Comprehensive analysis of RNA-Seq data reveals extensive RNA editing

in a human transcriptome

Peng et al. Nature Biotechnology (2012) doi:10.1038/nbt.2122

Presented by: GUAN Peiyong23rd Feb 2012

Overview

Definition Mechanisms Functions

Data Pipeline Results

RNA Editing Concepts BGI’s Methodology

RNA Editing Concepts

Definition Mechanisms Functions

Gott & Emeson. Annu Rev Genet. 2000

RNA Editing | Definition

RNA editing can be broadly defined as any site-specific alteration in an RNA sequence that could have been copied from the template, excluding changes due to processes such as RNA splicing and polyadenylation.

RNA editing is a process that changes the identity of an RNA base after it has been transcribed from a DNA sequence.

Gott & Emeson. Annu Rev Genet. 2000.E. C. Hayden, Nature 473, 432 (2011).

RNA Editing | Mechanisms

Insertion / deletion RNA editing Posttranscriptional Nucleotide Insertion/Deletion Nucleotide Deletion-Insertion Nucleotide Insertion During Transcription Mixed Nucleotide Insertion

Conversion / substitution editing Adenosine-to-Inosine Editing (A-I or A-G, most prevalent in human)

Enzyme ADAR (adenosine deaminases that act on RNA). Cytidine-to-Uridine Editing (C-U)

Enzyme APOBECs (apolipoprotein B mRNA editing enzymes, catalytic polypeptide-like).

E.g., CAA UAA (STOP) Uridine-to-Cytidine Editing (U-C)

Li et al. Science doi:10.1126/science.1207018 ; 2011Gott & Emeson. Annu Rev Genet. 2000

RNA Editing | Functions

E. C. Hayden, Nature 473, 432 (2011).

BGI Methodology

Data Pipeline Results

Data | Preparation

75bp and 100bp

90bp, strand-specific

LymphoblastoidCell Line

767.58 million reads (73.84%) uniquely aligned

767.58 million reads (73.84%) uniquely aligned

Illumina Genome Sequence Analyzer

Data | Preparation

RNA-Seq of Lymphoblastoid cell line of a male Han Chinese individual (YH) Genome sequence was reported previously.

Nature 456, 60-65, (2008) 767 million sequence reads

RNA-Seq 75bp and 100bp Poly (A)+

90bp Poly (A) - - strand-specific sequencing Small RNA-Seq

Data | Sequencing Coverage

Data | Sequencing Depth

Data | Simulated Data

Paired-end reads with fixed length of 75 bp, simulated randomly from chromosome 1 of the human RefSeq. Use chromosome 1 of the NCBI human RefSeq as a

reference: Two sets of simulated data were created:

Set #1: Random SNV by MAQ with default options (5-, 10-, 20-, and 50-fold coverage).

Set #2: A→G substitution at positions referenced in the DARNED database (50-fold coverage).

Pipeline | Overview

Pipeline | Illumina reads alignment (SOAP2)

Due to the potential uncertainty in read alignment across splice junctions, SOAP2 was used in this regard rather than tools that utilize gapped alignment across exon boundary, such as SOAPsplice.

Reference genome (NCBI Build 36.1, hg18). Two paired-reads – aligned together with both in the correct orientation. Aligning the cDNA reads to the reference genome:

≤ 3 mismatches for the 75-bp reads. ≤ 4 mismatches for the 90-bp and 100-bp reads.

Best Hits – alignments with the least number of mismatches: Uniquely placed – 1 best hit (kept). Repeatedly placed – multiple equal best hits (discarded). Potential PCR duplicates (discarded).

Reads with unique ungapped genome alignment.

Pipeline | RNA editing sites/RNA-centric SNVs detection

Multiple filters with stringent thresholds to facilitate unbiased detection of bona fide editing or base substitution events in the RNA-Seq reads.

RNA-centric SNVs were first identified from aligned cDNA reads using SOAPsnp, which uses a method based on Bayes’ theorem (the reverse probability model) to call consensus genotype by carefully considering the data quality, alignment and recurring experimental errors, with parameters e = 0.0001 and r = 0.00005.

We further lifted a default filter in the basic filter step of the program that was designed to discard sequence reads with more than one variant within a 5-bp span (for clustered AG editing?).

SNVs 10 filtering steps

Pipeline | 1. Basic filter

Retain SNVs that meet the following criteria: Quality score of consensus genotype ≥ 20. Covered depth ≥ 5. Repeats (estimated copy number of the flanked

sequence in genome) ≤ 1.

