_61_1105_F3
ACCTCGTTGAGGGGTATCGTTGACA, >84_61_1226_F3, CCGAGATGCATACAAACTGTACAGT, >84_61_1308_F3, TTTCTCTTGTGTTGTCCCCATTCTG, >84_61_1410_F3, TTGTGTGTGTGTGTGTGTGTACACA, >84_62_960_F
GACGTGTTTGGACCCGAGTGAGATA, >84_62_1287_F3, GTGCAACGACGAAAGTTATTACCGC, >84_62_1596_F3, TATTCCCCACCTCAGACTGTCACGA, >84_62_1908_F3, ATTGGTCTTCACTAGCAGTGCTGAC, >84_63_869_
CAGAGAGACAGGAGTAGGTGCATAC, >84_63_921_F3, GGTTCTTTGTGTGTGTGTGTGTGTG, >84_63_1221_F3, TCTCCTCCATCTGATGACCCGAGAG, >84_63_1279_F3, CAAGGGCTTGGCAATAAGCATCTGG, >84_63_1484
GGCCATATTGACTCCCTATTTTCGG, >84_64_893_F3, CGCACCTTCCTTCACCCTTGCTCCA, >84_64_1131_F3, AAATCTTCAATCCCAGCTGGCAAAA, >84_64_1316_F3, GTGCCACTGAGATTTCCTGTATTTG, >84_64_2027_F3
TTGGAAGAGGCCTGATCACATGTCC, >84_65_514_F3, TCTCCAGAACGACACTTCAGTCTCT, >84_65_815_F3, AGGTCTGCCAACTATAGCAATGTCC, >84_65_872_F3, CTTGGCTTTTCTTGTTGTGCTCTCC, >84_65_1005_F3,
CCGTTCAATGGTCTCTTCAACTAGT, >84_65_1489_F3, GGATCGGGTTTTGTGCGTCAAAACT, >84_65_1956_F3, GGCATGGTGTGTGCGCCTTTGCTCT, >84_66_1614_F3, TCTACAAGGTAAGGGGGCCCGAAGA, >84_67_527_
CTTACGTGTCAGAGGTAGAACAAGA, >84_67_764_F3, CTTGTACAAACCTGTCCACAGTGGT, >84_67_812_F3, TTGCTTTTGGTTGTGCCGGTACAAG, >84_67_961_F3, CCTACCGCCAAGAGTCCTTGCCATC, >84_67_1523_F3
GCTTCTTTGCGCCTGAAGGATCGCC, >84_67_1595_F3, ACCGTGAAACCCATCCCTTGCGGCT, >84_67_1789_F3, CCTTCTATCAGGTTCTGCTTCTCGG, >84_68_554_F3, GTTACACTGTGTGTGTGTGTGTGTG, >84_68_704_F
TTTACGCAAGGGCATATGTGACACA, >84 68 730 F3, GCCTTTCATTCCGGGTCTCCACGGA, >84 68 820 F3, CGTGTGACTGTGTGTGTGTGCGTGT, >84 68 999 F3, GAAACTGAGGAAGAACAGTGACAAC, >84 68 1202 F

Sequencing transcriptomes
in totoSean Grimmond
November 12th , 2008

Presentation overview

Introduction &Developing
The dilemma with transcriptome
transcriptomics
Sequencing

Defining
transcriptome
Complexity/
dynamics

Surveying
Sequence
content

mRNAmiRNA
networks

Conclusions

1

Presentation overview

Introduction &Developing
The dilemma with transcriptome
transcriptomics
Sequencing

Defining
transcriptome
Complexity/
dynamics

Surveying
Sequence
content

mRNAmiRNA
networks

Conclusions

Deriving biological insightfrom the transcriptome

Genome, transcriptome,
and proteome annotation

Survey gene
expression

Build
networks& candidates

Functional
Validation2

Transcriptome based screeningof Human ES cell surface markers
104

R2

R3
R14

103

FL2 Comp

102
R4

R5

101

100
100

101

102
FL1 Comp

103

104

Laslett et al, BMC Dev Biol. 2007

In situ based profiling of
Urogenital development

Brunskill et al, Developmental Cell, 20083

Deriving biological insightfrom the transcriptome

Survey mRNA
Polysome mRNA
mIR levels

Genome, transcriptome,
and proteome annotation

Build networks,
find candidates

Functional
Validation

Representative Gene Vs
Transcriptional complexity

TSS

ATG

TSS

TSS

ATG

pA

pA

pA

pA

TSS

PASR

miRNA

TASR
AAA

ATG
ATG

AAA

ATG

AAA

ATG

AAA

ATG

AAA

ATG

AAA

AAA

genomic DNA

microRNAs

TSS

spliced introntranscription start site

protein coding regions

pA

polyadenylation signal

ATG

translation start site

non-coding regions

AAA

polyadenylation

4

frameworks
Defining complexCreatingpromoter transcriptionalarchitectures acrossall loci.
against the genome:

Exhaustive surveying of expression space:~30,000 genes, >100,000 transcripts, > 65,000 ORFs
Multiple promoters, Novel layers of control…
Carninci et al, Nat Genet, 2005.

