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Small RNA Analysis

Small RNA Analysis. Gene 760 Jun Lu, PhD 2013-02-25. 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.

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Small RNA Analysis

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  1. Small RNA Analysis Gene 760 Jun Lu, PhD 2013-02-25

  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 BasicsTypes 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 BasicsmiRNAs • 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 BasicsmiRNAs • 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 BasicsTypes of small RNAs Rother and Meister, Biochimie 2011

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

  10. Small RNA Basicschemical 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 BasicsNon-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 SequencingLigation-mediated AMPlification 5’-P OH-3’ miRNAs Gel-Purify Product 3’ Adaptor B 5’-P 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 SequencingGel purification to avoid adaptor Dimer 3’ Adaptor 5’ Adaptor B 5’-P OH-3’ T4 RNA Ligase, ATP B RT-PCR

  15. Small RNA deep SequencingUse of Pre-adenylated 3’ adaptor 5’-P OH-3’ miRNAs 3’ Adaptor B 5’-P 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 SequencingCurrent Illumina workflow Total RNA Or Purified Small RNA 5’-P OH-3’ 3’ Adaptor B App T4 RNA Ligase, ATP 5’-P B Tabacco Acid Pyrophosphatase B 5’-P B 5’ Adaptor OH-3’ T4 RNA Ligase, ATP B RT B PCR

  17. Small RNA deep SequencingConsiderations 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 Sequencingalternatives 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 AnalysisAvailable Tools • miRDeep • miRDeep2 • miRCat • miRAnalyzer • miRTools • And others

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

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

  26. Data AnalysisSTEP 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 AnalysisSTEP 1: Remove Adaptors • Single adaptor removal drawbacks • Lose ~ 16 % of reads in the following example, can distort size distribution for specific small RNAs • TAGCTTATCAGACTGATGTTGACT 533006 reads • TAGCTTATCAGACTGATGTTGACTTGGACTTCTCGGGTGCCAAGGAACTC 87857 reads • Different ratios of adaptor-variants for different small RNAs, likely a sequence-dependent phenomenon • AACCCGTAGATCCGAACTTGTGA 666783 reads • AACCCGTAGATCCGAACTTGTGATGGACTTCTCGGGTGCCAAGGAACTCC 69 reads • 0.01%

  28. Data AnalysisSTEP 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 AnalysisMapping • Many identical reads for the same RNA, often associated with miRNAs. • E.gTCGTACGACTCTTAGCGG x5733052 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 AnalysisMapping • 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 AnalysisMapping • 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 AnalysisQuantification of Expression • Problem---how to normalize sequencing data? Can be especially problematic for small RNA data 0 Hour 12 Hour

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

  35. Data AnalysisQuantification 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 AnalysisQuantification 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 AnalysisSmall 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 AnalysisSmall 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

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