Chromatin-Associated RNA sequencing (ChAR-seq) that maps all RNA-to-DNA contacts across the genome. Using Drosophila cells, we show that ChAR-seq provides unbiased, de novo identification of targets of chromatin-bound RNAs including nascent transcripts, chromosome-specific dosage compensation ncRNAs, and genome-wide trans-associated RNAs involved in co-transcriptional RNA processing.RNA is a critical component of chromatin in eukaryotes, both as a product of transcription, and as an essential constituent of ribonucleoprotein complexes that regulate both local and global chromatin states. Here, we present a proximity ligation and sequencing method called  In Drosophila, roX1 and roX2 are part of the male-specific lethal (MSL) complex that coats the single male X chromosome to acetylate histone H4K16 and increase transcription , and several studies have established that a subset of these ncRNAs form ribonucleoprotein complexes that bind and regulate chromatin . Some ofcription . In femacription . Other n  and the genomic DNA side (black) of each read were computationally split and aligned to the transcriptome and genome. After post-processing for unique alignments, repeat removal, and removal of blacklisted regions, each RNA molecule was mapped to the genomic location to which it was ligated , suggesting the presence of unannotated ncRNAs in this region. The remaining chromatin-associated RNA detected in our assay arose from non-protein coding transcripts  of the RNAs captured in our assay were sense, with the largest single subtype represented by sense-stranded mRNA , owing to the capture of nascent transcripts . Most ofipts see, of whicHsromega, chinmo, ten-m). In the second class, RNAs were found bound to chromatin in trans, generally distributed across most or all of the genome, often in addition to a peak around the gene body from which the RNA is transcribed . In a third class, RNAs that are part of the dosage compensation complex (see roX1 and roX2) were enriched on and coat the X chromosome. To investigate this first class of RNAs, we compared aggregated RNA-to-DNA contacts with data from nascent transcription sequencing using PRO-seq (see PRO-seq and mRNA). Nevertheless, many RNA-to-DNA contacts in our dataset are associated in trans to genomic regions outside of the gene body from which the RNA is transcribed  from the mock-treated sample (bottom). Topologically associated domains (TADs) are preserved in the mock-treated sample and the DNA-DNA contacts show high correlation with previously published CME-W1-cl8+ cell Hi-C  . Furthermore, RNAs found at sites from which they are transcribed clustered tightly along the diagonal, a feature most pronounced for mRNAs (class I)  which largely functions co-transcriptionally  . Many ofclass I) . One of tionally .To identify RNAs that are highly enriched for chromatin interactions, we plotted the normalized cumulative distribution of the number of sense contacts observed for each gene . The majroX1 and roX2 would serve as an internal positive control. Both roX1 and roX2 are part of the MSL2 complex, which binds across the X-chromosome in male flies to recruit chromatin-modifiers that increase transcriptional output  and 8.1-fold  enriched for interactions on the X chromosome, respectively , which binds to msl2 mRNA to prevent its translation, blocking assembly of the MSL2 complex .We developed ChAR-seq using the male WME-cl8+ line, reasoning that the ncRNAs l output  , and ChAR-seq roX2 SNR was about two-fold higher than that of ChIRP-seq (45.2 vs 22.0), despite having 2 orders of magnitude fewer reads in ChAR-seq. Altogether, these data suggest that ChAR-seq has excellent sensitivity and sufficient signal-to-noise to characterize accurate chromatin-binding events for individual RNAs.To more quantitatively compare ChAR-seq to ChIRP-seq, we compared the signal-to-noise ratio (SNR) for ch assay . We defiroX1 and roX2 , putative enhancers (blue dashed lines) and a random distribution of genomic bins of similar size (black lines). We observed an enrichment of snRNAs , but not roX1 or roX2, over gene bodies  enriched at TAD boundaries  and a random distribution of identically sized genomic bins (black lines), and found that these RNAs were ~1.4 fold enriched over boundaries. We also noted that roX2 was ~2.2 fold enriched over TAD boundaries on the X chromosome  or heterochromatin . Previous work has shown that the snRNA:U5:38Aba variant /σ) based on the whole-genome mean (μ) and standard deviation (σ). Similarly, we re-binned the modENCODE tracks, removed bins that did not contain a DpnII site, and transformed the log2 mean-shift values to a z-score. We then calculated the pair-wise Pearson correlation coefficients between each signal track, and then clustered the data . We obseH3K36me3 , and JILH3K36me3 , validatH3K36me3 . Beyond H3K36me3 . In part variant . The difroX1 and roX2 associated with dosage compensation. The comparison between ChAR-seq and ChIRP-seq, which vary dramatically in the sequencing depth needed to analyze a specific RNA, highlights the utility of ChAR-seq as a de novo chromatin-associated RNA discovery tool. ChAR-seq also maps nascent RNAs found at the loci from which they are transcribed.ChAR-seq