Studio delle modificazioni post-trascrizionali mediante tecnologia RNA-seq

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1 Studio delle modificazioni post-trascrizionali mediante tecnologia RNA-seq Ernesto Picardi University of Bari IBIOM-CNR

2 2001 Publication of the human genome sequence Surprisingly the human genome contains about 23,000 (Ensembl GRCh38.p10) distinct protein-coding genes and other less complex species like the nematode Caenorhabditis elegans have a similar number of proteincoding genes (Frazer 2012, Genome Research).

3 Gene number is not correlated to organism complexity (something we learned from genome projects) Gene number Genome size (Mb) Number of genes in prokaryotes (up to 8000) Genome size in prokaryotes (up to 9 Mb) human mouse chicken xenopus zebrafish fugu ciona fly worm yeast

4 Explaining the paradox of gene numbers We can define the complexity of an organism as the number of theoretical transcriptome states that its genome could achieve, where the transcriptome represents the universe of transcripts for the genome. According to the simplest model, in which each gene is either ON or OFF, a genome with N genes can (theoretically) encode 2 N states. 2 23, , , ,000 = 900 However, gene expression exhibits more than two states. A trivial mathematical model can thus illustrate how a relatively small number of genes could be sufficient to generate a tremendous biological complexity. Calverie 2001 Science

5 Genetic flux of information genome Gene 1 Gene 2 24,000 23,000 transcripts X A C A B C products 200,000 X A C A B C

6 Alternative splicing expands the functional potential of encoded genes: antagonist functions of caspase 9 (CASP9) activity modulated by Alternative Splicing The constitutive form of the protein (CASP9, 9 exons, 416 aa) induces apoptosis. It contains a Caspase recruitment domain (CARD) and a Peptidase_C14 caspase domain. The shorter isoform of the protein (CASP9S, 5 exons, 266 aa) contains a Caspase recruitment domain (CARD) and a truncated Peptidase_C14 caspase domain. This isoform lacks protease activity and acts as an apoptosis inhibitor. Waltereit R, Weller M: The role of caspases 9 and 9-short (9S) in death ligand- and drug-induced apoptosis in human astrocytoma cells. Brain Res 2002, 106:42-49

7 RNA-Seq RNA-Seq refers to experimental procedures that generate sequence reads derived from the entire RNA molecule. It can be used to build a complete map of the transcriptome across all cell types, perturbations and states. Samples of interest Isolate RNAs Generate cdna, fragment, size select, add linkers Condition 1 (normal colon) Condition 2 (colon tumor) Sequence Map to genome, transcriptome, and predicted exon junctions Downstream analysis 10s of billions bases of sequence

8 RNA-Seq: Applications Gene/transcript expression ex1 ex2 ex3 Isoform reconstruction full RNA Alternative splicing detection ex1 ex2 ex3 ex1 ex3 Gene discovery gene 1 gene 2 gene x RNA editing identification * * * DNA

9 RNA editing Basic intro RNA editing is a widespread post-transcriptional molecular phenomenon that can increase the complexity of the eukaryotic transcriptome and proteome through a variety of mechanistically and evolutionarily unrelated pathways. Genomic DNA 5 3 A A A Primary mrna 5 I I I 3 5 I I I (A) n 3 Mature mrna Primary RNAs are modified at specific positions by base substitutions or insertions and deletions.

10 The impact of RNA editing on scientific community RNA editing publications since N. of publications Years Data from PubMed (last update 20/01/2018)

11 RNA editing in mammals (C-to-U) In mammals, RNA editing occurs mainly by C-to-U or A-to-I conversions.

12 RNA editing in mammals (A-to-I) A-to-I is carried out by ADAR (adenine deaminases acting on RNA) enzymes. Jepson et al., 2008, BBA Zinshteyn et al., 2009, WIREs Syst Biol Med ADAR1 and ADAR2 are expressed in almost all human tissues. ADAR3, instead, is expressed in the brain only.

