Analysis of various Chromatin states like Promoter, Enhancer, early gene etc using a HMM model(BDHMM). This HMM model can include the specific trait of a state, "Direction", which makes this HMM special and could help us find interesting discoveries. Here, We have developed a simple computational model to find unstable transcription via Contiguous States combination. :)

1. Chromatin State Dynamics of Fly using
biological(Bi-directional) HMM.
Max Delbruck Centre for Molecular Medicine, Berlin
Indian Institute of Technology Guwahati, India

2. Structure
✓Hidden Markov Model
▪ States and Features
▪ Why there is a need of new Model?
✓Bidrectional Hidden Markov Model (bdHMM)
▪ Comparison with regular Hidden Markov Model
✓Applications of Bidirectional Hidden Markov Model
▪ Given additionally Directionality feature to Chromatin States
▪ Extracting Unstable expression stretches

3. Hidden Markov Model?

4. How chromatin states look:
• HMM clustering of continuous ChIP-seq signal at 10 nucleotide resolution
• Dependence between marks accounted for
Chromatin State Definitions
H3K27ac
H3K4me3
H3K4me2
H3K4me1
1 2 3 4 5 6 7 8 State 1 2 3 4
ChIP Signal

5. Analyzing Bidirectional genes using regular HMM
Strand data lost
H3K27ac
H3K4me3
H3K4me2
H3K4me1
BD- Promoter
Histone
Modf.
BD-Genes
Regions will
look like
←

6. So the order of events in Genome are not random.
They are well mannered & decided.
We know that there is for sure directionality
data in the track. So the question is
How to take advantage of Directionality data?

7. A Bidirectional Hidden Markov Model(bdHMM)
UY
Rsx
Fsx
SX
Annotation of genomics data using bidirectional hidden Markov models unveils variations in Pol II
transcription cycle.( Zacher et al.’15)
Directed State
UnDirected State
F→ Observation Sequence X
R→ Conjugate observation viewed
from opposite Direction

8. Rx Fx
SX
In genome a Directed State will look like:
Negative
Strand
Positive
Strand
Split

9. -HMM State Transition -BDHMM State Transition
Transition Comparison:
Three States:
U → Untranscribed Region
E → 5’ End of gene
L → 3’ End of Gene
Assume:
No Transition Restriction Transition

10. -HMM State Transition -BDHMM State Transition
Non-stranded
Track
U → Untranscribed Region
E → 5’ End of gene
L → 3’ End of Gene
Assume:
No Transition Restriction Transition

11. -HMM State Transition -BDHMM State Transition
Non-stranded
Track
Undirected Output
Directed Output
Strandness
Annotation
U → Untranscribed Region
E → 5’ End of gene
L → 3’ End of Gene
Assume:
No Transition Restriction Transition

12. Datasets we’ll be analyzing:
Enriched at 30-40 downstream TSS and go on. (Pausing)
Datasets Traits to consider in our frame
✓ PEAT -Read at Transcription start site precisely
-Only shows Stable expression
-Narrow Distributed reads
✓ ProCap (cap) -Read at Transcription start site
-Shows both stable & unstable expression
- Widely Distributed reads
✓ Proseq (seq) -
-Shows both stable & unstable expression
-Widely Distributed reads
✓ Histone Modifications Chip-Seq -Reads at histone chemical changes like single methylation,
tri methylation, acetylation etc.

13. h3k4m
e
h3k4m
e
h3k27a
c
h3k27
m
Comaprison: Ordinary-HMM vs bdHMM
State Mapping:
1 U4
2 U1
3 U2
4 U5
5 U3
K4me
1
K4me
3
27ac
27me
3
K4me
1
K4me
3
27ac
27me
3
Heatmap from HMM Heatmap from bdHMM
Similar Biological
States

14. App 1:
Increasing Precision of chromatin States by adding
directionality feature.

15. Using Directed States
+ - NS NS NS NS
✓ Stranded Tracks
Input
✓ Stranded States
✓ Background=0
✓ States= 5
Flipped States
FX
RX