maps the chromosomal binding sites of all chromatin-associated RNAs, independent of whether they are associated as nascent transcripts or bound as part of ribonucleoprotein complexes (RNPs). In this way, ChAR-seq can be thought of as a massively parallelized de novo RNA mapping assay capable of generating hundreds to thousands of RNA-binding maps. ChAR-seq also detects multiple classes of chromatin-associated RNAs. ChAR-seq preserves the three-dimensional structure of the genome and provides an RNA-DNA interaction matrix that complements DNA-DNA proximity measurements using Hi-C. We validated ChAR-seq using chromosome-specific ncRNAs ChAR-seq is similar to a recently published method , but hasroX2 binding to chrX in Drosophila , splicing  and RNA processing . Interestingly, more than half of the chromatin-associated RNAs identified based on our enrichment criteria are snoRNAs, most of which––but not all––correlate with heterochromatin. Generally, snoRNA ribonucleoproteins (snoRNPs) use intermolecular base pairing to direct chemical modification of the 2'-hydroxyl groups or the isomerization of uridines to pseudouridine , all of which will be particularly useful in comparing ‘epigenomic’ changes that coincide with cellular differentiation or tumorigenesis.Drosophila melanogaster CME-W1-cl8+ cells  were grown in T-75 flasks at 27°C in Shields and Sang M3 media supplemented with 5 µg/mL insulin, 2% FBS, 2% fly extract and 100 µg/mL Pen-Strep  to an oligonucleotide 'bridge' molecule containing a 5'-adenylated ssDNA overhang. The RNA ligase was inactivated, the pellet was washed and the RNA strand was stabilized by first strand synthesis of the RNA through extension of the bridge by Bst 3.0 polymerase. The polymerase was inactivated and the pellet was washed. Genomic DNA was then digested with DpnII, followed by ligation of the DpnII digested genomic DNA to the opposite end of the oligonucleotide 'bridge'. Second strand synthesis was then performed using RNaseH and DNA Polymerase I to complete cDNA synthesis of the RNA-encoded side of the new, chimeric molecules. The sample was then deproteinized and crosslinks were reversed by heating overnight in SDS and proteinase K. DNA was then ethanol precipitated and sheared to ~200 bp fragments using a Covaris focused ultra-sonicator. DNA fragments containing the biotinylated bridge were then purified using magnetic streptavidin-coated beads. DNA ends were repaired using the NEBNext End Repair and dA tailing module, and ligated to NEBNext hairpin adaptors for Illumina sequencing. The adaptor hairpin was cleaved using USER, and DNA fragments were amplified by ~8–12 rounds of PCR with NEBNext Indexing Primers for Illumina (TruSeq compatible). The partially amplified library was then purified using AMPure XP beads to remove adaptor dimers, and the optimum number of additional PCR cycles was determined by qPCR to achieve approximately 30% saturation. Library amplification was then completed by the additional rounds of PCR, and the library was purified and size selected to a target range of 100–500 bps using AMPure XP beads. The size distribution of the library was checked by capillary electrophoresis using an Agilent Bioanalyzer, and quantified using qPCR against a phiX Illumina library standard curve. Libraries were sequenced using the Illumina MiSeq platform for quality control, and subsequently sequenced on the Illumina NextSeq platform  using single-end 152 bp reads. Data were processed and analyzed using a custom pipeline.19.6 g S and S M3 powder (Sigma)0.25 g bicarb5 µg/mL insulin2% FBS2% fly extract1x Pen/StrepSterile filter media using 0.2 um filter. Store media at 4C. Warm to room temperature before splitting cells. Culture cells at 27C in 75 flasks (1–2 flasks per library), splitting or harvesting approximately every 3–4 days. Cells are detached from the flask surface by vigorously pipetting up and down with a serological pipette. Confirm that >95% of cells are healthy and viable using trypan blue assay at each harvest.All buffers and solutions prepared with DEPC-treated water. All buffers are made with fresh, unopened chemicals to minimize RNase contamination. All enzyme stocks and reagents are dedicated for RNA work and kept RNase-free. Optional steps are included for the three alternative protocols used for (A) the RNA spike-in experiment, (B) the RNase control, and (C) the Hi-C/Mock-ChAR experiment.Pipette cells up and down to detach them from the surface. The cells grow in small strings or clumps that are very weakly attached to the surface.Count the cell using a hemocytometer. Typically, each T75 flask should yield between 100–150 M cells.Spin (~2000 x g) the cells in either one or two 50 µL conical tubes in a swinging bucket centrifuge for 5 min.Resuspend the cells in ~36 mL of fresh mediaAdd formaldehyde to 1% (1 mL of 37% form)incubate at RT for 10 min mixing end over endQuench by adding 3 mL of 2.5 M glycine , mix for 5 min end over endSpin the cells at 2000 x g for 5 minResuspend in ~1 mL of sterile filtered, ice cold PBS + DEPCcount cells and split into an appropriate number of tubes to aliquot 100 M cells per tubeSpin again at 2000 x g for 5 minRemove supernatant and flash freeze in liquid nitrogenNOTE: we