13 RNA editing in mammals (A-to-I) ADAR enzymes can deaminate As included in RNA duplexes. Keegan et al., 2001, Nature Rev. Gen. Nishikura 2010 Annu. Rev. Biochem Farajollahi and Maas 2010

14 Effects of RNA editing in mammals (A-to-I) A-to-I editing can modulate the gene expression at different levels. Maas S. web site Nishikura 2010 Annu. Rev. Biochem

15 A-to-I RNA editing in the innate immune response ADAR1 is present in the nucleus (ADAR1 p110) and cytoplasm (ADAR1 p150) and can edit endogenous RNA. ADAR1 is required to edit endogenous RNA to prevent the activation of the cytosolic pattern recognition receptor MDA5 in the cytosol, leading to induction of the innate immune/interferon response. ADAR1 can also edit viral dsrna and participate in the innate immune response as a direct interferon-stimulated gene (ADAR1 p150 isoform). The absence of ADAR1 or the absence of ADAR1-mediated editing leads to innapropriate activation of the MDA5 MAVS axis. Walkley and Li 2017 Genome Biol

16 RNA editing in mammals (A-to-I) RNA editing deregulation is associated to several human diseases including neurological disorders as major depression, schizophrenia, epilepsy, amyotrophic lateral sclerosis (ALS) and cancer.

17 RNA editing: experimental detection RNA editing changes can be experimentally detected by comparing the genomic locus with the corresponding cdna sequenced by classical Sanger methodology. Li et al Editing sites (indicated by black arrows) in the amyloid beta A4 precursor protein-binding (APBA1) gene in cerebellum.

18 RNA editing: bioinformatics The detection of RNA editing in human by conventional techniques is not feasible for large-scale experiments. Therefore, many candidate events have been mainly identified by computational analyses, employing mrna/est alignments onto the genome of origin.

19 RNA editing and NGS Massive RNA sequencing can facilitate the study of entire transcriptomes as well as post-transcriptional events occurring herein as alternative splicing and RNA editing. Genome Genome Short reads ESTs/cDNAs Using NGS, each genomic position can be supported by a large number of sequences and this can greatly improve the detection of RNA editing substitutions. Few clones are sequenced by Sanger method and differences between the genome and the related cdnas are scored as RNA editing sites Each editing site is supported by a very restricted number of transcripts.

20 RNA-Seq: Experimental and Practical considerations a. Experimental Design Biological replicates b. Poly(A) enrichment or ribosomal RNA depletion? c. Single-end or Paired end? Always paired-end a. Stranded or not? Prefer Stranded b. How much sequencing data to collect? At least 50M fragments

21 RNA-Seq: analysis workflow RAW Data Quality check Read Mapping Downstream analyses Fastq file(s) FastQC Trimgalore GSNAP STAR Tophat2 RUM HISAT REDItools JACUSA GIREMI

22 RNA-Seq: quality check Per base sequence quality After sequencing After adaptor trimming and removal of low quality regions Generated by FASTQC software

23 RNA-Seq: quality check Per base sequence content After sequencing After adaptor trimming and removal of low quality regions Generated by FASTQC software

24 Reads Reads RNA-Seq: read mapping We need to align the sequence data to our genome of interest In aligning RNA-Seq data to the genome always pick a slice-aware aligner: STAR, TopHat2, MapSplice, SOAPSplice, Passion, SpliceMap, RUM, ABMapper, CRAC, GSNAP, HMMSplicer, Olego, BLAT Genome Alignment Genome Gene Versus Splice-Aware Alignment Gene

