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Gene 760 Jun Lu, PhD 2013-02-25 SMALL RNA ANALYSIS.

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Presentation on theme: "Gene 760 Jun Lu, PhD 2013-02-25 SMALL RNA ANALYSIS."— Presentation transcript:

1 Gene 760 Jun Lu, PhD SMALL RNA ANALYSIS

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 Primary miRNA Precursor miRNA mature miRNA Winter et al. Nat Cell Bio 2009 MICRORNAS ARE PROCESSED FOR MATURATION Ago Proteins

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’-POH-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 miRNAs 5’-POH-3’ 5’-P B B SMALL RNA DEEP SEQUENCING LIGATION-MEDIATED AMPLIFICATION T4 RNA Ligase, ATP OH-3’ 5’-P B T4 RNA Ligase, ATP RT B PCR 3’ Adaptor 5’ Adaptor Gel-Purify Product Gel-Purify Product Gel-Purify Product Gel-Purify Product

14 SMALL RNA DEEP SEQUENCING GEL PURIFICATION TO AVOID ADAPTOR DIMER OH-3’ 5’ Adaptor 5’-P B T4 RNA Ligase, ATP 3’ Adaptor B RT-PCR

15 miRNAs 5’-POH-3’ 5’-P B B SMALL RNA DEEP SEQUENCING USE OF PRE-ADENYLATED 3’ ADAPTOR T4 RNA Ligase, ATP 5’-P 3’ Adaptor Self-circularization Product App B T4 RNA Ligase 2 Truncated, no ATP 3’ Adaptor Pre-adenylated 3’ Adaptor

16 SMALL RNA DEEP SEQUENCING CURRENT ILLUMINA WORKFLOW 5’-POH-3’ App B B T4 RNA Ligase, ATP OH-3’ 5’-P B T4 RNA Ligase, ATP RT B PCR 3’ Adaptor 5’ Adaptor B 5’-P Tabacco Acid Pyrophosphatase B Total RNA Or Purified Small RNA

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 miRDeep miRDeep2 miRCat miRAnalyzer miRTools And others DATA ANALYSIS AVAILABLE TOOLS

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 miRNA Sequencing Primer 50 bases 5’ Adaptor 3’ Adaptor

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 Length (Nt) # of reads

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 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 Hour12 Hour

34 DATA ANALYSIS QUANTIFICATION OF EXPRESSION Problem---how to normalize sequencing data? 0 Hour12 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


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