find it useful to remove the supernatant in this and all subsequent steps using a mechanical micropipette rather than an aspirator, which risks loss of sample.10 mM TrisHCl pH 810 mM NaCl0.2% igepal1 mM DTT0.2 mM EDTAMix 750 µL lysis buffer +150 µL Sigma protease inhibitor +45 µL RNaseOut per sample.DO NOT let cells thaw on ice before adding lysis buffer!Add 400 µL lysis buffer plus inhibitors to STILL FROZEN crosslinked cells and thaw with gentle mixing. Incubate for 2 min on ice after thawing is complete. Centrifuge at 2.5 k x g for 2 min, discard supernatant.Wash pellet with 500 µL of lysis buffer +inhibitors, spin again for 1 min.Gently resuspend the pellet in 100 µL 0.5% SDS and incubate at 62C for 5 minPre-mix 290 µL water +50 µL 10% Triton-X 100 per sample.Quench SDS with diluted Triton-X 100. Mix well by gentle pipetting; incubate at 37C for 15 min.Add 11 µL 10x T4 RNA ligase buffer for final concentration 0.25xexactly4min, 70’CIncubate for Snap cool on iceNote: Users may want to optimize fragmentation beforehand if using a different cell line or sample. Estimated RNA release is 65 µg / 250 M cellsFor 180 M cells, spike in 5.6, 0.56 and 0.056 µg1.86 µL each of 100%, 10% and 1% RNA stocks of MBP RNA1.55 µL each of 100%, 10% and 1% RNA stocks of GFP RNA1.55 µL each of 100%, 10% and 1% RNA stocks of Halo RNAThese are designated as ‘high’, ‘med’ and ‘low’Incubate on ice for 5 minAdd 1000 µL DEPC-treated PBS and incubate on ice for 2 minCentrifuge at 2.5 k x g for 2 min, discard supernatantAdd 1000 µL DEPC-treated PBS, mix gentlyCentrifuge at 2.5 k x g for 2 min, discard supernatantImmediately proceed to the next step, with pre-mixed reaction buffer already preparedPre-mix and resuspend the pellet in the following160 µL water20 µL 10x T4 RNA ligase buffer10 µL 10 mg/mL RNaseA10 µL RNaseH (NEB)Incubate at 37C for 4 hrCentrifuge at 2.5 k x g for 2 min, discard supernatantAdd 1000 µL DEPC-treated PBS, mix gentlyCentrifuge at 2.5 k x g for 2 min, discard supernatantImmediately proceed to the next step, with pre-mixed reaction buffer already preparedPre-mix the following and then add to cells20 µL 10X T4 trKQ RNA ligase buffer100 µL 50% PEG20 µL RNaseOUT20 µL 50 uM annealed bridge (2.5 nmols)10 µL T4 trKQ RNA ligase (4000 units)Incubate at 23C overnight, shaking 900 rpm in ThermoMixerPre-mix the following and then add to cells20 µL 10X T4 trKQ RNA ligase buffer100 µL 50% PEG20 µL RNaseOUT30 µL waterIncubate at 23C overnight, shaking 900 rpm in ThermoMixerEnd day 1-----------------------------------------Start day 2Centrifuge at 2.5 k x g for 4 min at RT, remove and discard supernatantResuspend pellet with 1 mL DEPC-treated PBSCentrifuge at 2.5 k x g for 5 min at room temperatureWhile spinning, make 200 µL 1x T4 trKQ RNA ligase buffer, including:0.2 µL 1 M DTT10 µL RNaseOUTResuspend pellet in 200 µL 1x T4 trKQ RNA ligase buffer +DTT and RNaseOUTAdd 20 µL 10 mM (each) dNTP mixAdd 15 µL Bst 3.0 (120 U/ µL) for 1800 UnitsIncubate for 10 min at RTIncrease temp to 37°C for 10 min (shaking 900 rpm in ThermoMixer)Increase temp again to 50°C for 20 minAdd 1000 µL DEPC-treated PBS, mix, and cool on ice 1 minAdd 35 µL PBS Incubate for 10 min at RTIncrease temp to 37°C for 10 min (shaking 900 rpm in ThermoMixer)Increase temp again to 50°C for 20 minAdd 1000 µL DEPC-treated PBS, mix, and cool on ice 1 minCentrifuge at 2.5 k x g for 2 min, discard supernatantRepeat wash with another 1 mL of DEPC-treated PBSCentrifuge at 2.5 k x g for 2 min, discard supernatantResuspend pellet in 100 µL 0.5% SDS +0.1 mM EDTAHeat inactivate for 10 min at 55Cpre-mixed), per sample.Quench SDS with 290 µL water + 50 µL 10% Triton-X 100  + 10 µL RNaseOUT (pre-mixed).Add 15 µL DpnII (750 Units)Incubate at 37°C for at least 3 hr, up to overnight if desired.Terminate digestion by adding 7.5 µL 0.5 M EDTA to a final concentration of ~15 mMSpin the cells at 2500 x g for 2 minRemove and discard the supernatantRe-suspend the X-linked cells with 1 mL DEPC-treated PBS,Spin again at 2500 x g for 2 minRemove and discard the supernatant using a P200Re-suspend the washed cells in 100 µL 0.5% SDS +0.1 mM EDTAIncubate at 55°C for 10 minQuench SDS by adding 290 µL water + 50 µL 10% Triton-X 100Incubate at 37°C for 5 minAdd 1 mL of PBS and incubate on ice for 5–10 minSpin the cells at 2500 x g for 2 min, discard supernatantRe-suspend the pellet with 1 mL DEPC-treated PBSSpin again at 2500 x g for 2 min, discard supernatantRe-suspend the pellet with 1 mL DEPC-treated PBSSpin again at 2500 x g for 2 min, discard supernatantPerform washes as in Step 12 abovePre-mix the following:64 µL DEPC water10 µL 10X T4 RNA ligase buffer10 µL 0.4 mM biotin-dATP4 µL 1 mM dCTP4 µL 1 mM dGTP4 µL 1 mM dTTP4 µL Klenow (exo minus)Add pre-mixed biotinylation solution to samplesIncubate at 23°C for 3–4 hrAdd 4 µL 500 mM EDTAHeat inactivate at 60°C for 10 minSpin the cells at 2500 x g for 2 minRemove and discard the supernatantRe-suspend the pellet with 1 mL DEPC-treated PBSSpin again at 2500 x g for 2 minResuspend pellet in the following pre-mixed solution:140 µL DEPC-treated water20 µL RNaseOUTT4 DNA ligase buffer20 µL T4 DNA ligase (8000 Units)20 µL reaction requires ATP and this is different from the buffer used throughout the earlier