Comparison of array detected transcription map with

GENCODE/HAVANA annotations in ENCODE regions

AFFX Transcriptome Group

Computation Molecular BiologyS. Bekiranov P. KapranovS. Brubaker I. BellJ. Cheng J. DrenkowS. Ghosh D. Kampa-Bailey G. Helt J. LongG. Madhavan J. ManakS. Patel V. Sementchenko H. TammanaA. Piccolboni

Support:NCI Contract (21XS019C Phases I- III) 2001-2006NHGRI ENCODE GrantAFFYMETRIX

AcknowledgementsNCIHarvard Medical School

K. StruhlH. HirschH. H. NgE. Sekinger

Broad Institute

B. BernsteinM. KamalK. Lindblad-TohD. J. HuebertS. McMahonE. K. KarlssonE. J. Kulbokas IIIS. L. SchreiberE. S. Lander

Transcription Map & Modification Site Transcription Map & Modification Site Generation…Generation…II

1. Median Scaling: Scale all features on chip such that chip median = M

2. Quantile Normalization(QN): QN Feature intensities within replicates only.QN Treatment and Control separately.

3. Probe Mapping to Genome:Map PM,MM pairs to genome via exact 25-mer alignment of PM.

4. Wilcoxon Signed Rank Test:• Perform test on probe-pair signal S = log2(PM-

MM)• Apply a sliding window to estimate intensity of

each probe pair as a pseudo-median of all probes in the window.

• A Sliding window makes use of neighboring probes; this reduces false positive rate and increases sensitivity.

• Window size varies w/ experiment: RNA~50bp, IP~250bp

5. Map and Site Generation:RNA

• Join probes w/ intensity > 5%FPR & maxgap, minrun to generate transcribed fragments

Chromatin IP• Generate Hodges Lehman Estimator to estimate

expression level :logDiff = log2(min(PM-MM)T,1) – log2(PM-MM)C,1)

• Generate p-Value estimate per probeJoin probes w/ p-value 10-5 & maxgap, minrun to generate modification/transcription factor binding sites

CEL fileCEL

fileCEL file

Compute median (M)

of all chip medians(if multiple arrays in a set)

Median Scaling

Quantile Normalization

Probe Mapping to Genome

Wilcoxon Signed Rank Test

RNA or IP

RNA: Transfrag Generation

Chromation IP: Site Generation

Filtration of 10 Chromosome Data(Cheng, J., et al. Science Express; March 24, 2005)( see UCSD Browser for 8 cell line data see Version 33)

• Low Complexity Repeats• Processed Pseudogenes• BLAT hits more than itself

(lose some members of gene families)

• Use of all filters this reduces the transfrag by ~20% of transfrags, ~30% of which are pseudogenes. With BLAT data reduction is 14%

RACE Model

(Need isothermal RT for unannotated transfrags)

RACE Analysis of Coding Gene

DeGeorge Critical Region 14 gene

Un-annotated transfrags of PISD are part of at least 9 different, yet overlapping sense-antisense transcripts

Sense Strand

Anti-sense strand

Region

Total 5' and 3' + 5' or 3' successful RACE

Total successful 5' or 3' RACE

Percent total success considering total 256

Intergen 178/25 78 70%

Intronic 213/256 48 83%

Exonic 243/256 13 95%

RACE Regions Validated for 768 Loci

Data sets analyzed

• Part 1 : a) Analysis done on v34 of the human genome. Total number of Encode regions analyzed = 12 ( region Enm006 ignored for this analysis since no annotations are available for v34).

b) Set of Known/validated exons

c) Set of predicted exons (from multiple gene predictions)

d) Array detected transcript maps from HL-60 cell lines at 4 time points after RA stimulation. (i.e one cell line at 4 biological states)

• Part 2 : a) Analysis done on v35 of the human genome. Total number of Encode regions analyzed = 44

b) Set of Known/validated exons.

c) Set of Vega putative exons.

d) Set of predicted exons outside sets b & c (from multiple gene predictions).

d) Array detected transcript maps from HL-60 cell lines at 4 time points after RA stimulation.

