Eukaryotic small RNA Small RNAseq data analysis for mirna identification

Transcription

1 Eukaryotic small RNA Small RNAseq data analysis for mirna identification P. Bardou, C. Gaspin, S. Maman, J. Mariette, O. Rué, M. Zytnicki INRA Sigenae Toulouse INRA MIA Toulouse GenoToul Bioinfo INRA MaIAGE Jouy-en-Josas

2 Outline Introduction to non coding RNA Small RNA-seq : focus on mirna Bioinformatics analysis Data cleaning Reads annotation mirna prediction Differential expression

3 Introduction to non-coding RNA

4 Evolution of the central dogma Replication DNA Reverse transcription Regulation Gene formation Epigenesis Transcription RNA Splicing and edition events Translation Proteins

5 The RNA world RNA mrna <Riboswitches> «Regulatory» RNAs lncrna srna (adaptative responses in bacteria) Non coding RNA «Housekeeping» RNAs - trna - rrna - RNaseP RNA - telomerase RNA (replication) - snrna (maturation-splicing) - snorna, grna (modification, editing) «Catalytic» RNAs - Hairpin ribozyme - Hammerhead ribozyme - - mirna (development...) - sirna (defense) - pirna (epigenetic and posttranscriptional gene silencing in spermatogenesis) Multiple roles of RNA in genes regulation

6 The non coding protein RNA world Not predicted by gene prediction tools No specific signal (start, stop, splicing sites...) Multiple location (intergenic, intronic, coding, antisense) Variable size No strong sequence conservation in general A variety of existing approaches not always easy to integrate Known family: sequence-structure homology New family: De novo (promoters, terminators...) / sequence homology

7 RNA background RNA folds on itself by base pairing : A with U : A-U, U-A C with G : G-C, C-G Sometimes G with U : U-G, G-U Folding = Secondary structure Structure related to function : ncrna of the same family have a conserved structure Sequence less conserved

8 RNA background Different elementary motifs

9 The smallrna world mirna : Pol II dependant Posttranscriptional regulation of protein coding genes Associated with AGO1, AGO2, AGO7 and AGO10 sirna (nat-sirna): Pol IV- & Pol V-dependant ~24nt Silencing of repetitive elements at the transcriptional level via DNA methylation and repressive chromatin modifications Associated with AGO4, AGO6, AGO9 and AGO1 ta-si RNA (specific to plants): Pol II dependant ~21nt Biogenesis initiated by mirna-guided cleavage of a single-stranded Associated with AGO7 (mir390) Deeply conserved TAS3 transcript precursor pirna (specific to animals): Pol II dependant ~26-30 nt Transposon silencing Associated to Piwi protein

10 ncrna databases mirbase ( Rfam ( Silva ( GtRNAdb ( pirna databank...

11 The srna biogenesis

12 Vocabulary on mirna Pri mirna Pre mirna mature mirna mirna* or 'passenger' mirna mirna 5' mirna 3'

13 Size matters 22 21

14 small RNAseq pipeline Exp. design Sequencing Quality check Cleaning with reference Mapping Prediction Normalization Differential expression without reference Annotation

15 Profiling mirna The average copy number of an individual mirna is estimated to ~500 per cell Higher than the average expression of mrna species Different mirna species vary widely in concentration over a dynamic range of at least four orders of magnitude (some are present at >10,000 copies per cell) mirnas exist in at least two distinct physical states in blood plasma within vesicles (exosomes and/or microvesicles) associated with Argonaute (AGO)2 containing protein complexes A known amount of synthetic mirnas that are not expressed in the biological sample can be spiked-in In situations in which assessment of RNA extraction efficiency is important (plasma, serum) When most of the mirnas are differentially expressed

16 mirna sequencing depth 83 new mirnas detected between 5 and 10 million reads for the Gastrocnemius muscle 58 new mirnas detected between 5 and 9 million reads for the plasma sample

17 mirna identification and profiling Three major platforms Quantitative real-time reverse transcription PCR (qrt-pcr) Microarray Major drawback: hybridization-based techniques suitable only for known mirnas Small RNA cloning followed by deep sequencing (mirna-seq) Significant biases introduced during small RNA cdna library preparation, often more than three orders of magnitude for individual mirnas: RNA ligation identified as one of the major sources

18 small RNAseq pipeline Exp. design Sequencing Quality check Cleaning with reference Mapping Prediction Normalization Differential expression without reference Annotation