25 We can employ NGS data (RNA-Seq, genome resequencing and exome sequencing) to study RNA editing at different levels: genome/exome Vs RNA-Seq to identify new events (REDItools); RNA-Seq to explore the presence of known A-to-I conversions; RNA-Seq: RNA editing detection r1 GGGTGCCTTTATGCAGCAAGGATGCGATATT r2 GGGTGTCTTTATGCAGCAAGGATGCGATACTTCGC Exome r3 GGGTGCCTTTATGCAGCAAGGATGCGATATTTCG r4 GGGTGCCTTTATGCAGCAAGGATGCGATATTTCG r5 GGGTGCCTTTATGCAGCAAGGATGCGATATTTCG...A... gdna TGGGTGCCTTTATGCAGCAAGGATGCGATATTTCGCC...G... r1 GGGTGCCTTTATGCGGCAAGGATGCGATATT r2 GGGTGTCTTTATGCAGCAAGGATGCGATACTTCGC RNA-Seq r3 GGGTGCCTTTATGCGGCAAGGATGCGATATTTCG r4 GGGTGCCTTTATGCGGCAAGGATGCGATATTTCG r5 GGGTGCCTTTATGCGGCAAGGATGCGATATTTCG RNA-Seq to detect de novo new editing candidates; gdna AGCTGGCCAGATACATTAAGACCAGTGCTCACTATGAAG...G... r1 GCTGGCCAGATACATTGAGACCAGTGCTCAC r2 GCTGGCCAGATACATTAAGACCAGTGCTCAC r3 CTGGCCAGATACATTGAGACCAGTGCTCACTATGAAG RNA-Seq r4 CTGGCCAGATACATTGAGACCAGTGCTCACTATG r5 CTGGCCAGATACATTAAGACCAGTGCTCACTATGAAG r6 CTGGCCAGATACATTAAGACCAGTGCTCACTATGAAG r7 CTGGCCAGATACATTGGGACCAGTGCTCACTATGAAG r8 CTGGCCAGATACATTGAGACCAGTGCTCACT r9 CTGGCCAGATACATTGAGACCAGTGCTCACTATGAAG

26 REDItools REDItools are a suite of python scripts to investigate RNA editing at large-scale employing RNA-Seq as well as DNA-Seq (WGS/WES) massive data. Starting point is a BAM file of aligned reads onto the reference genome. BAM file REDItoolDnaRna.py REDItoolKnown.py REDItoolDenovo.py RNA-Seq and DNA-Seq RNA-Seq and known events RNA-Seq only Picardi and Pesole 2013 Bioinformatics

27 Workflow to call RNA editing by REDItools. RNA editing and NGS Pre-aligned DNA-Seq reads Reference genome Pre-aligned RNA-Seq reads DNA-Seq gdna RNA-Seq r1 r2 r3 r4 r5 r1 r2 r3 r4 r5 BAM file GGGTGCCTTTATGCAGCAAGGATGCGATATT GGGTGTCTTTATGCAGCAAGGATGCGATACTTCGC GGGTGCCTTTATGCAGCAAGGATGCGATATTTCG GGGTGCCTTTATGCAGCAAGGATGCGATATTTCG GGGTGCCTTTATGCAGCAAGGATGCGATATTTCG...A... GGGTGCCTTTATGCAGCAAGGATGCGATATTTCGCC...G... GGGTGCCTTTATGCGGCAAGGATGCGATATT GGGTGTCTTTATGCAGCAAGGATGCGATACTTCGC GGGTGCCTTTATGCGGCAAGGATGCGATATTTCG GGGTGCCTTTATGCGGCAAGGATGCGATATTTCG GGGTGCCTTTATGCGGCAAGGATGCGATATTTCG Reads with mismatches are checked for mis-mapping by Blat using the REDItoolBlatCorrection.py script. >r1 TATAGGGTGCCTTTATGCGGCAAGGATGCGATATT >r2 GGGTGTCTTTATGCAGCAAGGATGCGATACTTCGC A list of bad reads is printed out and used as an additional input file for REDItools. Each genomic position is explored and several filters are applied: A --> GGGGGAAGGGAAAGGGAGGAGAGTAAAAA For each read position we can recover different info as: - Read name - Position along the read - Map quality and so on Filters: Quality score > 25/30 Map quality > 40 Per base coverage > 10 Bases supporting variation > 3 Remove substitutions in homopolymeric regions > 5 bases Remove substitutions near splice sites Check Blat alignments of reads supporting the variation Use only uniquely mapping reads Use concordant paired-end reads Exclude PCR duplicates Trim few bases upstream and/or downstream of each read Use an editing background value (0.1) Exclude positions with multiple changes