Using shotgun sequencing
to survey transcriptomes?

TSS

ATG

TSS

TSS

ATG

pA

pA

pA

TSS

AAA

ATG
ATG

AAA

ATG

AAA

ATG

AAA

ATG

AAA

ATG

AAA

pA

AAA

5

Presentation overview

The current
dilemma withtranscriptomics

Developing
transcriptome
Sequencing

Defining
transcriptome
Complexity/
dynamics

Surveying
Sequence
content

mRNAmiRNA
networks

Conclusions

Sequencing by ligation

Shendure et al (2005) Science 309:1728

6

SOLiD sequencing

AGNNNXXCGNNNXXTTNNNXXATNNNXX

Sequencing by Supported Oligo Ligation and Detection (SOLiD).
1. Anneal sequencing primer to amplicon.
2. Anneal and ligate first dinucleotide detection probe. From pool of 16.
3. Wash and capture image of slide.
4. Cleave after dinucleotide detector and wash.
5. Repeat cycle 5-7 times (25-35 base read).

SOLiD sequencing

AGNNNXXCGNNNXX

NNNATXXATNNNXX

TTNNN
Sequencing by Supported Oligo Ligation and Detection (SOLiD).
1. Anneal sequencing primer to amplicon.
2. Anneal and ligate first dinucleotide detection probe. From pool of 16.
3. Wash and capture image of slide.
4. Cleave after dinucleotide detector and wash.
5. Repeat cycle 5-7 times (25-35 base read).

7

SOLiD sequencing

PrimerA:
PrimerB:
PrimerC:
PrimerD:
PrimerE:T T 03 04 05 A T 08 09 10 C C 13 14 15 G G 18 19 20 ..
T 02 03 04 C A 07 08 09 G C 12 13 14 T G 17 18 19 A ..
01 02 03 A C 06 07 08 A G 11 12 13 A T 16 17 18 T A ..
01 02 G A 05 06 07 A A 10 11 12 A A 15 16 17 C T 20 ..
01 T G 04 05 06 T A 09 10 11 12 13 14 15 16 G C 19 20 ..

Sequence

T

T

G

A

C

A

T

A

A

G

C

C

A

A

T

G

G

C

T

A

•No homo-polymer problems, no phase problems•Errors are random•Each base is _read_twice_•Independent measures of each base >> raw accuracy (99.5% for single runs)•Consensus accuracy is 99.97% at 10x ( based on human BAC sequencing)
Output = 80-160,000,000 reads = 6.0Gb-18.0 Gb per run (3-9Gb per slide)
Latest output is > 200,000,000 reads per slide at 50mers per run.

SOLiD sequencing

PrimerA:
PrimerB:
PrimerC:
PrimerD:
PrimerE:T T 03 04 05 A T 08 09 10 C C 13 14 15 G G 18 19 20 ..
T 02 03 04 C A 07 08 09 G C 12 13 14 T G 17 18 19 A ..
01 02 03 A C 06 07 08 A G 11 12 13 A T 16 17 18 T A ..
01 02 G A 05 06 07 A A 10 11 12 A A 15 16 17 C T 20 ..
01 T G 04 05 06 T A 09 10 11 12 13 14 15 16 G C 19 20 ..

Sequence

T

T

G

A

C

A

T

A

A

G

C

C

A

A

T

G

G

C

T

A

Scale:•Output = 80-160,000,000 reads = 6.0Gb-18.0 Gb per run (3-9Gb per slide)•Latest output is > 200,000,000 reads per slide at 50mers per run.