Pipeline | 2. Read parameter filter

Optimize parameters using simulated data set: m, the minimal distance of a SNV site to its supporting

reads’ ends q, minimal sequencing quality score of SNV-corresponding

nucleotide (m, q)=(15,20)

n, minimal number of supporting reads that meet the above two cutoff parameters n = 2

Pipeline | 2. Read parameter filter

Two sets of data: Set #1: random substitution Set #2: A G substitution in DARNED database

Pipeline | 3. RNA-DNA variants filter.

Focus on RNA-DNA variants only: Sites of which DNA genotypes are the same as RNA

genotypes were removed.

Pipeline | 4. YH genome variants filter.

Distinguish RNA editing from allele-specific expression and duplication polymorphisms: Keep SNVs remaining from step 3 only if their corresponding

DNA genotypes are homozygous and diploid in copy number. Parameters of YH genome sequence reads corresponding to a

candidate site: Depth is ≥5; Consensus quality is ≥20; Average quality of the first best allele ≥ 20; Depth of the second best allele, if present, is <5% of the total number of

reads; The second best allele should not be the variant allele in the RNA data; And average sequencing quality of the second best allele is <10.

Exclude genomic duplication polymorphisms: CNVnator tool with bin set to 50, and removed sites that were nondiploid

in nature.

Pipeline | 5. MES filter.

Remove misaligned reads that arise from mapping error inherent to the mapping algorithm (MES): Simulate read sequences based on all human genes (hg18

transcriptome) using MAQ without mutation (-r parameter).

Align simulated reads using SOAP2 & call SNVs using SOAPsnp.

Filter the identified SNVs using filters #3 and #4 MES. SNVs matched the MES were removed.

Pipeline | 6. Strand filter.

Remove potential strand-specific errors in sequences generated by the Illumina platform: Evaluate the counts of the reads mapped to the +/- strands

using Fisher’s exact test. Discard the site if:

Reads exhibited strand bias in distribution (P < 0.01) & Number of supporting reads mapped to either strand is <2.

Pipeline | 7. BLAT filter.

Address the potential pitfall of paralogous sequences in site calling: Use BLAT to search for SNVs’ supporting reads in the

reference genome. Same mismatch tolerance used in SOAP2 alignment.

Discard all supporting reads with >1 hit. Filter SNVs that have <2 qualified supporting reads.

Pipeline | 8. Known SNPs filter.

Eliminate germline variants: Cross-reference the remaining SNVs against known

SNP databases: 1000 Genomes Project. Genomes of Yoruba, Watson, Korean. dbSNP (version 129).

Pipeline | 9. Multiple type of mismatches filter.

Discard SNV candidate sites with >1 nonreference type: For example:

Reference allele – A Nonreference alleles – G and T

Pipeline | 10. Editing degree filter.

Exclude polymorphic sites with extreme degree of variation (100%):

sitethe covering reads of #

allele variant the g supportinreads of#Editing of Degree

Remaining Sites Further Analysis

Pipeline | Analysis of the sequence and structural features of RNA editing.

To identify sites dsRNA structure, or sites in 3 -UTR ′that are likely microRNA seed matches: Li, J.B. et al. Genome-wide identification of human

RNA editing sites by parallel DNA capturing and sequencing. Science 324, 1210–1213 (2009).

Pipeline | Analysis of the sequence and structural features of RNA editing. Editing sites clustering:

Defined as occurrence of ≥3 variants per 100bp. Conserved region:

Annotated as ‘most conserved’ by the UCSC genome browser.

Coding sequence: Defined by the RefSeq annotation.

Highly edited genes: ≥10 variant sites per gene

Gene enrichment: DAVID pathway-classification tool.

Pipeline | Identification of miRNA and editing (1).

Filtering of small RNA reads: Filter out low-quality reads; Trim 3 adaptor sequence by a dynamic programming ′

algorithm; Remove adaptor contaminations formed by adaptor

ligation; Retain only short trimmed reads of sizes from 18 to 30

nt.

Pipeline | Identification of miRNA and editing (2).

Annotate and categorize small RNAs: Filter out small RNA reads possibly from known

noncoding RNAs: rRNA, tRNA, snRNA and snoRNA deposited in the Rfam

database and the NCBI Genbank. Discard small RNA reads assigned to exonic regions. Subject the remaining small RNA to MIREAP, which

identifies miRNA candidates according to the canonical hairpin structure and sequencing data.