28 RNA editing in human To profile RNA editing in human tissues we sequenced total RNA from 6 tissues in 3 Caucasian and non diseased Individuals (sex and age matched) using the Illumina HiSeq2500 platform. Paired end RNA-Seq reads (2x100) were generated according to the strand-oriented Illumina TruSeq kit. In addition, we produced WES and WGS (20x) reads from the same samples. ID TISSUE READ PAIRS PERCENT_DUPLICA PCT_RIBOSOMAL_ TION BASES PCT_MRNA_BASES PCT_CORREC T_STRAND_RE ADS 11 brain ,48 0, , , brain ,93 0, , , brain ,26 0, , , heart ,4 0, , , heart ,62 0, , , heart ,88 0, , , kidney ,23 0, , , kidney ,93 0, , , kidney ,18 0, , , liver ,55 0, , , liver ,53 0, , , liver ,15 0, , , lung ,8 0, , , lung ,93 0, , , lung ,53 0, , , muscle ,57 0, , , muscle ,78 0, , , muscle ,19 0, , , Picardi et al Sci. Rep. Statistics obtained by Picard on GencodeV19

29 Frequency Frequency RNA editing in human Most of the detected RNA editing events were A-to-G (>97%). Potential non canonical events were rare and showed frequency values less than in addition, the fraction of A-to-G changes in non-synonymous sites was notably high. 1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0 AC AG AT CA CG CT GA GC GT TA TC TG Observed nucleotide change BRAIN LUNG LIVER KIDNEY HEART MUSCLE Picardi et al Sci. Rep. 1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0 0,96 0,95 0,92 0,92 0,89 0,73 BRAIN KIDNEY LIVER LUNG HEART MUSCLE Tissue type

30 The Human Inosinome Classification of RNA editing sites. A) Partitioning of detected RNA editing sites in Alu elements (ALU), other repetitive regions (REP-NON-ALU) and non-repetitive regions (NON-REP). According to previous large-scale investigations, the vast majority of RNA editing sites resides in repetitive regions (97%). B) Distribution of editing events along gene structure. ALU REP-NON-ALU NON-REP UTR5 CDS intronic UTR3 ncrna intergenic A 8% 3% B 0.15% 0.05% 14% 10% 3% 73% 89% Picardi et al Sci. Rep.

31 % RNA editing events % RNA editing events The Human Inosinome To investigate the impact of RNA editing on human transcriptome, we mapped all detected events on Gencode (v19) annotations and discovered that 17,140 loci over 55,496 (31%) underwent RNA editing in their exons and/or introns. Interestingly, most of detected sites (92%) occurred in protein coding genes, modifying 13,062 loci out of 20,173 annotated (65%). Remaining A-to-I events (8%) were distributed in the non-coding RNA fraction (ncrna). RNA editing distribution in Gencode RNAs RNA editing distribution in Gencode ncrna ncrna mrna snorna processed_transcript sense_overlapping mirna sense_intronic Picardi et al Sci. Rep.

32 BRAIN_1 BRAIN_2 BRAIN_3 LUNG_1 LUNG_2 LUNG_3 LIVER_1 LIVER_2 LIVER_3 KIDNEY_1 KIDNEY_2 KIDNEY_3 HEART_1 HEART_2 HEART_3 MUSCLE_1 MUSCLE_2 MUSCLE_3 Absolute number of events x The Human Inosinome The number of detected A-to-I events varied greatly among tissues and individuals. This effect, described also in previous works, is mainly due to sequencing depth variation, stringent filters used to recover editing candidates and tissue specific roles of RNA editing. RNA Edits RNA HyperEdits Picardi et al Sci. Rep.

33 The Human Inosinome Despite the difference in the number of editing sites per sample, the distribution of RNA editing levels was quite similar across tissues and more evident within each tissue group. Picardi et al Sci. Rep.

34 The Human Inosinome We investigated the inosinome similarity across human tissues. Cluster analysis based on pairwise comparison of RNA editing levels per sample by the Spearman correlation coefficient, showed welldefined tissue segregation. Picardi et al Sci. Rep.

