1. Small RNA Analysis
Gene 760
Jun Lu, PhD

2. Overview Small RNA Basics Types of Small RNAs
miRNAs and Other Small RNAs
Chemical Structures of Small RNAs
Non-templated Modification
Small RNA Deep Sequencing
Other Methods to Quantify miRNAs
Data Analysis

3. Small RNA Basics Types of small RNAs
miRNAs and its precursors
piRNAs
Endogenous and exogenous siRNAs
snoRNAs and its derivatives
tRNA and its derivatives
Transcriptional start site associated small RNAs
Enhancer Associated RNAs (eRNAs)
Repeat associated small RNAs
Many other types of small RNAs (often without deep understanding)
Breakdown products from longer RNAs
Artificial biochemical products

4. microRNAs are processed for maturation
Primary miRNA
Precursor miRNA
mature miRNA
Ago Proteins
Winter et al. Nat Cell Bio 2009

5. Small RNA Basics miRNAs
The same mature miRNA can be produced from multiple loci in the genome
Hsa-let-7a-1, chr 9
Hsa-let-7a-2, chr 11
Hsa-let-7a-3, chr 22

6. Small RNA Basics miRNAs
Sequence Isoforms (Length, Position(start, end))

7. piRNAs PIWI-interacting RNAs
Generally larger than miRNAs (~26 to 31 bases; different size range in different species)
Khurana et al, JCB 2010

8. Small RNA Basics Types of small RNAs
Rother and Meister, Biochimie 2011

9. Small RNA Basics Types of small RNAs—artificial Reaction Products
Example: HITS-CLIP
Chi et al. Nature 2009

10. Small RNA Basics chemical structures
RNaseIII products have 5’ phosphate group, and 3’ OH group
But not all small RNAs have the same chemical structure
Without 5’ phosphate
5’ Gppp cap instead of 5’ phosphate
2’-OMe modification at 3’ end
5’-P
OH-3’

11. Small RNA Basics Non-templated modifications
3’ Tailing
Single or mutliple nucleotide additions, such as U addition at the end
Can be based on target as a template—but not the generating locus as a template
RNA editing
ADAR enzymes
A->I->reverse transcribe as if it is G

12. Overview Small RNA Basics Small RNA Deep Sequencing
Ligation-mediated Amplification
Illumina Small RNA Library Preparation
Considerations when using the Standard Library Prep Protocol
Alternative Bench-Level Preparations and Choices in Sequencing Parameters
Other Methods to Quantify miRNAs
Data Analysis

13. Small RNA deep Sequencing Ligation-mediated AMPlification
miRNAs
5’-P
OH-3’
Gel-Purify
Product
3’ Adaptor
5’-P B
T4 RNA Ligase, ATP
5’-P B
5’ Adaptor
Gel-Purify
Product
OH-3’
T4 RNA Ligase, ATP B RT B
PCR

14. Small RNA deep Sequencing Gel purification to avoid adaptor DimerB
OH-3’
T4 RNA Ligase, ATP B
RT-PCR

15. Small RNA deep Sequencing Use of Pre-adenylated 3’ adaptor
miRNAs
5’-P
OH-3’
3’ Adaptor
5’-P B
T4 RNA Ligase, ATP
Self-circularization Product
5’-P B
Pre-adenylated 3’ Adaptor
3’ Adaptor
App B
T4 RNA Ligase 2 Truncated, no ATP

16. Small RNA deep Sequencing Current Illumina workflow
Total RNA
Or Purified
Small RNA
5’-P
OH-3’
3’ Adaptor
App B
T4 RNA Ligase, ATP
5’-P B
Tabacco Acid Pyrophosphatase
5’-P B B
5’ Adaptor
OH-3’
T4 RNA Ligase, ATP B RT B
PCR

17. Small RNA deep Sequencing Considerations when using standard Lib Preparation
Rely on the presence of 5’phosphate (depending on the need of analysis)
Use of pyrophosphatase may introduce some capped small RNAs
T4 RNA Ligase has some sequence preferences for substrates; T4 RNA Ligase 2 Truncation/mutations may have a different spectrum of sequence preference—sequencing reads do not 100% reflect relative abundance
Use of total RNA or purified small RNAs may generate quantitatively different profiles

18. Small RNA Deep Sequencing alternatives and sequencing parameters
Gel purification of small RNAs with a specific size range (use denaturing polyacylamide gel)
Phosphatase treat + T4 polynucleotide kinase to capture small RNAs without 5’ phosphorylation
Use polyA tailing + RT instead of using a sequence-specific 3’-adaptor
Length of sequencing run
50 bases single end sequencing is common on Illumina

19. Overview Small RNA Basics Small RNA Deep Sequencing
Other Methods to Quantify miRNAs
Microarray
qRT-PCR
Data Analysis

20. Other Methods of miRNA quantification
Microarrays
Use ligation-mediated amplification to label miRNAs
E.g. with a biotinylated primer during PCR
Use other labeling techniques (use different criteria)
Agilent Method

21. Other Methods of miRNA quantification
qRT-PCR
Key-lock-like RT strategy
PolyA tailing strategy
ABI Method
Qiagen Method

22. Overview Small RNA Basics Small RNA Deep Sequencing
Other Methods to Quantify miRNAs
Data Analysis
Existing Tools
Adaptor Removal
Mapping
Quantification of Expression
Small RNAs other than miRNAs

