Use of second and third generation sequencing technology platforms to create a dataset for the discovery of full length transcripts

1. RNA-Sequencing for Full-length Transcript Discovery
Lab Meeting
2/10/14
Anne Deslattes Mays
Mentor: Anton Wellstein, MD, PhD
Special Recognition: Marcel Schmidt, PhD
4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
1

2. 2
Discovery of homing gene fragments
using bone marrow-derived monocytes
Questions:
1. which proteins drive organ homing of hematopoietic
cells ?
2. are there distinct homing proteins for diseased organs
(cancer, wound healing, ischemia, infection) ?
Approaches:
1. use human bone marrow (BM) cDNA library
that displays large proteins from bone
marrow & precursor cells on the phage
surface
2. in vivo selection of homing proteins from
target organs or vessels in animal models
(normal or diseased)
3. this approach selects for gene fragments
coding for homing proteins
full length transcripts
from source material

3. Experimental Objective
We aim to identify the full-length transcripts using 2nd and 3rd generation
sequencing methods for genes whose fragments were discovered through the
phage display experiments nearly a decade ago.
4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
3

4. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
4
MedStar Georgetown University Hospital Cell Processing Unit
Objective: Obtain healthy donor bone marrow bags

5. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
5
Objective: RNA Isolation from Total Bone Marrow
Step 1: Total Bone Marrow Isolation

7. Four Sequencing Experiments
Second Generation Sequencing
4/18/2014 Wellstein/Riegel Laboratory 7

8. 4/18/2014 Wellstein/Riegel Laboratory 8
2nd Generation
Sequencing with
Illumina HiSeq 2000

9. Four Sequencing Experiments
Second Generation Sequencing
1. Total.bm.random – total bone marrow sequenced mate paired
non-strand specific randomly primed ~ 180 million reads
4/18/2014 Wellstein/Riegel Laboratory 9

10. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
10

11. Experiment 1 Results
Genome aligned (tophat (bowtie2)/cufflinks) and De novo assemblies (trinity
(gsnap & blat)) using the read information
Wellstein Genome – created a sub genome with excised regions around the
phage with the hopes of discovering the underlying isoform and gene
structure
Blat/Blasted the short reads against this region and still
• Results were ambiguous information regarding isoforms and gene
structure hits which included phage
• Structure of transcript was not clear
• Strand information regarding reads aligned not clear
Next Steps
• Design another experiment, same cell population, this time targeted
(including original phage primers used often in experiments in both
lineage negative and total bone marrow experiments) and strand specific
• Create a custom long transcript library primed to include full length phage
transcripts
4/18/2014 Wellstein/Riegel Laboratory 11

12. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
12

13. Random RNA-Sequencing vs Strand-specific Targeted RNA-
sequencing
4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
13

14. Targeted RNA-Sequencing Workflow
4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
14
5

15. Initial G12 Gene Model from the Total Bone Marrow
4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
15

16. Design targeted primers and create custom long reaction cDNA
library
4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
16

17. Results and pre-sequencing fragmentation
4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
17

18. Experiment 2 Results
Genome aligned (tophat (bowtie2)/cufflinks) and De novo assemblies (trinity
(gsnap & blat)) using the read information
Wellstein Genome – created a sub genome with excised regions around the phage
with the hopes of discovering the underlying isoform and gene structure
Blat/Blasted the short reads against this region and still
• Results were ambiguous information regarding isoforms and gene structure
hits which included phage
• Strand information known but yet
• Structure of transcript was not clear
• Was it the depth? Was it the cell population? Was it mistargeted regions?
Next Steps
• Design another experiment, now looking at only the lineage negative cell
population where it is known the phage are enriched
• Return to randomly primed reads
• Sequence at a depth similar to the original total bone marrow experiment
(100 million reads)
4/18/2014 Wellstein/Riegel Laboratory 18