35 The Human Inosinome RNA editing in human is pervasive and occurs in the vast majority of mrna transcripts. From the outside: - Brain - Lung - Kidney - Liver - Heart - Muscle In red tissue exclusive eventss Picardi et al Sci. Rep.

36 Is Human Inosinome indispensable? Our genome wide screening indicates that more than 90% of RNA editing sites resides in known protein coding genes (affecting 65% of mrnas) and may profoundly affect transcriptome dynamics with a variety of functional consequences. Using our large collection of A-to-I modifications, we investigated the indispensability of RNA editing in human, exploring the relationship between inosinome and human diseases. We downloaded all known genes associated to human disorders from DisGeNET database comprising over associations between more than genes and diseases. Next, we calculated the enrichment in our set of edited protein-coding genes, if any. Surprisingly, we found that edited genes were consistently enriched in genes involved in neurological disorders and cancer. Disgenet ID GenesInDisease GenesInList Pvalue Term umls:c ,02E-05Neoplastic Process, Rhabdoid tumour of the kidney umls:c Intellectual Disability..Nervous System Diseases;Pathological Conditions, Signs and Symptoms;Behavior and Behavior Mechanisms;Mental 1,55E-06Disorders..Mental or Behavioral Dysfunction Substance-Related Disorders..Substance-Related Disorders;Mental Disorders..Mental or Behavioral umls:c ,40E-06Dysfunction Amyotrophic Lateral Sclerosis 2, Juvenile..Nervous System Diseases;Nutritional and Metabolic Diseases..Disease or umls:c ,93E-05Syndrome Tobacco Use Disorder..Substance-Related Disorders;Mental umls:c ,21E-212Disorders..Mental or Behavioral Dysfunction

37 REDIportal: The Human Inosinome We have extended RNA editing detection to 2642 RNAseq experiments from 55 body sites of GTEx Project. All events have been collected in a unique and comprehensive repository called REDIportal. Computational workfow used to load RNA editing sites in REDIportal. Raw data in Fastq format are quality checked by FASTQC and aligned onto the reference human genome by STAR. REDItools are then used to interrogate multiple read alignments using a large collection of known RNA editing sites from ATLAS repository and RADAR database. WGS data are finally included in REDItools tables and, in turn, stored in REDIportal. Picardi et al Nucleic Acids Research

38 REDIportal: The Human Inosinome Picardi et al Nucleic Acids Research

39 RNA editing in neurological disorders To evaluate RNA editing dysregulation in human neurological diseases, a total of 839 RNA- Seq experiments from 14 BioProjects were downloaded from the SRA repository. In addition, transcriptome data from 30 RNA-Seq experiments from sporadic ALS, Alzheimer and Parkinson diseases were generated in our laboratory and added to final list of samples.

40 RNA editing in neurological disorders Global RNA editing activity calculated through the Alu Editing index (AEI) and the Recoding Editing Index (REI) in Alzheimer s Disease (AD), showed for two different projects and brain area. Pvalues were calculated by the Mann-Whitney non parametric test. A) Hippocampus B) Broadmann Area 9

41 Direct RNA sequencing for RNA editing

42 Acknowledgments Lab of Bioinformatics and Comparative Genomics and at the University of Bari & IBIOM-CNR Prof. Eli Eisenberg at Tel Aviv University Dr. Erez Levanon at Bar Illan University Dr. Billy Li at Stanford University Italy Israel Actions

43 Single-cell Omics Single-cell transcriptome analyses of tissues and cell types. Cells from a healthy or pathological tissue are dissociated, analyzed independently with single-cell RNA-seq and clustered based on their gene expression profiles. Clustering of cells reveals a cell-type map that can be used to assess the composition of the tissue including the identification of new cell types or subtypes. These rich data can be used to address many questions of gene expression and regulation within or between cell types and between tissues. Bulk tissue Cell Dissociation Data Analysis NGS Library and Sequencing Darmanis et al. PNAS 2015 Cell Sorting and RNA isolation

