1. 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 Peiyong 23 rd Feb 2012

2. Overview  Definition  Mechanisms  Functions  Data  Pipeline  Results  RNA Editing ConceptsBGI’s Methodology

3. RNA Editing Concepts  Definition  Mechanisms  Functions

4. Gott & Emeson. Annu Rev Genet. 2000

5. 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).

6. 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 ; 2011 Gott & Emeson. Annu Rev Genet. 2000

7. RNA Editing | Functions E. C. Hayden, Nature 473, 432 (2011).

8. BGI Methodology  Data  Pipeline  Results

9. Data | Preparation 75bp and 100bp 90bp, strand-specific Lymphoblastoid Cell Line 767.58 million reads (73.84%) uniquely aligned Illumina Genome Sequence Analyzer

10. 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

11. Data | Sequencing Coverage

12. Data | Sequencing Depth

13. 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).

14. Pipeline | Overview

15. 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.

16. 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 A  G editing?). SNVs10 filtering steps

17. 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.

18. 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

19. Pipeline | 2. Read parameter filter  Two sets of data:  Set #1: random substitution  Set #2: A  G substitution in DARNED database

20. 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.

21. 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.

22. 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.

23. 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.

24. 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.

25. 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).

26. 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

27. Pipeline | 10. Editing degree filter.  Exclude polymorphic sites with extreme degree of variation (100%): Remaining SitesFurther Analysis

28. 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).

29. 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.

30. 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.

31. 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.

32. 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.

33. 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.

34. Results| Editing Sites Distribution 50% leads to changes in coded amino acids.

35. Results|  Editing sites  Characterization Poly (A) + Poly (A) - Poly (A) + Poly (A) +,CDS Poly (A) -

36. Results| Novel Editing Sites

37. Results| Frequency of nucleotides in the flanking sequences Poly (A) + Poly (A) -

38. Results| % of Edits in Conserved Regions Poly (A) + Poly (A) -

39. 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.

40. Results | Comparison with Other Datasets

41. Results | Genes with multiple editing sites.

42. Results | RNA editing and miRNA-mediated regulation  2,474 editing sites in 3′-UTRs  Extract 6 + 1 + 6 bp sequence & search in miRBase.

43. 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?

46. RNA Editing

47. Overview  Literature Review  RNA Editing Concepts  Definition  Mechanisms  Functions  RNA Editing Site Prediction  Prediction Methods  Machine Learning Based Methods  Mapping Based Methods  Database

48. Literature Review  RNA Editing Concepts  RNA Editing Site Prediction

49. RNA Editing Concepts  Definition  Mechanisms  Functions

50. Gott & Emeson. Annu Rev Genet. 2000

51. 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).

52. 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 ; 2011 Gott & Emeson. Annu Rev Genet. 2000

53. RNA Editing | Functions E. C. Hayden, Nature 473, 432 (2011).

54. RNA Editing | Functions (Cont’d…)  mRNA Gott & Emeson. Annu Rev Genet. 2000

55. RNA Editing | Functions (Cont’d…) Maas et al. 2003

56. RNA Editing Site Prediction  Prediction Methods  Machine Learning Based Methods  Mapping Based Methods  Database

57. Machine Learning | Bundschuh 2004 Bundschuh 2004

58. Machine Learning | Bundschuh 2004 Bundschuh 2004

59. Machine Learning | Bundschuh 2004 Bundschuh 2004

60. Machine Learning | Bundschuh 2004 Bundschuh 2004

61. 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

62. Machine Learning | Clutterbuck et al. 2005 Clutterbuck et al. 2005

63. 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

64. Machine Learning | Clutterbuck et al. 2005 Clutterbuck et al. 2005

65. 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

66. Machine Learning | Clutterbuck et al. 2005 Clutterbuck et al. 2005

67. 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

68. Mapping | Kim et al. 2004 Kim et al. 2004

69. Mapping | Kim et al. 2004 Kim et al. 2004

70. 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

71. Mapping | Levanon et al. 2004 Levanon et al. 2004

72. Mapping | Levanon et al. 2004 Levanon et al. 2004

73. 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

74. 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

75. Mapping | Li et al. 2011 Li et al. 2011

76. Flowchart of Analysis Li et al. 2011

77. 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

78. Mapping | Schrider et al. 2011 Schrider et al. 2011

79. 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

80. Mapping | Bahn et al. 2011 Bahn et al. 2011

81. 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

82. Mapping | Bahn et al. 2011 Bahn et al. 2011

83. Mapping | Bahn et al. 2011 Bahn et al. 2011

84. Mapping | Bahn et al. 2011  Determine whether the DNA–RNA differences are likely authentic events or sequencing errors. Bahn et al. 2011

85. 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

86. Mapping | Carmi et al. 2011 Carmi et al. 2011

87. Mapping | Carmi et al. 2011 Carmi et al. 2011

88. 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

89. Mapping |Picardi et al. 2011 Picardi et al. 2011

90. 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/http://t.caspur.it/ExpEdit/ Picardi et al. 2011

91. Database | Kiran et al. 2010  Data from previously published papers. Kiran et al. 2010

92. 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).