19. Four Sequencing Experiments
Second Generation Sequencing
1. Total.bm.random – total bone marrow non-strand specific
randomly primed ~ 180 million reads
2. Total.bm.ss.targeted – total bone marrow strand specific targeted
primed to a depth ~ 20 million reads
3. Lin.neg.ss.random – lineage-negative strand specific randomly
primed ~ 111 million reads
4/18/2014 Wellstein/Riegel Laboratory 19

20. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
20

21. Negative Selection:
Human Progenitor Cell Enrichment Kit with Platelet Depletion
to Isolate the Lineage Negative sub population from total bone marrow

22. Loading and Negative Controls
class gene total.bm.ss lin.neg.ss
loading ACTB 2933 12,643
loading B2M 1500 8473
loading GAPDH 622 44,413
negative CD11B 231 1193
negative CD11C 132 689
negative CD14 21 49
negative CD16a 418 1312
negative CD19 8 36
negative CD2 7 16
negative CD24 142 177
negative CD3EAP 28 243
negative CD56 197 2039
negative CD61 24 480
negative CD66B 207 208
negative glycophorin.A 49 80
negative mir155 2 20
Phage and Positive Controls
class gene total.bm.ss lin.neg.ss
phage _b9 203 2298
phage a1 0 0
phage A12 0 0
phage A5 186 553
phage a8 76 789
phage b3 439 4731
phage b6 68 331
phage B9 171 2354
phage C1 9 139
phage C12 42 10,657
phage C2 147 1757
phage c3 163 453
phage C7 170 1419
phage d5 236 744
phage E12.1 34 459
phage E7 106 300
phage E9 236 2723
phage F6 120 2556
phage G12 292 925
phage H3 64 1060
phage h4 179 658
phage h6 0 0
phage h7 126 1302
positive BST1 32 1616
positive CD133 0 0
positive CD34 9 398
positive THY1 2 4
3 loading controls
13 negative controls
27 Positive controls and phage

23. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
23
Peak read count: 45,701
Peak read count: 52,626
Peak read count: 12,570
Peak read count: 200
ACTB

24. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
24
Negative Control: CD14 (should be highest in Total Bone Marrow)
Peak read count: 109
Peak read count: 6318
Peak read count: 48
Peak read count: 21

25. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
25
Negative Control: CD34 (should be highest in Lineage Negative)
Peak read count: 169
Peak read count: 43
Peak read count: 386
Peak read count: 10

26. What’s Wrong With Illumina Reads
Uniformity of Read Coverage*
• An aligned read can be represented as an integer point in R2 as follows:
The ‘t-coordinate’ corresponding to the read is its left-end point while the
‘l-coordinate’ is the length of the fragment. In Evans et al. (2010), it is
shown that for any choice of fragment length distribution, the col- lection
of points f(t, l)} from a sequencing experiment forms a two-dimensional
Poisson process. This principle guides our further analysis of these points
f(t, l)}, as we test for uniformity in both the t and l coordinates. The output
of ReadSpy is a list of test statistics and P-values for each transcript. A
statistically significant (low) P-value means we reject the fact that the
dataset is uniform on that transcript. Thus, a higher P-value corresponds
to a set of reads sampled uniformly, which is desired. In the next two
sections, we describe the statistical test applied a each transcript. The test
is formulated in terms of the genomic segment [a, b].
*Hower, Valerie, Richard Starfield, Adam Roberts, and Lior Pachter. "Quantifying uniformity of mapped reads." Bioinformatics
28, no. 20 (2012): 2680-2682.
4/18/2014 Wellstein/Riegel Laboratory 26