Pipeline | Identification of miRNA and editing (3). Align identified miRNA reads to miRNA reference

sequences: BLAST, ≤1 mismatch. Reads that were uniquely aligned and overlapped with

known miRNAs were used to identify miRNA editing sites. First, identify reads with mismatch to hg18 genome.

Reads with mismatch within 1 nt at 5 end or 2 nt at 3 end were ′ ′discarded (?).

Then, identify miRNA edits by the following criteria: Sequencing depth of editing sites ≥ 5; Frequency of SNV occurrence ≥5% & ≤95%; Variants that were not found in previous SNP annotations

YH, 1000 genomes project, Yoruba, Watson, Korean and dbSNP version 129.

Results| Editing Events Identified

22,688 RNA editing sites Poly (A)+

To ascertain the editing type for these sites, cross-reference against RefSeq.

~30% of the identified sites: Unannotated in the database (5,381). Corresponded to overlapping transcript units on both strands

(57). 11,467 sites were unambiguously mapped to known gene models.

Poly (A)-

To identify editing sites in the intergenic regions of the transcriptome

11,221 RNA editing sites identified.

Results| Editing Sites Distribution

50% leads to changes in coded

amino acids.

50% leads to changes in coded

amino acids.

Results|

Editing sites Characterization

Poly (A)+ Poly (A)-

Poly (A)+

Poly (A)+,CDS

Poly (A)-

Results| Novel Editing Sites

Results| Frequency of nucleotides in the flanking sequences

Poly (A)+ Poly (A)-

Results| % of Edits in Conserved Regions

Poly (A)+ Poly (A)-

Results| Experimental Validation

Two replicates of PCR amplification and Sanger sequencing of both DNA and RNA from the same batch of cells from the YH cell line.

Results | Comparison with Other Datasets

Results | Genes with multiple editing sites.

Results | RNA editing and miRNA-mediated regulation

2,474 editing sites in 3 -UTRs′ Extract 6 + 1 + 6 bp sequence & search in miRBase.

Summary

Pipeline for identifying RNA editing events by screening RNA-DNA differences in the same individual. 10 filters to handle various aspects of false positives.

Experimentally validated novel RNA editing sites. Evidence of extensive RNA editing in a human cell line.

Question: since the model parameter were optimized using random data from DARNED, why there is no significant overlaps between DARNED database and BGI’s discovered editing sites?

RNA Editing

Overview

Literature Review RNA Editing Concepts

DefinitionMechanismsFunctions

RNA Editing Site PredictionPrediction Methods

Machine Learning Based Methods Mapping Based Methods

Database

Literature Review RNA Editing Concepts RNA Editing Site Prediction

RNA Editing Concepts

Definition Mechanisms Functions

Gott & Emeson. Annu Rev Genet. 2000

RNA Editing | Definition

RNA editing can be broadly defined as any site-specific alteration in an RNA sequence that could have been copied from the template, excluding changes due to processes such as RNA splicing and polyadenylation.

“RNA editing” is a process that changes the identity of an RNA base after it has been transcribed from a DNA sequence.

Gott & Emeson. Annu Rev Genet. 2000.E. C. Hayden, Nature 473, 432 (2011).

RNA Editing | Mechanisms

Insertion / deletion RNA editing Posttranscriptional Nucleotide Insertion/Deletion Nucleotide Deletion-Insertion Nucleotide Insertion During Transcription Mixed Nucleotide Insertion

Conversion / substitution editing Adenosine-to-Inosine Editing (A-I or A-G, most prevalent in human)

Enzyme ADAR (adenosine deaminases that act on RNA). Cytidine-to-Uridine Editing (C-U)

Enzyme APOBECs (apolipoprotein B mRNA editing enzymes, catalytic polypeptide-like).