steps, if you use T4 RNA ligase buffer, you must supplement the final concentration with 1 mM ATPNOTE : this Incubate at 16C overnightEnd day 2-------------------------------------------Start day 3** Allow Hi-C ligation to proceed during second strand synthesis, so skip this step for Hi-C sample **Add 7.5 µL 0.5 M EDTA Spin the cells at 2500 x g for 2 minRemove and discard the supernatantRe-suspend the pellet with 1 mL DEPC-treated PBSSpin the cells at 2500 x g for 2 minRemove and discard the supernatantRe-suspend each pellet in the following pre-mixed solution:80 µL DEPC-treated water10 µL 10x T4 RNA ligase buffer1 µL 1 M DTT10 µL RNaseOUT2x cDNA bufffer10 mM TrisHCl (8.0)180 mM KCl4)2SO4100 mM (NHAdd 100 µL 2x cDNA bufferAdd 20 µL 10 mM dNTP (each) mixE. coli DNA Polymerase I (NEB)Add 100 Units Add 5 Units RNaseH (NEB)Mix and incubate at 16C for at least two hoursCentrifuge at 2.5 k x g for 5 minRemove and discard supernatantResuspend pellet in 400 µL DEPC-treated PBSTo ~450 µL of sample, add55 µL 10% SDS55 µL 5 M NaCl3 µL 20 mg/mL Proteinase Kincubate overnight at 70°CEnd day 3-------------------------------------------Start day 4room temperatureCool sample to NOTE: Do not cool the sample on ice as the SDS will precipitateAdd 50 µL 3 M NaOAcAdd 2 µL (5 mg/ml) glycogenAdd 850 µL ice cold 100% EtOH.NOTE: Flocculent should be visible after inverting the tube gently 3–4 times. If no flocculent is visible, add 200 µL of additional ice cold 100% EtOHIncubate on ice for 30 minCentrifuge at ~21,000 x g for 5–10 minWash pellet with 70% ice cold EtOHCentrifuge at ~21,000 x g for 5 minGently dry pellet and resuspend in 130 µL TEShear DNA to ~200 bp fragments using the following settings: 10% duty factor, 175 peak incident power, 200 cycles per burst, for 180 sNOTE: shearing of Hi-C sample should target 300–500 bp fragments… make appropriate adjustments for AMPure steps1X tween wash buffer (TWB)5 mM TrisHCl (7.5)0.5 mM EDTA1 M NaCl0.05% Tween 202X bead binding buffer (BBB)10 mM TrisHCl (7.5)1 mM EDTA2 M NaClWash 150 µL Dynabeads MyOne SA T1 beads (Life Tech) with 400 µL TWB. Pull down beads with a magnetic tube rack and discard supernatant.Resuspend the beads in 130 µL 2X BBBMix the bead slurry with the DNA Incubate at RT for 15 min with rotation to bind biotinylated DNAPull down beads with a magnetic tube rack and discard supernatant.NOTE: Do not allow the beads to dry outWash the beads by adding 750 µL 1x TWB and mixingWarm the tubes to 50°C for 2 minPull down beads with a magnetic tube rack and discard supernatantWash the beads by adding 750 µL 1x TWB and mixingPull down beads with a magnetic tube rack and discard supernatantResuspend the beads in 40 µL TEadd 7 µL NEB Next End Prep Bufferadd 3 µL NEB Next End Prep Enzyme MixIncubate at RT for 20 min with agitation to keep beads suspendedIncrease temp to 65°C for 30 minCool to room temperatureNOTE: The following order of addition is importantFIRSTadd 2.5 µL NEBNext Adaptor add 1 µL ligation enhancerLASTadd 30 µL NEBNext UltraII Ligation Master Mix Room Temperature for 15–20 min with agitation to keep beads suspendedmix vigorously, incubate at add 3 µL USER enzymeincubate at 37°C for 15 minPull down beads with a magnetic tube rack and discard supernatantWash the beads by adding 750 µL 1x TWB and mixingWarm the tubes to 50°C for 2 minPull down beads with a magnetic tube rack and discard supernatantWash the beads by adding 750 µL 1x TWB and mixingPull down beads with a magnetic tube rack and discard supernatantFor each index, pre-mix the following30 µL 2x NEB Next High Fidelity master mix3 µL 10 uM Universal Primer (NEBNext Multiplex Oligos for Illumina)3 µL 10 uM Indexing Primer (NEBNext Multiplex Oligos for Illumina)24 µL waterstep 25)Resuspend the beads in 50 µL amplification mix , pull down, remove sup, andelute with 30 µL of 10 mM Tris pH 8step 23Mix 5 µL of each library eluted from the AMPure beads with 10 µL of the reserved PCR mix from Dilute 100x Syber Green 1:3 in water for a 33x working solutionAdd 0.5 µL to each 15 µL reaction for ~1xRun on qPCR to determine the number of cycles to amplify… target should be the number of cycles required to reach ~1/3 of the saturationFor each index, pre-mix the following30 µL 2x NEB Next High Fidelity master mixStep 2425 µL eluted library from 2.5 µL 10 uM Universal Primer (NEBNext Multiplex Oligos for Illumina)2.5 µL 10 uM Indexing Primer (NEBNext Multiplex Oligos for Illumina)Run the following PCR cycle98°C for 30 s63°C for 30 s72C for 40 s98C for 3 minthen N cycles ofNOTE: the number of additional cycles, N, is ascertained by estimating the number of additional cycles required to reach 1/3 saturation from the qPCR analysis in Step 24. The number of cycles will vary between each sample library.Allow AMPure bead slurry to warm to room temperatureAdd 0.5 volumes of AMPure bead slurry (30 µL) to PCR mix (60 µL). Mix by pipetting and incubate at RT for 5 min.Pull down beads with magnet, give 1–2 min to clear solution, then recover the supernatant. This should contain fragments from 0 to 500 bp. Larger fragments, if you want them, are bound to the beads. Add 70% EtOH to the beads and set aside.To the supernatant (90 µL), add 0.5 volumes of original volume (30 µL) for a final bead