27. Lior Pachter’s ReadSpy Results
Total BM Targeted
Strand Specific (20 million reads)
target_id length df
pair_counts
_0 test_stat_0 p_value_0
chr19 49129131 19 226 3948.34 0.00E+00
chr4 191038775 19 227 1760.40 0.00E+00
chr11 135006716 19 304 2811.79 0.00E+00
chr2 243199471 19 361 6859.00 0.00E+00
chr16 90354953 38 402 7638.00 0.00E+00
chr9 141354337 38 436 2754.92 0.00E+00
chr12 133851995 57 797 15143.00 0.00E+00
chr15 102531492 76 841 15979.00 0.00E+00
chr1 249250866 247 2739 20184.43 0.00E+00
chr7 159138908 285 3325 54980.68 0.00E+00
Lineage Negative Strand Specific
Random (110 million reads)
target_id length df
pair_counts
_0 test_stat_0 p_value_0
chrY 59373664 19 224 4256.00 0.00E+00
chr21 48130091 19 284 2951.63 0.00E+00
chr19 49129131 57 663 10583.74 0.00E+00
chr8 146364218 57 751 5478.61 0.00E+00
chr10 135534897 76 902 8655.73 0.00E+00
chr3 198022577 76 957 12936.24 0.00E+00
chr16 90354953 133 1439 27341.00 0.00E+00
chr11 135006716 190 2067 23431.41 0.00E+00
chr2 243199471 190 2260 42940.00 0.00E+00
chr4 191038775 285 3236 40639.91 0.00E+00
chr9 141354337 304 3423 23574.66 0.00E+00
chr15 102531492 380 5735 108965.00 0.00E+00
chr1 249250866 912 10322 97596.23 0.00E+00
chr7 159138908 2394 29726 504209.24 0.00E+00
chr12 133851995 5605 84272 1601168.00 0.00E+00
Our reads all have low p-values
indicating the non-uniform
nature of their read coverage

28. Experiment 3 Results
Genome aligned (tophat (bowtie2)/cufflinks) and De novo assemblies (trinity (gsnap & blat)) using the
read information
Wellstein Genome – created a sub genome with excised regions around the phage with the hopes of
discovering the underlying isoform and gene structure
Blat/Blasted the short reads against this region and still
• Results were ambiguous information regarding isoforms and gene structure hits which included
phage
• Strand information known but yet
• Enrichment in population is evident
• Unambiguous Structure of phage transcripts still not clear
• Finding known genes can be done, even de novo assembly of novel transcripts is done on a regular
basis
• But with these phage, a fragment is known -- how do we find the full length structure of this
phage?
• What if we had the phage transcripts in the targeted full length library, but it was lost in the
fragmentation? Is there a way to do sequencing without fragmentation?
Next Steps
• Use new 3rd generation technology to do full length transcript sequencing without fragmentation
4/18/2014 Wellstein/Riegel Laboratory 28

29. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
29
Source: Iso-seq webinar by Liz Tseng, Pacific Biosystems
https://github.com/PacificBiosciences/cDNA_primer/wiki/Understanding-PacBio-
transcriptome-data

30. Four Sequencing Experiments
Second Generation Sequencing
1. Total.bm.random – total bone marrow sequenced non-strand
specific randomly primed ~ 180 million reads
2. Total.bm.ss.targeted – total bone marrow sequenced strand
specific targeted primed to a depth ~ 20 million reads
3. Lin.neg.ss.random – lin- sequenced strand specific randomly
primed ~ 111 million reads
Third Generation Sequencing
4. Lin.neg Pac Bio Long reads –
6 million CCS Filtered SubReads ~ 277,000 readsOfInserts
4/18/2014 Wellstein/Riegel Laboratory 30

31. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
31Source: http://www.pacificbiosciences.com/products/smrt-technology/

32. 4/18/2014 Wellstein/Riegel Laboratory 32
Source:
https://github.com/PacificBiosciences/cDNA_primer/wiki/Understanding-
PacBio-transcriptome-data#wiki-roiexplained

33. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
33
Source:
https://github.com/PacificBiosciences/cDNA_primer/wiki/Understanding-
PacBio-transcriptome-data#wiki-roiexplained

34. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
34
Source: Bobby Sebra – smrt
portal analysis results

35. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
35
Peak read count: 45,701
Peak read count: 52,626
Peak read count: 12,570
Peak read count: 10
ACTB

36. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
36
Negative Control: CD14 (should be highest in Total Bone Marrow)
Peak read count: 109
Peak read count: 6318
Peak read count: 48
Peak read count: 21

37. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
37
Negative Control: CD34 (should be highest in Lineage Negative)
Peak read count: 169
Peak read count: 43
Peak read count: 386
Peak read count: 10

38. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
38
Phage: B9 – only the phage (953 bp)
Peak read count: 10
Peak read count: 10
Peak read count: 10
Peak read count: 10

39. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
39
Peak read count: 10
Peak read count: 16
Peak read count: 10
Peak read count: 10
Phage: B9 10x larger region (~9kb) centered on phage evidence

40. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
40
2/6/2014 Reports for Job readsofinsert
http://ec2-54-197-149-12.compute-1.amazonaws.com:8080/smrtportal/View-Data/Report/16437?name=readsofinsert&media=all&reportKey=Reads-Of-Insert-R… 1/1
Read Length Of Insert Read Quality Of Insert
Number Of Passes
Reports for Job readsofinsert
Reads Of Insert
Movie
Reads Of
Insert
Read Bases
Of Insert
Mean Read Length
Of Insert
Read Accuracy
Of Insert
Mean Number Of
Passes
m131214_160008_42177R_c100597152550000001823102305221422_s1_p0 47,762 61,257,390 1,282 97.96% 11.01
m131212_234151_42177R_c100597412550000001823102305221473_s1_p0 23,360 33,092,110 1,416 98.39% 11.65
m131214_092100_42177R_c100597152550000001823102305221420_s1_p0 36,623 59,671,472 1,629 98.41% 10.78
m131214_124034_42177R_c100597152550000001823102305221421_s1_p0 49,710 63,809,739 1,283 98.04% 11.26
m131213_232025_42177R_c100597412550000001823102305221475_s1_p0 30,720 37,357,905 1,216 97.49% 10.75
m131213_030106_42177R_c100597412550000001823102305221474_s1_p0 24,284 34,943,462 1,438 98.49% 11.85
m131214_060132_42177R_c100597412550000001823102305221477_s1_p0 32,492 39,813,943 1,225 97.49% 10.54
m131214_023937_42177R_c100597412550000001823102305221476_s1_p0 32,210 39,536,384 1,227 97.57% 10.74
Generated by SMRT® Portal. Thu Feb 06 13:30:44 UTC 2014
For Research Use Only. Not for use in diagnostic procedures.
Source: self-install smrt portal – reads of insert

41. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
41
87%
11%
2%
Transcript Size Distribution
1 to 2k 2 to 3k over 3k

42. Summary of reads.
4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
42
------ 5' primer seen summary ----
Per subread: 258835/277161 (93.4%)
Per ZMW: 258835/277161 (93.4%)
Per ZMW first-pass: 258835/277161 (93.4%)
------ 3' primer seen summary ----
Per subread: 1361/277161 (0.5%)
Per ZMW: 1361/277161 (0.5%)
Per ZMW first-pass: 1361/277161 (0.5%)
------ 5'&3' primer seen summary ----
Per subread: 1341/277161 (0.5%)
Per ZMW: 1341/277161 (0.5%)
Per ZMW first-pass: 1341/277161 (0.5%)
------ 5'&3'&polyA primer seen summary ----
Per subread: 18/277161 (0.0%)
Per ZMW: 18/277161 (0.0%)
Per ZMW first-pass: 18/277161 (0.0%)
------ Primer Match breakdown ----
F0/R0: 258855 (100.0%) Source: output of summarize_results.py (Liz Tseng)