16. Metaplot of States around Bidirectional Genes:
Forward States
Reverse
States
Gene.-
Gene -
Gene.+
Gene+
Forward→ State on
+ve Strand
Reverse → State on
-ve Strand
Midpoint is center of both TSS
X-Axis: Distance from Centre of both TSS
Y-Axis: Count of state occurrence at position X
Promoter
Enhancer
Poised Enhancher
Polycomb A
Polycomb B

17. Regular Hidden Markov Model
−1000 −500 0 500 1000
1
2
3
4
5
X-Axis: Distance from Centre of
both TSS
Y-Axis: Count of state occurrence
at position X
Promoter
Enhancer
Poised Enhancher
Polycomb A
Polycomb B

18. −10
00
−5
00
0 50
0
10
00
1
2
3
4
5
Directionality Feature is being added
Superimposition
Promoter
Enhancer
Poised Enhancher
Polycomb A
Polycomb B

19. Results:
✓ We can give the Strand of a Histone Modification State.
✓ This algorithm describes the genome organization in more accurate sense.
No Post-Analysis.
✓ Similar Metaplots are constructed around various genomic regions and they are
convincing with some interesting results.
Open Problem:
We are not getting Forward State and Reverse State symmetrically in metaplots.

20. App 2:
Extracting Stretches of Unstable expression.

21. Modelling behavior of Unstable RNA Stretches
0-10 11-20 21-30 31-40
PEAT –ve PEAT –ve PEAT –ve PEAT –ve
CAP +ve CAP +ve/CAP –ve CAP +ve/CAP –ve CAP +ve/CAP –ve
SEQ –ve SEQ –ve SEQ –ve /Seq +ve SEQ +ve
Initiation Pausing Pausing Polymerase Binding
Signals
Combinations
5’ 3’
Note: Model is considering ProCap noise into model.
Let’s try to get states like this.
Unstable RNA Transcription SiteTSS
Genome Position
relative to TSS
On +ve Strand:
On –ve Strand:

22. Modelling behavior of Unstable RNA Stretches
0-10 11-20 21-30 31-40
PEAT –ve PEAT –ve PEAT –ve PEAT –ve
CAP +ve CAP +ve/CAP –ve CAP +ve/CAP –ve CAP +ve/CAP –ve
SEQ –ve SEQ –ve SEQ –ve /Seq +ve SEQ +ve
Initiation Pausing Pausing Polymerase Binding
Signals
Combinations
5’ 3’
Note: Model is considering ProCap noise into model.
Let’s try to get states like this.
Unstable RNA Transcription SiteTSS
Genome Position
relative to TSS
On +ve Strand:
On –ve Strand:

23. Required Comb. of States
+ - + - + -
INFO: Stranded Data:
ProSeq, Procap, PEAT
Undirected states
Background= -1
States No=15 (H&T)
Seq+ Seq- Cap+ Cap- PEAT+
PEAT-
U13 U11* U11* U6
*Only one combination is shown

24. U11 U11 U13 U11/15/13 U11/15/13 U6 U11 U11
……….. …..
U1 U1 U11 U11 U12/U10 U8/15/13 U8/15/13 U8....
………..
Stretch on +ve Strand
Stretch on -ve Strand
State Combinations for Unstable RNA
Seq+
Seq-
Cap+
Cap-
PEAT+
PEAT-
States on Genome looks like:

25. Stretches location on Genome
Unannotated regions before genes
In beginning of Introns, Ending of Introns, In Introns
Unstable expression stretches
Annotated genes
Unstable expression stretches
Annotated genes

26. Structure
✓Hidden Markov Model
▪ States and Features
▪ Why there is a need of new Model?
✓Bidrectional Hidden Markov Model (bdHMM)
▪ Comparison with Hidden Markov Model
✓Applications of Bidirectional Hidden Markov Model
▪ Giving additional Directionality feature to Chromatin States.
▪ Extracting Unstable expression stretches.
Open Problem:
We are not getting Forward State and Reverse State symmetrically.

27. Acknowledgements
Mahmoud Ibrahim
Scott A. Lacadie
Uwe Ohler
Philipp Drewe
Sucheta Gokhale
Henritte Miko
Antje Hirsekorn
Hans Wessels
Neelanjan Mukherjee
Rebecca Hunt
Lorenzo Calviello
Dina Hafez
Aslihann Karaback
Sophia Bauch
Alina Munteanu

28. Thank You
☺