to input ratio of 1:1 . Mix and incubate at RT for 5 min.Pull down the beads and remove the supernatant, which should contain small, unwanted fragments . Nonetheless, save it for now just in case and set aside.Wash the beads with 700 µL 70% EtOH, pull down the beads in a magnetic tube rack until clear (1–2 min), discard supernatant, and repeat for a total of two washes. Do not disrupt the beads during the second wash.Remove the EtOH completely and let the beads passively dry for ~5 min20 mM Tris pH 8). Incubate for 2–3 min. Pull down the beads until clear and remove the supernatant. This should contain fragments from ~100–500 bp.Add 50 µL of elution buffer Quantify library by phiX qPCRChAR-seq libraries were sequenced with single-end 152 bp reads on the Illumina MiSeq and NextSeq according to manufacturer’s instructions.https://github.com/straightlab/flypipe (https://github.com/elifesciences-publications/flypipe). PCR duplicates were removed using Super Deduper , repetitive regions downloaded from the UCSC Table Browser (https://genome.ucsc.edu/cgi-bin/hgTables) using the following selection parameters: clade: Insect, genome: D. melanogaster, assembly: Apr. 2006 (BDGP R5/dm3), group: Variation and Repeats, track: Repeatmasker. Several RNAs that were re-annotated as rRNA in later Drosophila genome annotation versions were also removed.Previously linked RNA and DNA sequences with sense alignments had their information re-associated using the original read ID. These 1-to-1 links are deemed ‘RNA-DNA contacts’. Any RNA-DNA contact that contained Ribosomal RNAs (rRNA) were removed from further analysis. Additional processing was then performed to remove mapped DNA contacts that overlapped regions of poor mappabilty using the modEncode Drosophila blacklist  . Coverage was calculated using BEDtools for 200 bp windows, and the number of contacts was then normalized by dividing by the number of DpnII sites for each window throughout the genome. Coverage tracks for the metagene profile analysis in https://github.com/deeptools/HiCExplorer/blob/master/docs/index.rst). Matrices were built at 10 kb resolution. Data from GEO:GSE58821 were downloaded, re-aligned and processed using HiCExplorer in parallel. Plotting, TAD calling, and matrix correlation were all performed in HiCExplorer. Plots showing RNA-DNA and DNA-DNA contacts were also generated using HiCExplorer, where RNA-DNA matrices were also built at 10 kb resolution.HiC libraries (‘Hi-C/Mock-ChAR’) were performed as indicated in the methods (blue text indicates branch points in the protocol). Hi-C libraries were sequenced using 2 × 75 paired end reads and were aligned to blacklisted and repeatmasked genome using the ‘local’ and ‘reorder’ flags. The subsequent bam files were used to build the contact matrices using HiCExplorer . TAD midpoints were calculated and rounded down to the nearest multiple of 10 kb, and the midpoint region was set as 10 kb to either side of the midpoint. Both boundary and midpoint region edges therefore align with 10 kb bins used for TAD calling. Regions overlapping the modENCODE blacklist or our manually curated blacklist were removed from analysis. ChAR-seq contacts were filtered to remove cis contacts, defined here as the RNA parent gene locus ±2 kb. Counts of DpnII sites, ATAC-seq insertion sites  and ChAR-seq DNA contacts within regions were calculated using the coverage or map commands in bedtools (version 2.20.1). The number of base pairs in each region (midpoint or boundary) masked by our repeat masking blacklist was calculated using bedtools coverage and groupby commands. Subsequent analysis was done with R version 3.3.1. To match the distributions of masked base pairs per region between TAD midpoints and boundaries, 20 kb region masked lengths were divided into bins of 50 bp between 0 and 1000 bp, and 500 bp between 1000 bp and 20000 bp. The boundaries and midpoints were divided into these masked length bins; within each bin, boundaries and midpoints were subsampled to the minimum number of regions for the two region types  The bin matching was verified with a Wilcoxon rank sum test of the total length of repeat masked regions in each region, with an expected non-significant p-value of 0.76. ChAR-seq DNA contact counts for each RNA type within each region were normalized by dividing the number of counts in the region by the number of DpnII sites in the region. https://github.com/fidelram/deepTools) to calculate the correlation between coverage tracks using the multiBigWigSummary tool excluding the following chromosome regions: chrU, chrUextra, ChrYHet and chrM. Coverage was calculated using 100 kb bins unless indicated. The Pearson correlation coefficients were then clustered and plotted using the either the PlotCorrelation function in deepTools (snRNA clustering) or the heatplot function in R using the ward.D2 method and Euclidian distance function (modENCODE vs ChAR-seq correlation clustering). modENCODE datasets  for CME-W1-cl8+ were downloaded from data.modencode.org, and then re-binned into 2 kb windows, and filtered to remove modENCODE blacklist sites and repeat-regions, chrYHet, chrM, chrU and any bins lacking DpnII sites. modENCODE tracks were then transformed into z-scores by