8

Poly A transcriptome

nuclearexportnucleus

5’CAP

AAAA 3’cytoplasm

Coding mRNA
5’CAP

AAAAAAAAA
3’mRNA

peptideNon- coding mRNA
5’CAP

AAAA 3’

Shotgun transcriptome libraries:

Cloonan et al, Nature Methods, 5: 613-9 (2008)9Accuracy of mapping at 25-35bp

Unique mapping of short tags
(impact of length & error):

Faulkner et al Genomics 91 (2008) 281–288

10Depth of sequencing required for
SQRL of mRNA

7000

No Refseqs

6000
5000
4000
3000
2000
1000
0

1

10

100

1000

1xe4

1xe5

1xe6

Tag count per Refseq (3Gb sequence)

Biological and technicalreplication of SQRL

11

Presentation overview

The current
dilemma withtranscriptomics

Developing
transcriptome
Sequencing

Defining
transcriptome
Complexity/
dynamics

Surveying
Sequence
content

mRNAmiRNA
networks

Conclusions

Defining transcript
Specific expression

A

AAA

B

AAA

C

AAA

D

AAA

Transcripts defined by Aceview (September 2007 release)protein coding regions

non-coding regions

AAA

polyadenylation

spliced intron

12

“Diagnostic” features

A

AAA

B

AAA

C

AAA

D

AAA

protein coding regions

non-coding regions

AAA

polyadenylation

spliced intron

Defining transcript
Specific expression

TSS

ATG

TSS

ATG

ATG

TSS

pA

pA

pA

ATG

AAA

ATG

AAA
AAA

ATG
ATG

AAA
AAA

ATG

92.6% known transcripts have diagnostic features (covers 99.8% of loci)217127 diagnostic features covering 160156 individual transcripts from 65254 locigenomic DNA

microRNAs

TSS

spliced introntranscription start site

protein coding regions

pA

polyadenylation signal

ATG

translation start site

non-coding regions

AAA

polyadenylation

13

Surveying exon usage

TSS

ATG

TSS

genomic DNA
TSStranscription start site

ATG

translation start site

ATG

TSS

pA

pA

pA

spliced intron

microRNAs
protein coding regions

pA

polyadenylation signal

non-coding regions

AAA

polyadenylation

SQRL is quantitative
correlation with qRT-PCR

14

Key questions in ES cell biology

What are the signals required tomaintain stem cell Pluripotency?

What are the molecular programs
controlling lineage determination?

What role does RNA play in ES cellmaintenance and differentation?
http://www.stemcellresearchfoundation.org/

Using shotgun sequencing
to survey transcriptomes
Starting material: Ribo-depleted polyAand polysome associated RNA frommouse or human ES cells & EBs.
-Sequence ~100,000,000 mappable
reads per library using SOLiD.
-Reads are 25, 30 or 35bp in length-Make 3 libraries per biological state-All tags are mapped to the genome
first and then to a database of all
known exon junctions.

15

SQRL profiling is quantitative (SOLiD Vs Illumina array)

RNAseq is sensitive &quantitative(8,800) Bead array
EB

R = 0.935

RNAseq (13,000)
EB

ES

ES

EB

R = 0.939

EB

ES

ES

Red: >2x up regulated in EB, Green: <2x down regulated in ES, Grey: Marginal detection(<.95 detection score for Illumina or 50tags for SQRL )16

Better sensitivity improves
functional annotations
FGF/ERK/MAPK pathway

40

20

30

15

number of genes

number of genes

Wnt pathway

20

10

0

10

5

0

ES

EB

ES
Illumina

EB

SOLiD

Increased transcriptional complexityof TGF-β signalling in ES

0 mRNA
1 mRNA>1 mRNA

46 nodes in the pathway
25 loci transcriptionally active in ESCs
41 variant mRNAs expressed in ESCs

17

Transcriptome Discovery:in ES cells and EBs

Category

ES

EB

Known exons

74.2%

75.5%

Predicted exons

5.6%

7.8%

Known regions

7.9%

5.6%

Predicted regions

4.8%

3.9%

2.8%

3.2%

5.6%

7.8%

Conserved regions
Other regions

Widespread and dynamic
Repeat expression:

Other>200,000 repeats expressed

EB
ES

SINE.MIR
SINE.ID
SINE.B4
SINE.B2~30,000 elements dynamically
expressed.

SINE.Alu
Simple_repeat
SatelliterRNA
LTR.MaLR
LTR.ERVLSINB2 elements are regulated
in a state specific fashion.

LTR.ERVK
LTR.ERV1
Low_complexity
LINE.RTE
LINE.L2
LINE.L1
LINE.CR1
DNA
transposons

0

2

4

6

>300 Repeat elements display bidirectional expression in the ES cell state18

TEs can act as locus
boundary elements:>300 Repeat elements display bidirectional expression in the ES cell state
Lunyak, V. V., et al., Science (2007).