44 RNA editing detection in single cells We have profiled RNA editing in 466 single cells of human brain cortex from living individuals, in which a transcriptomic analysis was already been completed (Darmanis et al. PNAS 2015). BAM file Picardi et al Nucleic Acids Research gdna RNA-Seq r1 r2 r3 r4 r5 GGGTGCCTTTATGCAGCAAGGATGCGATATTTCGCC...G... GGGTGCCTTTATGCGGCAAGGATGCGATATT GGGTGTCTTTATGCAGCAAGGATGCGATACTTCGC GGGTGCCTTTATGCGGCAAGGATGCGATATTTCG GGGTGCCTTTATGCGGCAAGGATGCGATATTTCG GGGTGCCTTTATGCGGCAAGGATGCGATATTTCG 4,6 million sites REDItools REDItools Picardi and Pesole 2013 Bioinformatics Known RNA editing sites

45 Frequency RNA editing in adult brain cells We found that the number of RNA editing events per cell was strongly correlated with the number of uniquely mapped reads. Strikingly, RNA editing levels (proportions of reads supporting an editing event at each known editing site) for individual cells showed a bimodal distribution with picks close to extreme values (0 and 1), a sort of all or nothing effect. Pearson correlation r : 0.85 Pvalue: 1.4 x RNA editing levels Picardi et al. RNA 2017

46 Examples of all or nothing effect RNA editing levels calculated in some neurons showing similar profiles with high frequencies near 0 and 1. Picardi et al. RNA 2017

47 Is this effect due to duplicated reads? This observation was not an artifact resulting from the presence of PCR duplicate reads, as PCR duplication was globally low (affecting on average 10% of aligned reads). Furthermore, raw and deduplicated datasets shared, on average, 95% of candidate editing sites and by position comparison of A-to-I levels showed a remarkable positive correlation (r=0.9998, P=0.0) Picardi et al. RNA 2017

48 Frequency Frequency RNA editing in adult brain cells After the removal of PCR duplicates, A-to-I editing levels of single cells continued to exhibit an extreme bimodal distribution. However, when scrnaseq reads were merged, mimicking an ensemble tissue, RNA editing levels displayed a classical unimodal distribution in which the majority of A-to-I editing levels were lower than 0.2, as previously observed in six human tissues (including brain cortex). RNA editing levels RNA editing levels RNA editing levels in three bulk tissues from Picardi et al Scientific Reports

49 RNA editing levels in LCL cells We have applied the same method to profile RNA editing in LCL cells (GM12878) from Marinov et al Genome Res. Authors sequenced total RNA extracted from 1 cell, 10 cells, 30 cells and 100 cells. RNA editing bimodality is visible in single cells and pools of 10 cells. When the number of cells increases, RNA editing distribution recapitulates the distribution of bulk tissues. The number of RNAseq reads per experiment was comparable across all samples. 1 cell 30 cells 28,678,213 28,979,309 29,424,677 26,994, cells 100 cells 45,697,590 24,736,654 26,571,449 28,696,144 Picardi et al. RNA 2017

50 Cell Type Is RNA editing cell type specific? The vast majority of RNA editing resides in Alu repetitive elements. To provide a more realistic estimate of global editing activity per cell, we calculated the Alu editing index (AEI) per cell as it represents the weighted average editing level across all expressed Alu sequences. To confirm cell specificity of RNA editing, we performed a non-metric multidimensional scaling (nmds) analysis, revealing four clusters corresponding to astrocytes, neurons, oligodendrocytes and OPCs. A B Cell Type AEI index Picardi et al. RNA 2017

51 Recoding RNA editing in single cells Recoding RNA editing sites comprise only a very limited fraction of inosinome (<1%). However, they may have profound functional consequences. We have explored RNA editing levels of 183 recoding sites in single cells. Picardi et al. RNA 2017

52 REDItools is slow for large dataset Although REDItools are designed to handle massive data, they require a lot of computational time in standard non-hpc infrastructures. In a sample of 2,600 public RNA- Seqs, REDItools took, in many cases, from 100 to 300 hours to complete a single experiment using only a core and 2GB of RAM (tests performed at INFN infrastructure in Bari comprising a server farm with 150 nodes and 4000 cores [AMD and Intel])