dividing the mean shifted  by the genome-wide standard deviation of the bin depth. Metaplots were generated using the computeMatrix and plotProfile tools with a 2 kb window up and downstream of each region. Signal was then normalized to fold-change relative to mean of the random signal and re-ploted in GraphPad Prism.We used deepTools  (Drosophila melanogaster genome (dm3) was divided into equally sized windows for a range of distinct window sizes (50 bp to 1 MB). Read counts per window were obtained using BEDtools. ChIRP-seq and ChAR-seq data were filtered identically to remove modENCODE blacklist sites and repeat-regions, chrYHet, chrM, and any bins lacking DpnII sites. roX1 and roX2 origins were excluded due to extreme read pileups in ChIRP-seq data (ChIRP-seq binding profiles for erge.bw) . The Droseq data . SpearmaroX RNA contacts on the autosomes as ‘noise’, and dense chrX contacts as ‘signal’. Due to the limited number of reads for individual RNAs and the insufficient resolution afforded by DpnII cut-site locations, we were unable call narrow (<1 kb) peaks for roX1 and roX2 ChAR-seq reads using standard methods . Therefore, to calculate signal, we divided the genome into 2 kb bins and counted the number of roX1 or roX2 RNA-DNA contacts per bin for the 300 chrX bins containing the most contacts. ChAR-seq reads were normalized to DpnII cut sites within each bin. To define noise, we randomly selected 300 equal sized bins from autosomes. The mean RNA-DNA contact count per bin on chrX peaks  was divided by the mean RNA-DNA contact count per bin on autosomes (‘noise’) to produce the SNR value. To avoid complications due to extreme bin values or repetitive regions, we filtered for chr4, chrM, chrY, chr2Het, chr3Het, chrXHet, chrU, and modEncode blacklist regions (REF:modencode). The roX1 and roX2 loci on chrX were removed due to extreme values for ChIRP-seq data  analysis for ChAR-seq and ChIRP-seq data was performed similar to the approach in seq data . For conroX2 locus were designated as roX2 RNAs, and the associated DNA coordinate as the roX2 DNA-contact. For consistency with the other data sets, GRID-seq reads were filtered for repeats and small chromosome scaffolds identically to the ChAR- and ChIRP-seq filtering applied in the SNR analysis. We divided the genome into 2 kb bins and counted the number of roX2 DNA-contacts in each bin, as above for ChAR- and ChIRP-seq. Restriction enzyme density can bias the number of reads within a given bin, so we normalized the raw GRID-seq read count by dividing the reads-per-bin by the number of AluI RE sites-per-bin (AluI cut map generated with hicup(version 0.5.2). This signal was then calculated for 20 kb bins across chrX, and Spearman’s rank correlation performed pairwise among the three methods.ChAR-seq and ChIRP-seq roX2 DNA contact signal was processed as in the SNR analysis. GRID-seq RNA and DNA raw reads were download from the NCBI SRA database (accessions: SRX1824449 and SRX1824449) aligned to the genome using bowtie2 (version 2.3.4.1) with the parameter --local, and minimum scoring threshold set to G,1,10. GRID-seq S2 cell Replicates 1 and 2 were merged after mapping. Uniquely mapping RNA reads with uniquely mapping DNA mates (samtools flag -q2) were preserved. RNA reads aligning to the For total RNA-seq analysis, total RNA from 10 to 20 million CME-W1-cl8+ cells was purified using TriPure, then treated with 10 units of TURBO DNase (Life Technologies) at 37°C for 30 min according to the manufacturer’s instructions. RNA was re-purified with TriPure, resuspended in DEPC-treated water, and quality checked by Bioanalyzer (Agilent). Ribosomal RNAs were depleted using the Ribo-Zero rRNA removal kit (Illumina), RNA was purified using Agencourt AMPure XP beads (Beckman Coulter), then cDNAs were generated, amplified, and indexed with the ScriptSeq v2 RNA-Seq Library Preparation Kit (Epicentre) according to manufacturer’s instructions. Indexed libraries were quantified by Bioanalyzer and qPCR, pooled, and sequenced on a NextSeq 500 (Illumina).ATAC-seq using 250 K-500K CME-W1-cl8+ cells per reaction was performed with the Nextera DNA Library Prep Kit . The reaction protocol was as previously described , but witDrosophila melanogaster genome (dm3) using bowtie2 (version 2.2.5) with the parameter -X 2000. Duplicates were removed with Picard; mitochondrial reads or reads with bowtie2 MAPQ score <30 were removed using SAMtools. All replicates had high similarity so their alignment files were merged to increase library complexity before analysis with ChAR-seq data.Six technical replicates were sequenced with 75 bp paired-end reads on the Illumina Next-seq. Illumina Nextera Adapters were removed using a custom Python script. Reads were aligned to the  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been favorably evaluated by Kevin Struhl (Senior Editor) and three reviewers, one of whom, Job Dekker (Reviewer #1), is a member of our Board of Reviewing Editors.Thank you for submitting your article "Chromatin-associated RNA sequencing (ChAR-seq) maps genome-wide RNA-to-DNA contacts" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:This is an interesting manuscript describing a new approach for mapping RNA-chromatin interactions