43. But this is not good – it turns out that the primers were incorrectly
chosen and the best way to find the primers used is to do as follows:
4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
43
>cat reads_of_insert.fasta | grep -A1 "AAAAAAAAAAAAAAAAA" | more
GGCTTAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGTACTCTGCGTTGATACCACTGCTT
--
AACATTGAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGTAACTCTGCGTTGATACCACTGCTT
--
TGTTTTATAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGTACTCTGCGTTGATACCACTGCTT
--
TTACAATTTAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGTACTCTGCGTTGATACCACTGCTT
--
GAGCCCTTACCGAAAAAAAAAAAAAAAAAAAAAAAAAAGTACTCTGCGTTGATACCACTGCTT
--
GTGGTGATTGTTTACTAAAAAAAAAAAAAAAAAAAAAAGTACTCTGCGTTGATACCACTGCTT
--
GACAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGTACTCTGCGTTGATACCACTGCTT
--
TTTCCCGCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGTACTCTGCGTTGATACCACTGCTT
--
CTTACTTACGTAAAAAAAAAAAAAAAAAAAAAAAAAAGTACTCTGCGTTGATACCACTGCTT
--
GCCCCATCTCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGTACTCTGCGTTGATACCACTGCTT
>cat reads_of_insert.fasta | grep -A1 "TTTTTTTTTTTT" | more
AAGCAGTGGTATCAACGCAGAGTACTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTATTTGGCTTGAT
--
AAGCAGTTGGTATCAACGCAGAGTACTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGTTTTGATTTCCAT
--
AAGCAGTGGTATCAACGCAGAGTACTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTACTTGGGATCTTT
--
AAGCAGTGGTATCAACGCAGAGTACTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTATTTTTTTTTTTTTT
--
AAGCAGTGGTATCAACGCAGAGTACTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTACCCATCAGCG
--
AAGCAGTGGTATCAACGCAGAGTACTTTTTTTTTTTTTTTTTTTTTTTTTTGGTATTTGTTTGTTTCTG
--
AAGCAGTGGTATCAACGCAGAGTACTTTTTTTTTTTTTTTTTTTTTTTTTTTATTTTTTTTTTTTTTTTT
--
AAGCAGTGGTATCAACGCAGAGTACTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTGACATAAACAC
--
AAGCAGTGGTATCAACGCAGAGTACTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTACTAAGCATATT
T
Now my primers are:
>F0
AAGCAGTGGTATCAACGCAGAGTAC
>R0
GTAACTCTGCGTTGATACCACTGCTT

44. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
44
------ 5' primer seen summary ----
Per subread: 256672/277161 (92.6%)
Per ZMW: 256672/277161 (92.6%)
Per ZMW first-pass: 256672/277161 (92.6%)
------ 3' primer seen summary ----
Per subread: 208877/277161 (75.4%)
Per ZMW: 208877/277161 (75.4%)
Per ZMW first-pass: 208877/277161 (75.4%)
------ 5'&3' primer seen summary ----
Per subread: 207111/277161 (74.7%)
Per ZMW: 207111/277161 (74.7%)
Per ZMW first-pass: 207111/277161 (74.7%)
------ 5'&3'&polyA primer seen summary ----
Per subread: 100863/277161 (36.4%)
Per ZMW: 100863/277161 (36.4%)
Per ZMW first-pass: 100863/277161 (36.4%)
------ Primer Match breakdown ----
F0/R0: 258438 (100.0%)
Source: output of summarize_results.py (Liz Tseng)

45. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
45
Negative Control: CD14 (should be highest in Total Bone Marrow)
Peak read count: 109
Peak read count: 6318
Peak read count: 48
Peak read count: 21

46. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
46
Negative Control: CD34 (should be highest in Lineage Negative)
Peak read count: 169
Peak read count: 43
Peak read count: 386
Peak read count: 10

47. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
47
Phage: B9 – only the phage (953 bp)
Peak read count: 10
Peak read count: 10
Peak read count: 10
Peak read count: 10

48. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
48
Peak read count: 10
Peak read count: 16
Peak read count: 10
Peak read count: 10
Phage: B9 10x larger region (~9kb) centered on phage evidence

49. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
49
Scale
chr11:
MOB2
CTSD
Indiv. Seq. Matches
Sequences
SNPs
Genes
Human mRNAs
Spliced ESTs
DNase Clusters
Txn Factor ChIP
Rhesus
Mouse
Dog
Elephant
Chicken
X_tropicalis
Zebrafish
Lamprey
Common SNPs(138)
RepeatMasker
200 bases hg19
1,774,050 1,774,100 1,774,150 1,774,200 1,774,250 1,774,300 1,774,350 1,774,400 1,774,450
Your Sequence from Blat Search
UCSC Genes (RefSeq, GenBank, CCDS, Rfam, tRNAs & Comparative Genomics)
RefSeq Genes
Retroposed Genes V5, Including Pseudogenes
Publications: Sequences in scientific articles
Human mRNAs from GenBank
Human ESTs That Have Been Spliced
H3K27Ac Mark (Often Found Near Active Regulatory Elements) on 7 cell lines from ENCODE
Digital DNaseI Hypersensitivity Clusters in 125 cell types from ENCODE
Transcription Factor ChIP-seq from ENCODE
100 vertebrates Basewise Conservation by PhyloP
Multiz Alignments of 100 Vertebrates
Simple Nucleotide Polymorphisms (dbSNP 138) Found in >= 1% of Samples
Repeating Elements by RepeatMasker
01823102305221476_s1_p0/142269/25_1056_CCS
001823102305221475_s1_p0/23219/25_2124_CCS
14-10
01823102305221420_s1_p0/101093/25_2057_CCS
001823102305221420_s1_p0/43933/25_2151_CCS
01823102305221474_s1_p0/126784/25_2052_CCS
001823102305221474_s1_p0/38774/25_2111_CCS
001823102305221473_s1_p0/61096/26_2148_CCS
001823102305221420_s1_p0/90213/25_2018_CCS
001823102305221420_s1_p0/70860/25_1785_CCS
001823102305221420_s1_p0/46857/25_2050_CCS
01823102305221474_s1_p0/129700/25_2069_CCS
001823102305221473_s1_p0/56996/25_2088_CCS
01823102305221421_s1_p0/102623/25_2092_CCS
0001823102305221477_s1_p0/3072/2126_65_CCS
001823102305221476_s1_p0/26060/25_2036_CCS
0001823102305221476_s1_p0/1057/25_2034_CCS
0001823102305221474_s1_p0/5669/25_2058_CCS
01823102305221476_s1_p0/118762/25_1890_CCS
001823102305221422_s1_p0/82049/25_2039_CCS
MOB2
CTSD
Layered H3K27Ac
100 _
0 _
100 Vert. Cons
4.88 _
-4.5 _
0 -
Phage 14-10: 100% identity and alignment to 19 full length read of inserts

50. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
50
Scale
chr11:
MOB2
IFITM10
CTSD
Indiv. Seq. Matches
Sequences
SNPs
Genes
Human mRNAs
Spliced ESTs
DNase Clusters
Txn Factor ChIP
Rhesus
Mouse
Dog
Elephant
Chicken
X_tropicalis
Zebrafish
Lamprey
Common SNPs(138)
RepeatMasker
5 kb hg19
1,775,000 1,780,000 1,785,000
Your Sequence from Blat Search
UCSC Genes (RefSeq, GenBank, CCDS, Rfam, tRNAs & Comparative Genomics)
RefSeq Genes
Retroposed Genes V5, Including Pseudogenes
Publications: Sequences in scientific articles
Human mRNAs from GenBank
Human ESTs That Have Been Spliced
H3K27Ac Mark (Often Found Near Active Regulatory Elements) on 7 cell lines from ENCODE
Digital DNaseI Hypersensitivity Clusters in 125 cell types from ENCODE
Transcription Factor ChIP-seq from ENCODE
100 vertebrates Basewise Conservation by PhyloP
Multiz Alignments of 100 Vertebrates
Simple Nucleotide Polymorphisms (dbSNP 138) Found in >= 1% of Samples
Repeating Elements by RepeatMasker
01823102305221476_s1_p0/142269/25_1056_CCS
001823102305221475_s1_p0/23219/25_2124_CCS
14-10
01823102305221420_s1_p0/101093/25_2057_CCS
001823102305221420_s1_p0/43933/25_2151_CCS
01823102305221474_s1_p0/126784/25_2052_CCS
001823102305221474_s1_p0/38774/25_2111_CCS
001823102305221473_s1_p0/61096/26_2148_CCS
001823102305221420_s1_p0/90213/25_2018_CCS
001823102305221420_s1_p0/70860/25_1785_CCS
001823102305221420_s1_p0/46857/25_2050_CCS
01823102305221474_s1_p0/129700/25_2069_CCS
001823102305221473_s1_p0/56996/25_2088_CCS
01823102305221421_s1_p0/102623/25_2092_CCS
0001823102305221477_s1_p0/3072/2126_65_CCS
001823102305221476_s1_p0/26060/25_2036_CCS
0001823102305221476_s1_p0/1057/25_2034_CCS
0001823102305221474_s1_p0/5669/25_2058_CCS
01823102305221476_s1_p0/118762/25_1890_CCS
001823102305221422_s1_p0/82049/25_2039_CCS
MOB2
IFITM10 CTSD
Layered H3K27Ac
100 _
0 _
100 Vert. Cons
4.88 _
-4.5 _
0 -
Phage 14-10: 100% aligned to CTSD, 2 possibly 3 splice variants in lineage negative cell
population – structure fully resolved

51. Conclusions:
• Full Length Transcript discovery is achieved with Pacific Biosystems RS
sequencer, using size selection in library preparation prior to sequencing
and Reads Of Insert algorithm
• Even before the release of the ReadsOfInsert approach, the subreads that
are available as a result of the sequencing still had the ability to tell you
the structure of the complete transcript.
• With an error rate of 15%, seemingly daunting, the random nature of the
error and the length of the read provided the complete structure in a way
that no short read second generation sequence could.
• When one is searching for the complete structure, perfection in the parts
is of no consequence
• NO ASSEMBLY is REQUIRED
4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
51

52. Next Steps:
1. Compete the reads of insert approach with 75% accuracy and minimum 1
pass
2. Identify additional full length structure (if possible with the sample reads)
3. Write up the results
4. (next paper) If no additional phage found, sequence an enriched
population with confirmed phage evidence at full length with more
another pacific bio sequencing
5. Use illumina reads to correct for errors and recover more reads
6. Use greater pac bio sequencing depth
4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
52

53. 4/18/2014 Wellstein/Riegel Laboratory 53
Acknowledgements
Dr. Anton Wellstein
Dr. Anna Riegel
Dr. Elena Tassi
Dr. Marcel Schmidt
The entire lab: Elena, Virginie, Ghada, Ivana, Eveline, Khalid, Khaled, Eric, Nitya, the entire
Wellstein/Riegel laboratory
My Committee
Dr. Yuri Gusev
Dr. Anatoly Dritschilo
Dr. Michael Johnson
Dr. Christopher Loffredo
Dr. Habtom Ressom
Dr. Terry Ryan (external committee member)
Robert Sebra, Mt. Sinai PacBio Sequencing
Liz, Tseng, Pacific Biosystems
Eric Schadt, Mt. Sinai PacBio Sequencing
Brian Haas, Author Trinity Suite
`

54. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
54
CD11: New Evidence of an Exon From all Samples, confirmed by PacBio
Peak read count: 16
Peak read count: 1925
Peak read count: 639
Peak read count: 121

55. 4/18/2014
Wellstein/Riegel Laboratory, Lombardi
Cancer Center, Washington DC 20007
55
PASA assembly (Trinity Pipeline) Denovo + Genome Guided
Evidence of a new exon – not found in annotation
for CD11