Genomic sequence

35 bp avg. distance

Repeats (RepeatMasker)

Coverage of interogated Regions using algorithms usedTo call Transfrags

Probes

Exon 1 < 100% Covered

Exon 2 is 100% Covered

Annotation (e.g. Vega)

Analyses done only within interrogated regions

How Comparisons are carried out using arrays, Annotations and predicted regions

Predicted exons

Probes

Exon 2

Annotation

Genomic sequence

Transfrags after minrun/maxgap parameters

Positive probes

X

Predicted exons

Coverage of Annotation by array detected transfrags from HL60 cell line in 13 ENCODE

regions

Analysis results of 12/13 ENCODE Regions

Total Number of exons

Interrogated

Number of exons detected by array generated transfrags

( overlap by at least 1 bp)

Number of exons detected by array generated transfrags

( > 75% of exon bp overlapped by a transfrag)

1852

(Known/Validated)

1068 [ 57.7%] (74% avg. bp coverage)

700 (37.7%)

1181

(Predicted)

360 [30.5%] (69.2% avg.bp coverage)

175 (14.8%)

Size distribution of annotated/validated exons detected ENCODE array transfrags

0

50

100

150

200

250

300

350

400

450

1 3 5 7 9 11 13 15 17 19 21 23 25

Exon size (40 bp bins)

# o

f exo

ns

100 pct

< 100 pct

< 75 pct

< 50 pct

<25 pct

0 pct

• Mode size of annotated exons is ~120bp• Detection of exons is not dependent upon size (bp) of the exon (i.e. small exons are not biased against)• If an exon is detected by transfrag, 65% of these are covered at >75%

Size distribution of predicted exons detected by ENCODE array transfrags

050

100150200250

1 3 5 7 9 11 13 15 17 19 21 23 25

Exon size (40 bp bins)

# of

exo

ns

100 pct

< 100 pct

< 75 pct

< 50 pct

< 25 pct

0 pct

• Mode size of predicted exons is ~120bp• Approximately 30.5 % of predicted exons are covered (i.e. at least 1bp coverage) by transfrags. •If an exon is detected by transfrag, 48.6% of these are covered at >75%

Coverage of Annotation by array Detected transfrags from HL60 cell line in all 44 Encode

regions

Analysis results of 44 ENCODE regionsTotal number of exons

interrogatedNumber of exons detected by array generated transfrags

( overlap by at least 1 bp)

Number of exons detected by array generated transfrags

( > 75% of exon bp overlapped by a transfrag)

6467

(Known/Validated)

3487 [ 53.9%] (70% avg.

bp coverage)

2142 (33.1%)

4455

(Predicted)

809 [18.2%] ( 62.23% avg. bp coverage)

361 (8.1%)

185

(Vega Putative)

39 [ 21.1%] (35.71 % avg. bp coverage)

3 (1.6%)

Size distribution of known/validated exons detected by ENCODE array transfrags

0

200

400

600

800

1000

1200

1400

1 3 5 7 9 11 13 15 17 19 21 23 25

Exon sixe (40 bp bin)

# o

f exo

ns

100 pct

< 100 pct

<75 pct

< 50 pct

< 25 pct

0 pct

• Mode size of annotated exons is ~120bp• Detection of exons is not dependent upon size (bp) of the exon (i.e. small exons are not biased against)• If an exon is detected by transfrag, 61.4% of these are covered at >75%

Size distribution of predicted exons detected by ENCODE array transfrags

0

200

400

600

800

1000

1200

1400

1600

1 3 5 7 9 11 13 15 17 19 21 23 25

Exon size ( 40 bp bins)

# o

f eo

ns

100 pct

< 100 pct

< 75 pct

< 50 pct

< 25 pct

0 pct

• Mode size of predicted exons is ~80bp• Approximately, 18.2% of predicted exons are detected by transfrags ( ie. by at least 1 bp)•If an exon is detected by transfrag, 44.6% of these are covered at >75%

Important Caveats To Recall InPondering the Prediction vs Array

Results • Only one cell line used in this evaluation.• We have set very conservative thresholds for transfrag prediction. Other thresholds can be used• Strand information not deducible from transfrag map. TUFs (transcripts of unknown function) are collection of transfrags shown to be on the same molecule by RACE-RT/PCR-cloning/sequencing.• Array interrogation resolution is 20bp on average for non-repeat portion of the genome and probes are 25mers. Thus, the boundaries of transfrags are not as precise as arrays with 5bp interrogation resolution and some small exons will not not be interrogated or detected• Have not included other functional features (e.g.TF binding) which would provide additional confidence to transfrag data. These will be added under ENCODE project.

Conclusions

• Array based method detects ~53.9% of known/validated exons.

• Similarly, array based method provides evidence for ~18.2% of predicted exons. These detected exons should be analyzed further to improve the annotation.

• A combination of array based RNA map generation, followed by RACE experiments can significantly improve the rate of validation of gene predictions.

• Transfrags that map outside validated and predicted exons can be used to improve gene prediction programs and can form the basis for further experiments.