1. Identifying Conserved microRNAs in a Large Dataset of Wheat Small RNAs
Md Safiur Rahman Mahdi
Advisor: Dr. Michael Domaratzki
Department of Computer Science
July 27, 2015
Dear audience, good afternoon. Welcome to the presentation of my thesis defence. My thesis title is “Identifying Conserved microRNAs in a Large Dataset of Wheat Small RNAs”. My advisor is Dr. Michael Domaratzki from Department of Computer Science.

2. Key concepts
MicroRNA (miRNA)
Small RNA
Differential expression

3. From cell to DNA
Cells are the basic building blocks of all living things.
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4. From DNA to Protein Transcription Translation
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Central dogma of molecular biology
DNA contains gene  RNA  Proteins

5. Noncoding RNA (ncRNA)
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6. miRNA : how does it work?
Part of gene contains small non-coding rna called mirna which contains only 22 neucleotides in length
They don’t make proteins, acts completely in a different way….find the complementary portion in mRNA and binds together that blocks translation form mRNA to protein
They are the fundamental regulator of gene expression
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7. miRNA  1000 nt  70-600 nt Mature/star miRNA 17-24 nt 5’ 3’
There should be 0-4 nt difference between the miRNA mature and star
miRNA [Kozomara et al., 2014]

8. What is differential expression?
Responds to signals / triggers
Changes in expression level
Between two sample groups
Control Vs. treatment
Up-regulated (increased in expression)
Down-regulated (decreased in expression)
gene expression that responds to signals or triggers

9. Motivation Wheat is an 11 billion dollar industry [NRCC, 2015]
How can we improve wheat breeding with possible climate change?
Which miRNAs are differentially expressed with different stresses?
NRCC = National Research Council Canada

10. Related work Mayer et al., 2014 98,068 putative precursor miRNAs
52 miRNA sequences
Sun et al., 2014
Used Mayer et al.’s precursors
260 mature and star miRNAs

11. Contribution
Designed and implemented a toolchain that identify conserved miRNAs
Identified differentially expressed miRNA

12. 72 input files (4*6*3) ~523 million reads
Methodology
No stress (Control)
Heat (37◦)
Light
6 days
UV (2 min)
3 Replicates
72 input files (4*6*3) ~523 million reads
21 GB
Plant miRNAs
filtering
BLASTn
15,158 sequences
Conserved miRNAs

13. Tools used Python/biopython (Ubuntu Linux system)
bash shell scripting (Hermes, WestGrid)
BLAST
Bowtie2
MAFFT
RNAfold

14. Unique sequence identification
Distinct read
Sequence
Read count
Normalized read count / RPM:
1,000,000 * read count / total number of sequence

15. Removal of ncRNA sequences
Split each unique file into 300 files
Performed BLASTN with Rfam database
Aggregated 300 files to a single file

16. Filtering Consistent naming at least 10 RPM [Montes et al., 2014]
Identified 15,158 sequences in total

17. Conserved miRNA identification
Used miRNA database: miRBase
Discarded all miRNAs except plants
Performed BLASTN with miRBase
Considered 0-4 mismatches, no indels [Michael Axtell]

18. Conserved miRNA identification [continued]

19. Multiple sequence alignment
Matched species  bowtie2 with  wheat genome  contigs
Wheat, precursor, and conserved miRNA sequences
Experimental Sequences

20. 72 input files (4*6*3) ~523 million reads
No stress (Control)
Heat (37◦)
Light
Mayer et al.’s
supplementary
materials
6 days
UV (2 min)
3 Replicates
72 input files (4*6*3) ~523 million reads
21 GB
Putative
Precursors
Bowtie 2
filtering
15,158 sequences
Putative mature miRNAs
Star miRNA prediction
Conserved miRNAs

21. Matched species

22. Differential gene expression
Mayer et al.
Sun et al.
36 miRNAs, 232 sequences
Day 0: Control Vs. Heat
Control Vs. light
Control Vs. UV
Day 10: Control Vs. Heat
Control Vs. light
Control Vs. UV
edgeR
Day 1: Control Vs. Heat
Control Vs. light
Control Vs. UV
…………

23. 613 experimental sequence
Result - miRBase
No stress (Control)
Heat (37◦)
Light
6 days
UV (2 min)
3 Replicates
72 input files (4*6*3) ~523 million reads
21 GB
Plant miRNAs
filtering
BLASTn
15,158 sequences
87 plant miRNAs
613 experimental sequence

24. Result - Mayer et al. and Sun et al.
No stress (Control)
Heat (37◦)
Light
Mayer et al.’s
supplementary
materials
6 days
UV (2 min)
3 Replicates
72 input files (4*6*3) ~523 million reads
21 GB
Putative
Precursors
Bowtie 2
filtering
15,158 sequences
Sun et al.’s
supplementary
materials
Putative mature miRNAs
Star miRNA prediction
36 wheat miRNAs
232 experimental sequences