19 How can we study mirna? RNAseq not suited for mirna (protocol and size) small RNAseq mirnas lack a common sequence, such as a poly(a) tail, that can be used for selective enrichment or as a universal primer-binding site for reverse transcription

20 small RNA-Seq library preparation Monophosphate at 5' end and OH at 3' end Total RNA: contain all kinds of RNA species including mirna, mrna, trna, rrna... Ligate with 3' adapter RNA with modified 3'-end will not ligate with 3' adapters. Only RNA with OH in 3'-end will ligate. Ligate with 5' adapter RT-PCR and Size Selection Only RNA with monophosphate in 5'-end will ligate with 5' adapters. cdna containing both adapter sequences will be amplified. microrna will be enriched from PCR and gel size selection.

21 Description of the dataset Genome : A. thaliana 2 conditions, 3 replicates each : WT dcl2-dcl3-dcl4 mutant Only the first reads The data is available in the Published Pages

22 small RNAseq pipeline Exp. design Sequencing Quality check Cleaning with reference Mapping Prediction Normalization Differential expression without reference Annotation

23 Let us get started! Connect to the Toulouse Galaxy: Login Start a new history Go to Shared Data > Published Pages > srna-seq (keep the page open somewhere) Import the first dataset WT1

24 Quality control Per base quality

25 Quality control Sequence duplication

26 Quality control Nucleotide composition

27 small RNAseq pipeline Exp. design Sequencing Quality check Cleaning with reference Mapping Prediction Normalization Differential expression without reference Annotation

28 Why cleaning? Look at the reads...

29 Trim Adaptor Many possible tools. We will use Trim Galore (wrapper around Cutadapt ) Use full parameter list: Adapter sequence: AGATCGGAAGAGC Overlap with adapter: 5 Discard reads that became shorter than: 18 Generate report file: yes Check the report.

30 Size distribution

31 small RNAseq pipeline Exp. design Sequencing Quality check Cleaning with reference Mapping Prediction Normalization Differential expression without reference Annotation

32 mirbase A. thaliana

33 mirbase mir156a stem-loop

34 mirbase mir156a mature and star

35 Mapping to mirbase Aim: Get/Quantify the known mirnas. Which mapper/parameters to use?

36 Mapping to mirbase Aim: Get/Quantify the known mirnas. Map to mature or hairpins?

37 Mapping to mirbase Aim: Get/Quantify the known mirnas. Unique vs Multiple hits ath-mir157b-5p ath-mir157d ath-mir157c-5p ath-mir157a-5p ath-mir156h ath-mir156d-5p ath-mir156a-5p ath-mir156e ath-mir156f-5p ath-mir156c-5p ath-mir156b-5p ath-mir156b-3p ath-mir156f-3p ath-mir156c-3p ath-mir156a-3p ath-mir156d-3p ath-mir157a-3p ath-mir157b-3p ath-mir157c-3p..uugacagaagauagagagcac.....ugacagaagauagagagcac....uugacagaagauagagagcac....uugacagaagauagagagcac.....ugacagaagaaagagagcac.....ugacagaagagagugagcac.....ugacagaagagagugagcac.....ugacagaagagagugagcac.....ugacagaagagagugagcac.....ugacagaagagagugagcac.....ugacagaagagagugagcac.. UGCUCACCUCUCUUUCUGUCAGU...GCUCAC.UCUCUAUCCGUCACC...GCUCACUGCUCUAUCUGUCAGA...GCUCACUGCUCUUUCUGUCAGA...GCUCAC.UCUCUUUUUGUCAUAAC.GCUCUC.UAGCCUUCUGUCAUC...GCUCUC.UAGCCUUCUGUCAUC...GCUCUC.UAUACUUCUGUCACC..

38 Mapping to mirbase Aim: Get/Quantify the known mirnas. # mismatches

39 Mapping to mirbase Aim: Get/Quantify the known mirnas. Start new history with the trimmed reads. Get the mature mirna file. Use Bowtie for Illumina with options: use reference from history: the mature file see full parameter list seed length 11 report all valid alignments suppress all alignments if more than 1 exists use best use strata do not print header

40 mir Quantification Aim: Get/Quantify the known mirnas. Get the reads that map to something (filter c3!= * ) FastQC to check size Extract the targets from the SAM file (cut c3) Sort by names (sort Column 1, Alphabetical sort, Ascending order) Count the names (group Column 1, Insert Operation, Count, Column 1)

41 Contamination with trf or rrna? What the reads that match to mirnas and other annotation? Extract the read name (cut c1) from the filtered SAM file Sort with ascending order (sort column 1, Alphabetical sort, Ascending order) Derive a workflow from these steps

42 Contamination with trf or rrna? What the reads that match to mirnas and other annotation? Get the trna sequences Apply the workflow to the reads and these sequences Do the same for rrna Use the Venn tool

43 small RNAseq pipeline Exp. design Sequencing Quality check Cleaning with reference Mapping Prediction Normalization Differential expression without reference Annotation

44 mirna or not?