23. Data Analysis Available Tools
miRDeep
miRDeep2
miRCat
miRAnalyzer
miRTools
And others

24. Data Analysis Available Tools—miRDeep2
Run under Unix/Linux environment
Perl-based
Utilize Bowtie (v1) for mapping and RNAfold for folding RNA structures

25. Data Analysis STEP 1: Remove Adaptors
This is quite unique to small RNA sequencing analysis, because what you sequence is short RNAs
Sequencing Primer
miRNA
5’ Adaptor
3’ Adaptor
50 bases

26. Data Analysis STEP 1: Remove Adaptors—Details matter
Adaptors were not synthesized to 100% purity!
Standard miRDeep2 package allows removing only a single adaptor sequence.
Match first 6 bases of the adaptor to each sequence after 18 nt
If there is no match, sequentially match 5, 4, 3, 2, 1 of adaptor bases to the end of each read.
Some issues of such an algorithm
Single adaptor removal may lead to loss of reads and change of size distribution
6nt match may to be short, and may cut off real RNA sequences.
Ignored small RNAs less than 18 nt in length, which may be helpful to understand small RNA mechanisms
Artificially create reads in the 47, 48, 49 bp range due to non-stringent adaptor matches at the end of reads

27. Data Analysis STEP 1: Remove Adaptors
Single adaptor removal drawbacks
Lose ~ 16 % of reads in the following example, can distort size distribution for specific small RNAs
TAGCTTATCAGACTGATGTTGACT reads
TAGCTTATCAGACTGATGTTGACTTGGACTTCTCGGGTGCCAAGGAACTC reads
Different ratios of adaptor-variants for different small RNAs, likely a sequence- dependent phenomenon
AACCCGTAGATCCGAACTTGTGA reads
AACCCGTAGATCCGAACTTGTGATGGACTTCTCGGGTGCCAAGGAACTCC 69 reads
0.01%

28. Data Analysis STEP 1: Remove Adaptors
Adaptors were not synthesized to 100% purity!
Standard miRDeep2 package allows removing only a single adaptor sequence.
Single adaptor removal drawbacks
Modification
1. allow removing 2 (or more) adaptor sequence variants.
2. use a user-defined length of adaptor for sequence match (e.g. 10nt)
3. no limitation on the size of small RNA to be 18nt or more; instead, give user the option to define it.
4. do not remove end bases if there are only 3 or fewer nt matches to adaptor, again user definable for this cutoff.

29. Details matter! By removing one extra adaptor variant
# of reads
Length (Nt)

30. Data Analysis Mapping
Many identical reads for the same RNA, often associated with miRNAs.
E.g TCGTACGACTCTTAGCGG x times in one run (~10% of all reads!)
Reducing reads by “collapsing” reads of the same seq can significantly save time in alignment
Can reduce seqs by >20 fold—depending on miRNA abundance in cell
Can align to different regions on the genome—i.e. not unique in mapping
If sequence is too short, it may generate too many hits in the genome
Consider non-templated modifications
Non-templated tailing in small RNAs
Need to distinguish tailing vs. adaptor impurity
RNA Editing

31. Data Analysis Mapping Bowtie or Bowtie2
Mapping to known small-RNA-generating-sequence collections
E.g. precursor miRNA collection (downloadable from miRBASE)
Or snoRNA collections, or tRNA collections
Benefit:
can reduce mapping time;
can allow all non-unique mapping instances;
Can tolerate more mismatches for understanding of non-templated modifications
Drawback: can only inform those at known loci
Mapping to genome directly
Can help interpret modifications vs imperfect mapping conditions
Can help identify new small RNA regions

32. Data Analysis Mapping Hsa-miR-125b-1 Hsa-miR-125b-2
What the mapping cannot tell:
If there are RNA editing events, since many small RNAs have defined starting sites, it may be more difficult to differentiate between real RNA editing vs sequencing or PCR introduced errors.
If one miRNA can come from multiple loci, it is not possible to differentiate which loci the small RNA come from, even though it is possible to tell the opposite strand.
Hsa-miR-125b-1
Hsa-miR-125b-2

33. Data Analysis Quantification of Expression
Problem---how to normalize sequencing data? Can be especially problematic for small RNA data
0 Hour
12 Hour

34. Data Analysis Quantification of Expression
Problem---how to normalize sequencing data?
0 Hour
12 Hour

35. Data Analysis Quantification of Expression
Problem---how to normalize sequencing data?
Use total reads to normalize—most commonly used but may introduce artifacts.
Assume total/mean miRNA is the same
Quantile normalization
Use Spike-in controls
Spike-in controls are artificial small RNA sequences that can be used as “loading controls”
Spiked into initial RNA samples
Multiple spike-in RNAs should be used simultaneously to avoid relying on a single sequence to normalize data

36. Data Analysis Quantification of Expression
How to summarize given positional variations
Allow some flanking bases for tolerance
Depending on the aim of the analysis (e.g. seed sequence)

37. Data Analysis Small RNAs other Than miRNAs
Use transcriptional start site associated small RNA as an example
Adaptor removal
Collapse reads based on sequence
Map to known small RNA generating loci
Map the leftover sequences to genome
Align the mapped positions relative to transcriptional start sites

38. Data Analysis Small RNAs other Than miRNAs
Use transcriptional start site associated small RNA as an example

39. Summary Small RNA Basics Variations associated with small RNAs
Small RNA Deep Sequencing
Biochemical reactions determine interpretation of analysis
Other Methods to Quantify miRNAs
Useful in validating results
Data Analysis
Key steps in processing small RNA data
Pay attention to details in bench and bioinformatic methods