Repeat element expression
across 50 mouse tissues

19

TE can act as alternate
promoters
 TEs known to provide alternative promoters to
nearby genes (Speek et al, Genomics, 2008).
 CAGE delineated >100,000 TE promoters-<100kb upstream of a RefSeq-same strand as a RefSeq-not overlapping a RefSeq
 >700 confirmed as alt. promoters by ESTs
 Confirmation of novel promoters RT-PCR, cDNA
sequencing and RACE

Viewing exon and junction expression
in a genomic context:

20

The Polysome-associatedtranscriptome:

nuclearexportnucleuscytoplasm 5’CAP

AAAAAAAAA
3’mRNA

peptide5’CAP

AAAA 3’i) Free polysome
Associated RNAs
5’CAPii) Membrane polysome
Associated RNAs
AAAA3’AAAAAAAAA
3’

peptidepeptide5’CAP

AAAAAAA 3’

peptidehES PolyA Vs Polysome
RNA sequencing:Diehn et al., Nature Genetics 25:58-62 (2000)
ECS

log2Ratio(Mem/Free)

Cyto/Nucl

ratio (Mem/Free) vs signal
intensity
Raw signal intensity (Free)

21

Expression Vs translation

Tag count
Total:
1115
Cytosol:0
Membrane: 3

hES PolyA Vs Polysome
RNA sequencing:
CSTE

T
M
C

T
M
C

22

Network analysis of ECS signalling:

Secreted Predictions
Membrane Predictions

Modelling the extracellular spacein ES cells:
Wnt

HH

TGF-Beta

Ephrins

FGF

EGF

Integrin
/Vegf

ECS

MEM

Ingenuity Pathways Analysis ™

Secreted Predictions
Membrane Predictions

23

Transcription output of VEGFR1
In hES:

Presentation overview

The current
dilemma withtranscriptomics

Developing
transcriptome
Sequencing

Defining
transcriptome
Complexity/
dynamics

Surveying
Sequence
content

mRNAmiRNA
networks

Conclusions

24

Screening sequences for substitutions(expressed SNPs, mutations, RNA editing)

SNPs, mutations
All tags

RNA editing

Map to genome

Align, ID and
QC call SNPs

Determine if
SNP is in dbSNP?
ORF, UTR, Syn/Non

Rank SNPs(polyphen,
Canpredict

ACGATATTACACGTACACTCAAGTCGTTCGGAACCTACGATATTACACGTACATTCAAATCGTACGATATTACACGTACATTCAACTCGT
ACGATATTACACGCACATTCAAGTCGTCGATATTACACGTACATTCAAGTCGTT
ATATTTCACGTACATTCAAGTCGTTCG
ATATTAAACGTACATTCAAGTCGTTCGATTACACGTACATTCAAGTCGTTCGGAATTACACGTACATTCACGTCGTTCGGACACGTACATTCAAGTCGTTCGGAACCT-----------------T------------------

Aligned Reads

SNP call

Validation of sequence variations

Dnttip2; T->A; L269Q

Arid3b; G->T; Q->K

Uchl4; T->A; Y->F

25

SNP detection in ES:
Evidence of RNA editing?

ES novel

EB novel

C->A

C->G

ES known

EB known

All SNPs

0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
A->C

A->G

A->T

C->T

G->A

G->C

G->T

T->A

T->C

T->G

Presentation overview

The current
dilemma withtranscriptomics

Developing
transcriptome
Sequencing

Defining
transcriptome
Complexity/
dynamics

Surveying
Sequence
content

mRNAmiRNA
networks

Conclusions

26

miRNA suppression ofmRNA targets

Droshaprocessing

5’

pri-miRNA

nucleus3’

5’cytoplasm

nuclearexport3’ pre-miRNA

5’

5’

3’ Dicerprocessing

3’miRNA

duplex
5’

mRNA

3’

Asymetricalunwinding

5’ AAAAAAAAAAA
AAA 3’
3’

5’

RNA-Induced Silencing Complex(RISC)

miRNA sequencing in
mammalian ESCs & EBs

Sequence ~10,000,000 15-35bp reads using SOLiD
Map against miRBase, then the genome
Cluster miRbase matches and summarise isomiR complexity

27

miRNA sequencing in
mammalian ESCs & EBs

30.0%

25.0%

20.0%

15.0%

10.0%

5.0%

0.0%
15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

Studying miRNA mediatedcontrol of gene networks:

1. Define the miR targets of the polycistronic miRs (PicTar)
2. Determine functional relationship of targets (IPA)
3. Benchmark against similarly size random sets of targets
4.5

Category: Cell Cycle

4-log (significance)