genome-wide. It has the potential to be a major improvement over existing methods. Three patterns of RNAs linked to chromatin are identified in flies: 1) those of nascent transcript next to their genes, 2) RNAs in trans across all genome, and 3) those on X chromosome for male cells. The method could be an important approach for the study of how RNA modulates the genome and is therefore of interest to many. Several issues related to reproducibility, sensitivity and specificity need to be addressed, as outlined below.Essential revisions:The authors need to address the following main points:1) Compare CharSeq performance in more detail to previously published MARGI and ChIRP-Seq methods: what are the key difference, improvements? Can you quantify this?2) Can you add metrics describing sensitivity and specificity of the method?3) Please clarify concerns about how many times experiments were performed and add details on statistical analysis of reproducibility of data and analyses.4) Can you make the results available in a more user-friendly way, i.e. make the results available as a list of RNAs and loci they associate with.5) Explore RNAs interacting with heterochromatin, rDNA etc. These are repetitive DNA sequences, but a meta-analysis of RNAs associating with defined repeats is of considerable interest and would enhance the paper.Reviewer #1:This is an interesting manuscript describing a new approach for mapping RNA-chromatin interactions genome-wide. It has the potential to be a major improvement over existing methods. I missed a more deep quantification of background, sensitivity and signal to noise ratios and how these relate to expression levels.Reviewer #2:Drosophila melanogaster CME-Wl-cl8+  wing disc cells. Some work is also done using female Kc167 cells. Three patterns of RNAs linked to chromatin are identified: 1) those of nascent transcript next to their genes, 2) RNAs in trans across all genome, and 3) those on X chromosome for its inactivation. This last group, as expected, does not show up in the female cell line. The data obtained via ChAR-seq are in agreement with those obtained by Quinn et al., 2014 using ChIRP-seq. The ChAR-seq appears as a more sensitive method because it captures all RNA-DNA contacts. Authors also use their data to provide novel insights about the function of the various RNAs that they found to have abundant association with chromatin. The application of ChRA-seq in this study have revealed many snoRNA molecules interacting with chromatin in the heterochromatin form.The manuscript by Bell et al., presents a sequencing method to map all RNA-chromatin/DNA contacts in a genome: Chromatin-Associated RNA sequencing (ChAR-seq) a proximity ligation and sequencing method. The approach is utilized mainly to examine what RNAs are interacting with chromatin in Drosophila male cells but not in the female cells. In addition to the similarity of the ChRA-seq approach to the ChIRP-seq by Quinn et al., Nat Biotech 2014, which although is limited to RNA-chromatin interactions, as pointed out by the Authors, ChRA-seq is almost identical to the method used in Sridhar et al., 2017in vivo. Thus, the novelty of the study by Bell et al. is in part reduced because of this recent publication, which exploits a very similar ligation method to capture sites of RNA interaction with chromatin and DNA.1) Overall, the authors show that ChRA-seq works well to identify both RNAs that are nascent and thus still connected with DNA in cis, and RNAs that do not directly interact with DNA, but rather with chromatin factors in trans. The ChRA-seq approach is validated with the RNAs that are known to be functioning in chr. X dosage compensation, in fact these RNAs (roX1 and roX2) are found to bind chr. X chromatin in the Other points:2) What is described in the first paragraph of Results does not match perfectly with Figure 1A, because the RT step in the figure is shown after the bridge is ligated to DNA, while in the text it is described before the bridge is ligated to DNA. The text should match the figure description.3) It would be good to add the polarity of the RNA molecule in red in the Figure 1A. It would be good also to explain that the approach captures the 3' RNA ends of RNA molecules. Also Figure 1A should be bigger to clearly show which RNA end is ligated to the bridge, to clearly indicate the polarity of ends and the structure at the junction. If the App has a free 3' end, and this is ligated to 3' end RNA, is this a 3'-3' ligation? Or is the App removed in the ligation process? This should be explained well because it is not evident.4) The statement "the 5'-adenylated end (5'App) enables increased ligation specificity for 3'-terminated ssRNA" is not clear: 'increased specificity' relative to what?5) Is the sequence of RNA and DNA oligos presented in Figure 1—figure supplement 2 provided? It is not clear what the molar excess of DNA over RNA is, please clarify. Also the legend of this figure should explain what is shown in every lane. In lane 1 there is a high mw band, what is this, what is its size? The ladder sizes are provided only for the bottom part of the gel, they should be provided also for the top part, considering that there is a high mw