E.g., CAA UAA (STOP) Uridine-to-Cytidine Editing (U-C)

Li et al. Science doi:10.1126/science.1207018 ; 2011Gott & Emeson. Annu Rev Genet. 2000

RNA Editing | Functions

E. C. Hayden, Nature 473, 432 (2011).

RNA Editing | Functions (Cont’d…)

mRNA

Gott & Emeson. Annu Rev Genet. 2000

RNA Editing | Functions (Cont’d…)

Maas et al. 2003

RNA Editing Site Prediction

Prediction Methods Machine Learning Based Methods Mapping Based Methods

Database

Machine Learning | Bundschuh 2004

Bundschuh 2004

Machine Learning | Bundschuh 2004

Bundschuh 2004

Machine Learning | Bundschuh 2004

Bundschuh 2004

Machine Learning | Bundschuh 2004

Bundschuh 2004

Machine Learning | Bundschuh 2004

Results Predictive performance of over 90% on the amino acid

level and of over 70% on the editing site level. Limitations

Specific for Physarum polycephalum Insertion of C is most common.

Requires training data Uses information on homologs of the gene in other organisms

and statistical information on editing sites specific for Physarum. Very limited training / testing data

Only 6 genes with known RNA editing sites in the mitochondrion of Physarum.

Tested using leave-one-out approach.

Bundschuh 2004

Machine Learning | Clutterbuck et al. 2005

Clutterbuck et al. 2005

Machine Learning | Clutterbuck et al. 2005

Focused on recoding A–I mRNA editing sites A recoding site is a site where editing alters the amino acid

sequence. Used a combination of 7 predictive features to screen

a large set of expressed versus genomic sequence mismatches. For many of the known sites, editing can be observed in

multiple species and often occurs in well-conserved sequences.

In addition, they often occur within imperfect inverted repeats and in clusters, etc.

Clutterbuck et al. 2005

Machine Learning | Clutterbuck et al. 2005

Clutterbuck et al. 2005

Machine Learning | Clutterbuck et al. 2005

7features Number of putatively edited mouse cDNAs or ESTs with the same mismatch as the same

position (Allowed values: 1, 2, >2); Number of non-edited mouse cDNAs or ESTs combined with the number of publicly

available genomic sequences for each given mismatch (Allowed values: 1, 2, >2); Where possible the human homologues were aligned using Lagan; We calculated the effect of the edit on the amino acid sequence by BLAST searching the

Ensembl nucleotide sequence against the equivalent protein sequence, then mapping the putative editing site onto the alignment.

Sequence conservation was analyzed using the same Lagan mouse/human alignments, from which the best conserved 120 bp window overlapping each putative editing site was selected (Continuous variable);

Putative mouse ECSs were identified by scanning for inverted repeats using a Smith–Waterman alignment algorithm from EMBOSS

Clusters of sites were defined by the observation of more than one putative editing site within an exon (Continuous variable).

Clutterbuck et al. 2005

Machine Learning | Clutterbuck et al. 2005

Clutterbuck et al. 2005

Machine Learning | Clutterbuck et al. 2005

Limitations Only identifies recoding sites. Only for A-I editing. Highly depends on known biology knowledge (7

seemingly ad hoc features). Model over-fitting? (so many features that should be

inter-dependent).

Clutterbuck et al. 2005

Mapping | Kim et al. 2004

Kim et al. 2004

Mapping | Kim et al. 2004

Kim et al. 2004

Mapping | Kim et al. 2004

Method Mapping human and mouse cDNA from UCSC to the reference

genome. Filtering (95% sequence identity + alignment score). Using a scan statistic method to look for clusters of A-to-G

substitutions in each transcript. Results

An excess of A-G substitutions in human full-length cDNAs. Correlation between A-G substituted bases and Alu sequences. Etc.

Limitations Relying on biology knowledge, i.e., the A-G substitution sites

tends to cluster together.

Kim et al. 2004

Mapping | Levanon et al. 2004

Levanon et al. 2004

Mapping | Levanon et al. 2004

Levanon et al. 2004

Mapping | Levanon et al. 2004

Method Algorithm to align the expressed part of the gene with the corresponding

genomic region, looking for reverse complement alignments longer than 32 bp with identity levels higher than 85%.

Cleaned the sequences supporting the stem region. Because sequencing errors tend to cluster in certain regions, especially in low

complexity areas and towards the ends of sequences, we discarded all single-letter repeats longer than 4 bp, as well as 150 bp at both ends of each sequence.

In addition, all 50 nucleotide-wide windows in which the total number of mismatches was five or more were considered as having low sequencing quality and were discarded.

However, four or more identical sequential mismatches were masked in the count for mismatches in a given window. This exception is intended to retain sequences with many sequential editing sites.

Mismatches supported by <5% of available sequences were also discarded, and, finally, known SNPs of genomic origin were removed.

