1. The document presents an annotation-agnostic method called derfinder to identify differentially expressed regions (DERs) of the genome using RNA-seq data, even with potentially incomplete transcriptome annotation. 2. Applying derfinder to BrainSpan and GTEx data identified intronic DERs that can differentiate tissues independently of nearby exonic regions. 3. Tools were developed for reproducible reporting of DER results, and simulations showed the method maintains power comparable to fully annotated methods.

1. Annotation-agnostic differential
expression analysis
Leonardo Collado-Torres
@fellgernon
#ENAR2016
www.slideshare.net/lcolladotor

2. Mo#va#ng problem: iden#fy and validate
regions of the genome that change expression
when analyzing #ssues with poten#ally
incomplete transcriptome annota#on

3. RNA-seq
reads
Genome
(DNA)
RNA transcripts
(many possible
variants)
Measuring gene expression: RNA-seq
Adapted from @jtleek

4. Genome
(DNA)
Mapped reads
Adapted from @jtleek
Common analysis pipelines:
• Feature coun#ng (gene or exon level)
• Transcript assembly

5. Challenges in counting
hBp://www-huber.embl.de/users/anders/HTSeq/doc/count.html

6. Annotation variation
Frazee et al, Biostatistics, 2014

7. DER finder approach
• Find con#guous base pairs with
Differen#al Expression signal à DE
Regions or DERs
• Find nearest annotated feature
Frazee et al, Biostatistics, 2014

8. coverage
vector
2 6 0 11 6
Genome
(DNA)
Read coverage
Adapted from @jtleek

9. Jaffe et al, Nat. Neuroscience, 2015

10. Finding DERs by expressed-regions

11. CBC: 28
MD: 24 STR: 28 AMY: 31 HIP: 32
DFC: 34
Total N samples: 487
BrainSpan data
Coverage Data from BrainSpan:
hBp://download.allenins#tute.org/brainspan/MRF_BigWig_Gencode_v10/
VFC: 30 MFC: 32 OFC: 30 M1C: 25
S1C: 26 IPC: 33 A1C: 30 STC: 35 ITC: 33
V1C: 33

14. • Data: 3 #ssues (liver, tes#s, heart), 8 samples each
• Align with
• Iden#fy expressed regions with derfinder
– Adjust coverage (40 million)
– Find expressed regions (cutoff 5)
– Discard ERs < 9 bp
GTEx: DERs via expressed-regions

15. Presence of intronic ERs
Can strictly intronic ERs differen#ate #ssues?

16. PCs differentiate tissues

17. Differential intronic ERs | exonic ERs

18. Differential intronic ERs | exonic ERs

19. Differential intronic ERs | exonic ERs

20. Simulation setup
3 replicates:
2 groups, each with 5 samples
~2 million paired-end reads for chr17
1/6 high, 1/6 low in group 2 vs group 1
Annota#on:
complete
missing 20% of transcripts (8.28% exons)
Reference set:
3868 exons that overlap only 1 transcript

21. Simulation results
• Similar power to methods that have complete
annota#on
• Methods with incorrect annota#on lose a lot of power
• Higher empirical FDR/FPR

22. Collado-Torres et al, F1000Research, 2015
regionReport

23. Mo#va#ng problem: iden#fy and validate
regions of the genome that change expression
when analyzing #ssues with poten#ally
incomplete transcriptome annota#on
derfinder permits discovery of novel expressed
regions
1. we iden#fied expressed intronic regions that
differen#ate #ssues independently of the
nearest exonic region
2. we have developed tools for reproducible/
shareable repor#ng

24. Acknowledgements
Hopkins
Jeffrey Leek
Alyssa Frazee
Abhinav Nellore
Chris Wilks
Ben Langmead
LIBD
Andrew Jaffe
Jooheon Shin
Nikolay Ivanov
Amy Deep
Ran Tao
Yankai Jia
Thomas Hyde
Joel Kleinman
Daniel Weinberger
Harvard
Rafael Irizarry
Michael Love
Funding
NIH
LIBD
CONACyT México

25. References + software + code
• Collado-Torres, et al. bioRxiv (2015) doi:10.1101/015370
– http://bioconductor.org/packages/derfinder
– http://lcolladotor.github.io/derSupplement/
• Collado-Torres, et al. F1000Research (2015) doi:10.12688/f1000research.6379.1
- http://www.bioconductor.org/packages/regionReport
- http://lcolladotor.github.io/regionReportSupp/
• Nellore, Collado-Torres, et al. bioRxiv (2015) doi:10.1101/019067
- rail.bio
• Nellore, …, Collado-Torres, et al. bioRxiv (2016) doi:10.1101/038224
- intropolis.rail.bio
• Jaffe, Shin, Collado-Torres, et al. Nat. Neurosci. (2015) doi:10.1038/nn.3898
– https://github.com/lcolladotor/libd_n36
– https://github.com/lcolladotor/enrichedRanges
• Frazee, et al. Biostatistics. (2014) doi:10.1093/biostatistics/kxt053
– https://github.com/leekgroup/derfinder