53 A The Human Inosinome The large number of detected RNA editing sites allowed us to investigate the sequence context flanking A-to-I changes. We observed G depletion one nucleotide downstream (-1) RNA editing sites and G enrichment one nucleotide upstream (+1) RNA editing sites. Strong avoidance of G in the first nucleotide downstream (-1) editing sites was also observed in other vertebrates and invertebrates. All genomic regions B Hyper edited regions C Non hyper edited regions

54 REDItools is slow for large dataset i-th nucleotide genome A T C C T C A A T C T T C G A T A C A C T A G C T G C T T G C A T C C T C A A T C T A C G A T A C T T C A A T C T T C G A T A C T A C T C A A T C T T C G A T A T C A T T C T T A G A C A C A C A T C T T C G A T A C A C T A G T T C G A T A C A C T A G C T A T C G A T A C A A T A G A T reads supporting the i- th nucleotide All intersecting reads are loaded all over again each time a new position is visited

55 First optimization: serial code A T C C T C A A T C T T C G A T A C A C T A G C T G C T T G C T C C T C A A T C T A C G A T A C T T C A A T C T T C G A T A C T A C T C A A T C T T C G A T A T C A T T C T T A G A C A C A C A T C T T C G A T A C A C T A G T T C G A T A C A C T A G C T A T C G A T A C A A T A G A T Position 2: only reads number 1 and 3 are loaded Position 3: only read number 2 is loaded Position 4: no new reads are loaded at all Position 5: only read number 4 is loaded and so on...

56 The Human Inosinome Hierarchical clustering analysis as well as the uneven distribution of A-to-I changes across tissues indicates that RNA editing profiles are strongly tissue dependent. This behaviour may be mainly due to tissue specific regulation of ADAR enzymes and only partially to variable RNA-Seq coverage among samples.

57 REDItools 1.0 Vs REDItools 2.0 more than 10 times faster

58 Second optimization: HPC code Differential intervals High coverage / thin intervals Low-coverage / wide intervals with approximately homogeneous computing time

59 REDItools 2.0 performance Strong scaling curve describing the behavior of the algorithm when multiple cores are used. On x-axis, we report the number of cores, while the y-axis indicates the time elapsed (in minutes) for completing the analysis using a certain number of cores. For example, by using 72 cores, the algorithm completed in 33 minutes approximately. The semi-transparent line shows the regression line interpolating data points.

60 Other tools to detect RNA editing Diroma et al Brief in Bioinf

61 Comparison in U87MG cell line Diroma et al Brief in Bioinf

62 RNA editing in fetal brain To further investigate the possibility that RNA editing profiles represent powerful signatures of cell type specificity, we analysed single fetal brain cells, since they are considerably different from any cell type in the adult brain and because RNA editing efficiency increases during brain development and, consequently, different editing patterns are expected between fetal and adult cells. Picardi et al. RNA 2017

63 Recoding RNA editing in fetal brain Notably, RNA editing activity at recoding sites was higher in adult than fetal neurons. In particular, the Q/R site in Gria2, linked to neurological disorders, was edited to high levels in fetal quiescent neurons but not in neuronal progenitors as previously assessed in vitro. Picardi et al. RNA 2017

64 RNA-Seq: RNA editing detection coverage effect Reads from GM12878 RNA-Seq (two replicates 202M and 240M of paired-end reads). From Ramaswami et al Nature Methods

65 Ramaswami et al Nature Methods RNA-Seq: RNA editing detection read mapper effect

66 The Human Inosinome We calculated the distribution of correlation values between the expression of ADARs (ADAR and ADARB1) and editing levels per each position. As background distribution we used the same dataset in which editing levels were randomly shuffled. Limiting the analysis to sites covered by at least 10 RNA reads, we interestingly found a striking and statistically significant positive correlation between ADAR expression and individual editing levels (Kolmogorov-Smirnov Pvalue=8.83* against the shuffled distribution)