METHODS FOR DE NOVO
DISCOVERY OF RNA EDITING
EVENTS
IRINA SHCHUKINA
ALEXANDER KANAPIN, ANASTASIA SAMSONOVA
DEPARTMENT OF ONCOLOGY
UNIVERSITY OF OXFORD
TYPES OF RNA EDITING
A
C
I (ADAR)
U (APOBEC)
G
A
A
G
G
G
A
A
G
A
A
A
G
A
A
G
G
G
G
G
G
G
A
G
G
A
A
G
AAGGACTCGTATCACGGATACCGTAGC
Global goal: framework
development
This semester: benchmarking
of existing methods
TOOLS AND DATA
▸ REDITools
▸ RED
▸ GIREMI
K-562 cell lines RNA-seq and DNA-seq (ENCODE project)
MCF-7 cell lines RNA-seq (hypoxia and normoxia)
VARSCAN DNA
4,140,928
RED
29,927
K-562: genome +
transcriptome
REDITOOLS
31,896
FRACTION DISTRIBUTION (K-562)
All sites. K−562
3
density
2
1
0
0.25
0.50
Fraction
0.75
1.00
FRACTION DISTRIBUTION (K-562)
All significant sites. K−562
Significant sites. No genome SNPs. K−562
4
2.0
3
density
density
1.5
2
1
1.0
0.5
0
0.0
0.25
0.50
Fraction
0.75
1.00
0.25
0.50
Fraction
0.75
1.00
MCF-7
Normoxia
Hypoxia
(2 biological replicates)
Nuclear RNA
Cytoplasmic RNA
Nuclear RNA
Cytoplasmic RNA
REPLICATES COMPARISON
REDITools
(nucleus normoxia)
RED
(nucleus normoxia)
REDITOOLS AND RED RESULTS
Nucleus
(normoxia)
Cytoplasm
(normoxia)
DATA ANALYSIS
Hypoxia vs normoxia
Cytoplasm vs nucleus
9000
8000
8000
7000
7000
6000
6000
5000
5000
4000
4000
3000
3000
2000
2000
1000
1000
0
0
REDITools
Normoxia
RED
Hypoxia
REDITools
Cytoplasm
RED
Nucleus
RESULTS
▸
▸
▸
▸
Bias towards sites with high fraction (SNPs)
Poor noise removal
Intersection between two methods: 30%
Still some biologically relevant results
Future plans: framework development and analysis of
total RNA-seq of 10 cancer cell lines (2 conditions)
Thank
you!