45 Canonical mirna

46 mirna characteristics for pre-mirna identification Pre-miRNA information Hairpin structure of the pre-mirna Pre-miRNA localisation (coding/non coding TU intronic/exonic ) mature Presence of cluster Size of the pre-mirna star mirna-5p and mirna-3p information Existence of both mirna-5p and mirna-3p Sequence conservation Overhang (around 2 nt) related to Drosha and Dicer cuts Size of mirna-5p and mirna-3p Overexpression of one of the mirna-5p and mirna-3p

47 mirna characteristics for pre-mirna identification

48 Existing software

49 Basic features Availability (web/executable) Computing resources (time, memory) Reads pre-processing Mapping Precursor identification

50 Steps Pre-processing Adaptators trimming Redundancy Low complexity Other ncrna Size of the mature mirna (min/max) Mapping Size and region of the read Number of locations Considered Reported Error(s) consideration in mapping Quality of the read Precursor identification Characteristics used for identification Post processing step: assessment of the potential mirna SVM, bayesian statistics based score, combinatorial rules...

51 mirdeep

52 mirdeep2

53 The Mapper module Map reads with Bowtie bowtie -f -n 0 -e 80 -l 18 -a -m 5 --best --strata (no mismatch in the 18-seed, 2 afterwards, discard reads that match >5 times) The Quantifier module bowtie -f -v 1 -a --best --strata --norc? mirdeep2 modules The Core module Scan each strand separately Stop at first peak Find highest peak in the vicinity (70bp) Fold around highest peak (twice: , ) Remap reads on the region Fold region Check reads and folding no interior loop 2bp overhang not much in loop Evaluate

54 mirdeep2 Mapper Start new analysis New history and import the trimmed reads Our version mirdeep2 only reads FASTA files Fastq Fasta (whichever) of the data Use mirdeep2 Mapper srnaseq mirdeep2 Mapper inputs: this file TAIR10 reference outputs: collapsed input fasta >seq_0_x3095 GCGTCTGTAGTCCAACGGTTAGGATAATTGC the mapped read in the ARF (?) format

55 ARF format seq_17330_x tttggattgaagggagctctt tttggattgaagggagctcct + 1 read id length of read mapping start position mapping end position sequence chromosome name chr2_gi ref NC_ mmmmmmmmmmmmmmmmmmmmm mapping size start position end position genome sequence strand # mismatches 'm': match, 'M': mismatch 21

56 mirdeep2 Prediction / Core Use mirdeep2 Core srnaseq mirdeep2 Prediction inputs: the two previous files outputs: HTML report Same report in CSV The genomic interval of the predicted mirnas BED format

57 mirdeep2 Prediction / Core

58 Compare predictions Aim: Compare the prediction with known annotation Get the mirbase annotation file Use bed intersect inputs: the BED file of the prediction the BED file of the known mirnas output style: wao output: a tabular file

59 Output format 1-3: interval of the query 4: id of the query 5:??? 6: strand of the query 7-9: same stuff 10-15: target description (-1 if no match)

60 mirdeep2 Quantifier Get annotated data Import the precursors and the mature mirnas from Saved Pages Use mirdeep2 Quantifier My tests mirdeep2 Quantifier outputs: inputs: HTML report file collapsed fasta tabular file with expression precursors (almost same data) matures rest left untouched mature star

61 Output HTML Results (hover on the title to see description): pre-mirna name # reads in the pre mature (star) counts (unknown normalization) # reads in the loop link to NCBI Blast mirbase mature sequence mirbase pre-mirna sequence

62 Example of output

63 small RNAseq pipeline Exp. design Sequencing Quality check Cleaning with reference Mapping Prediction Normalization Differential expression without reference Annotation

64 Differential expression

65 Differential expression Get the counts from the Saved Page Create a design file with the counts Use Preprocess files with SARTools Groups: WT vs dcl, and replicate names WT1, etc. Start differential expression analysis Use SARTools edger Provide the 2 outputs (design & counts) from previous step Other options left unchanged

66 The end!