Levanon et al. 2004

Mapping | Levanon et al. 2004

Results Mapped 12,723 A-to-I editing sites in 1,637 different

genes, with an estimated accuracy of 95%, raising the number of known editing sites by two orders of magnitude.

Limitations Only focused on A-I editing.

Levanon et al. 2004

Mapping | Li et al. 2011

Li et al. 2011

Flowchart of Analysis

Li et al. 2011

Mapping | Li et al. 2011

Method Comparing RNA and sequences from Human B cells of 27

individuals, who were sequenced in the 1000 Genomes Project and the International HapMap Project.

Map RNA-seq to the hg18 mRNA using Bowtie. Results

More than 10, 000 exonic sites with RNA and DNA differences (RDD).

RRD not limited to A-G and C-U, but all 12 possible categories. Problem

Too many false-positives caused by paralogs in the genome. Rigorous filtering should have been performed.

Li et al. 2011

Mapping | Schrider et al. 2011

Schrider et al. 2011

Mapping | Schrider et al. 2011

Similar methods to Li et al. Science 2011 paper but: Additional filtering steps. Schrider et al. used BWA instead of Bowtie for mapping because it is

more accurate and allows for indels. This paper criticize the Li et al. paper.

Pointing out that many of the 10, 000 exonic RDD sites discovered by Li et al. are actually from paralogs.

But Levanon et al. 2004 even mapped 12,723 A-to-I editing sites in 1637 different genes!

This raised the questions: How precise is the mapping? How abundant is the RNA editing events in human?

Schrider et al. 2011

Mapping | Bahn et al. 2011

Bahn et al. 2011

Mapping | Bahn et al. 2011

2 main challenges for genome-wide identification of RNA editing: Separating true editing sites from false discoveries

and, Accurate estimation of editing levels.

Bahn et al. 2011

Mapping | Bahn et al. 2011

Bahn et al. 2011

Mapping | Bahn et al. 2011

Bahn et al. 2011

Mapping | Bahn et al. 2011

Determine whether the DNA–RNA differences are likely authentic events or sequencing errors.

Bahn et al. 2011

Mapping | Bahn et al. 2011

Data RNA-seq data of a human glioblastoma cell line, U87MG. Whole genome sequencing data.

Method Combines multiple mapping tools (Bowtie, BLAT and TopHat) Double-filtering of mismatches in the mapped reads:

Mapped uniquely with ≤ n1 (5) mismatches and, Did not map to other genomic loci with ≤ n2 (12) mismatches are retained (n2 > n1).

Results Around 10,000 DNA–RNA differences were identified, the majority being

putative A-to-I editing sites. (FDR ~ 5%). The estimated editing levels from RNA-seq correlated well with those based on

traditional clonal sequencing. Simulated data for FDR calculating.

Bahn et al. 2011

Mapping | Carmi et al. 2011

Carmi et al. 2011

Mapping | Carmi et al. 2011

Carmi et al. 2011

Mapping | Carmi et al. 2011

Method MegaBLAST Very important step to replace As with Gs and re-map. Filtering

RNAs that appeared ultra-edited in more than one transformation/strand combination.

Etc.

Carmi et al. 2011

Mapping |Picardi et al. 2011

Picardi et al. 2011

Mapping |Picardi et al. 2011

Method Aligns RNA-seq reads to hg18 (Bowtie). Pileup (SAMTool). Explore position by position and record all substitutions. Build table with probability of observing the change (Fisher-

test). Filtering

Known genomic polymorphisms in dbSNP (v130) are excluded. Substitutions compatible with RefSeq annotations are filtered in. Sites with a coverage lower than 10 reads are removed. Sites with multiple observed substitutions are also excluded. Sites with a background higher than 0.1 are not considered.

URL: http://t.caspur.it/ExpEdit/

Picardi et al. 2011

Database | Kiran et al. 2010

Data from previously published papers.

Kiran et al. 2010

Summary | Literature Review

Machine Learning Based Methods Highly depends on the biology knowledge Highly depends on experimentally validated data for model training

Mapping Based Methods Sensitive to systematic sequencing error. Different mapping tools:

Different ways of dealing with gaps, mismatches and splice junctions. Mapability

Ultra-edited RNA may not be mapped & discarded. Intensive filtering is required due to:

Paralogs in the genome Imperfect mapping

Data DARNED could serve as a validation / comparison resource.

E. C. Hayden, Nature 473, 432 (2011).