25. Result - differential gene expression

26. miRNAs: day 7, all stresses

27. miRNAs - in all days, heat stress
miR 398 miR 5064 miR 2020b

28. Number of miRNA differentially expressed
Heat: 34 families
Light: 8 families
UV: 7 families

29. Per day expression

30. Expression of miRNA families
miRNA 395 and 398 strongly suppressed
miRNA 1439, 2020, 5064 and 5175 expressed(+) with heat
miRNA 395 was suppressed in all days and in all stresses

31. Toolchain validation
Validated our tool with Brassica rapa dataset [Bilichak et al., 2015]
Includes pollen, embryo, endosperm and progeny tissue
Control and heat stress only

32. Identified Same differential expression
Same miRNA-168 is expressed in endosperm tissue of heat stressed plant
Bichilak et al. identified bra-miR168 with 6.48 log fold change
Identified cca-miR168 with 5.83 log fold change
Our log fold change may be lower because we mapped the Brassica rapa dataset to miRBase with 0 to 4
mismatches, which may possibly have excluded some Brassica rapa miRNAs.

33. Comparison – miRBase result
Researchers are doing more research concerning miRNAs in Triticum aestivum or species related
to it than Brassica rapa, which causes more entries in miRBase similar to the Triticum
aestivum miRNAs than the Brassica rapa miRNAs.

34. Summary ~ 523 million reads: miRBase: Mayer et al. and Sun et al.:
Filtered down to 15,158 sequences
miRBase:
Identified 87 plant miRNAs (613 sequences)
Mayer et al. and Sun et al.:
Identified 36 wheat miRNAs (232 sequences)
Differential gene expression:
Heat stress (most significant)

35. Find out the known conserved miRNAs
Summary
Find out the known conserved miRNAs

36. Future Work
Novel miRNA prediction
Predict novel miRNA from isomiR

37. Thank you

38. Related work Mayer et al., 2014 Sun et al., 2014
Identified 98,068 putative miRNA precursors
Identified 52 miRNA sequences
Sun et al., 2014
Used 11 libraries (dry grain, embryo etc.)
Used Mayer et al.’s 98,068 miRNA precursors
Reported 260 mature miRNA and star sequences

39. Our experimental data 21 GB dataset (~ 523 million small RNA reads)
From leaf samples of 96 wheat plants
72 files:
6 days: 0, 1, 2, 3, 7, 10
4 stresses (for 3 days)
Heat: 37◦ C
Light: continuous light
UV: 2 minutes of exposure/day
Control
3 replicates
File size: 250 to 450 MB

40. Methodology 4. Conserved miRNA identification 5. Multiple Sequence
Pre-processing
Processing
Post-processing
1. Unique sequence identification
4. Conserved
miRNA
identification
5. Multiple Sequence
Alignment
2. ncRNA removal
3. Filtering

41. 4. Conserved miRNA identification [continued]

42. 4. Conserved miRNA identification [continued]

43. Identification of conserved miRNAs using supplementary materials of Mayer et al., 2014
Aligned 10 RPM experimental sequences with the precursor database using Bowtie2 Finding an energetically stable structure of RNA using the sequence is known as the MFE method
A dot “." represents an unpaired base
open parenthesis “(" represents a base that is paired (5' end) to another base ahead of it (3' end)
closed parenthesis “)" represents a base that is paired (3' end) to another base behind it (5' end)
Considered exact match and MFE <0.2 Kcal/mol/nt [Kozomara et al., 2014]
Considered only the 52 wheat miRNA provided by Kurtoglu et al., 2014

44. Finding star sequence:
5’ end:
2 base bair overhang [Kozomara et al., 2014]

45. Finding star sequence:
5’ end:
3’ end:
2 base bair overhang [Kozomara et al., 2014]

46. Exceptions

47. Exceptions [continued]

48. Differential gene expression
Combination of Mayer et al. and Sun et al., 10 RPM
36 conserved miRNA families
232 unique sequences (325 in total)
205 sequences from Mayer et al.
12 sequences from Sun et al.
15 sequences are common
Used edgeR package of R programming language
3 (Control Vs. Heat, light, UV) files * 6 days = 18 input files

49. Differential gene expression

50. Conserved miRNA identification using miRBase database
Identified 87 conserved miRNA families
Matched with 613 sequences from experiment (10 RPM)
Many miRNA families matched with multiple experimental sequences
Tae-miR159b matched with 150 experimental sequences (maximum)

51. conserved miRNAs identification using the supplementary materials of Mayer et al. and Sun et al.
232 unique sequences (325 in total)
205 sequences from Mayer et al.
12 sequences from Sun et al.
15 sequences are common