3.5
3
2.5
2
1.5
1
0.5
0

miR-17-5p

randomrandomrandomrandomrandomrandomrandomrandomCloonan et al, Genome Biology, 9: R127 (2008)28

Studying miRNA mediatedcontrolnetworkof gene networks
Interaction
WEE1
CRK

PDGFRA

4.5

PKD2

Category: Cell Cycle

4

GAB1
PKD1

3.5

-lo g (s ig n ific a n c e )IRF1

TP53INP1

NR4A3

3

STAT3

NCOA3

TSG101

2

CCND2

PPARA

FOXO1A
CCNG2

CCND1

PCAF

RB1

1

APP

APBB2

E2F3

0.5
0

EREG

HIF1A

CDKN1A

RBBP7

HAS2

DMTF1

2.5

1.5

KHDRBS1

miR-17-5p random

randomrandomrandomrandomrandomrandomrandomBCL2L11

E2F1

CUL3

RBL2
MYCN

RBL1

E2F5

MAPK9
MAP3K8

KPNA2

TXNIP

Studying miR-290 mediatedcontrol of gene networks:

30

Embryonic development-lo g (sig n ifican ce)

25
20

15
10
5

0

miR-290 random random random random random random random random random
cluster

29

Studying miRNA mediatedcontrolnetworkof gene networks
Interaction
Nodes
Transcription factor

WEE1

Cytokine/growth factor
Nuclear receptor

IRF1
GAB1

TP53INP1
PKD1

Kinase

NR4A3

STAT3

NCOA3

TSG101

EREG

HIF1A

CDKN1A
CCND2

PPARA

FOXO1A
CCNG2

CCND1

PCAF

RBBP7

HAS2

DMTF1

Transporter
EnzymeKHDRBS1

Category: Cell Cycle

PKD2

Plasma membrane protein

Other

CRK

PDGFRA

RB1

APP

APBB2

E2F3

Edges

Indirect interaction(does not require physical contact,or physical contact unknown)

BCL2L11

E2F1

CUL3
Direct interaction(requires physical contact)

RBL2
MYCN

RBL1

E2F5

MAPK9
MAP3K8

KPNA2

TXNIP

Cloonan et al, Genome Biology, 9: R127 (2008)

miRNA expression:
Complexity from IsomiRs

Morin et al, Genome Res. (2008) 18(4):610-21

30

Presentation overview

The current
dilemma withtranscriptomics

Developing
transcriptome
Sequencing

Defining
transcriptome
Complexity/
dynamics

Surveying
Sequence
content

mRNAmiRNA
networks

Conclusions

Summary:
Generate 108-5x108(mRNAs miRNAs, gDNA tags)
Map to Genome &known exon junctions
Genome mappings:•Loci•Transcripts•Exons•Junctions•Diagnostic Seqs
•ORFs
•Domains

DB

Cluster
Novel
Tags

Output:•Locus activity•Exon usage•Splicing events•Promoter usage•Novel expression

Visualisation:•Framework structure•Exon coverage plots•Junction usage

31

Conclusions:

RNAseq is a powerful tool for studying RNA abundance, dynamics andcomplexity with unprecedented sensitivity
Transcriptome discovery is identifying roles for novel expression
Polysome associated RNAseq gives novel insights into those transcriptsthat are actively translated.
SQRL provides a complete view of small RNA complexity andabundance.
It is also now possible to screen the transcriptome for expressed SNPs,
mutations (mis-sense, and indels), allelic specific expression and RNA
editing.

Challenges:-Sequence content (more reads, longer reads, more accuratesequencing).
-Ambiguity of mapping. “Black holes” in the transcriptome.
-Transcript length affect shotgun tag sensitivity.
-Matching to a reference is easy, de novo assembly is hard.
-Diagnostic sequences only infer transcription complexity-Novel combinations of junction usage (ie alt splicing event between exon
1& 3 cannot be conclusively tied to alternate 3’utr usage. )32

SOLiD:
Kevin McKernan
Clarence Lee
Heather Peckham
Stephen McLaughlin
SREK:
Scott Kuersten
Jian Gu
Catalin Barbacioru
IMB array facility:
Tina Maguire
Keerthana Krishna
Stem Cell Research:
Andrew Perkins
Steve Bruce
Andrew Laslett
George Zhou
Repeat analysis:
Piero Carninci
Shintaro Katayama
Valerio Orlando

Nicole Cloonan, Gabe Kolle, [Al Forrest], Geoff
Faulkner, Brooke Gardiner, Darrin Taylor, Allan
Robertson, Shivangi Wani, Graham Bethel.

33