band. How many times was this experiment repeated? Is there any statistical analysis of these data? This, possibly, should be explained in the legend.6) The statement: "This RNase-treatment dramatically reduced in the number of bridge molecules identified, demonstrating that bridge ligation is indeed RNA-dependent " is not strongly supported by the data shown in this figure. In addition to a typo in this statement, looking at the figure, there seems to be a factor of six reduction, which does not represent a 'dramatic' reduction. Importantly, there appears to be no repeats of this experiment and no statistical analysis of the data. Was this experiment reproducible, are there repeats that can be shown?7) Legend to Figure 1E has a typo: "Zoomed in region of shown".8) Typo in Results: "we performed RNA-seq to for".9) The correlation between expression level of the RNA and FPKM contacts is not very clear, because in Figure 2—figure supplement 1 and 2 this does not seem to be the case.Reviewer #3:Drosophila cultured cells identifies a spectrum of chromatin-associated RNAs, including chromosome-specific, nascent mRNAs, and sn/sno RNAs. The utility and novelty of ChAR-seq makes it completely appropriate for publication in eLife; I am very enthusiastic about acceptance once some issues about the presentation and analyses are addressed / clarified.This manuscript presents a very important new technique (CHAR-seq) developed to identify chromatin-associated RNAs. The major benefit of this method in contrast to previously used techniques is the use of proximity dependent ligation to generate sequences from ssRNAs linked to nearby DNA, which allows relatively unbiased assessments of associations across much of the genome. The authors demonstrate very nicely that application of ChAR-seq to 1) Data completeness and transparency:To provide a more complete resource for the community, all potentially interesting RNAs should be named/identified in a table, including those that were initially included but didn't match criteria for 'chromatin associated'.cis and trans contacts, RNA expression level, RNA-DNA contacts, and a way to access information about their genomic distributions, etc. – essentially anything that may be of use to scientists interested in whether or not their favorite RNA is present, or in performing their own analyses of the data.a) There should be a table listing the 1797 RNAs , and relevant information, such as% and numbers of b) I assume Figure 2E shows all 1797 as 'included', but the 'chromatin associated' RNAs in Figure 2E should be listed in a table. My impression is that some (73) but not all are listed in Figure 2—figure supplement 1 – are these the red dots in 2E? Even if all red dots are listed in the supplement, readers should be able to access identity and other data for all 1797, and especially the grey dots that are RNA-DNA contact outliers with high expression . Similarly, there appear to be RNAs with low expression that may be chromatin enriched, yet are not highlighted in red.2) Genomics, Analysis and Statistics:a) Figure 1D, 2A and elsewhere. I could not find a description of what the black circles and darker grey areas on the chromosome graphics indicate. I assume black=centromere, but the rest is unclear.not need to assemble the clone8 or Kc genomes, but they should acknowledge this issue.b) Why was release 5 used, given that the more complete release 6 has been available for at least a year? In addition, genomes from the cell lines used have not been assembled, and are likely rearranged  relative to the reference sequence, so the positions shown in the figures are likely to be incorrect, though we don't know where. No, the authors do c) It is unclear what parts of the genome assembly were included, for example were heterochromatic regions included, and if so where is that data presented? This information would help with the interpretations, especially in Figure 4D.d) A related point, for Figure 4D it would be helpful to report the genomic locations (middle of euchromatin? Pericentromeric? X vs. autosomes?) for RNAs whose chromatin contacts show strong correlation with specific chromatin marks. Also, please explain how the correlation signals were aggregated in Figure 4D – the methods described using 2kb bins while the figure showing 100kb bins.e) Can the authors discuss how the use of PCR to amplify ligated RNA/DNA contacts, combined with normalization to expression levels, could exclude RNAs with lower transcription levels, even if they have many  DNA contacts? I think they are being appropriately conservative, but as with all such screens, and to help others who will certainly try it themselves, it would be good to have a brief discussion about the tradeoffs.f) Some of the statements need quantitative support, such as:- Figure 2E – what is the threshold for identifying RNAs with more than expected chromatin interaction  calculated?- Figure 3E: correlation between the DNA contact locations from ChIRP and ChAR- seq –.is this simply the Pearson correlation coefficient?- Figure 4—figure supplement 1: how was the base sequence similarity calculated and used to aggregate signals for different snRNAs?g) Using the correlations between ChIRP and ChAR-seq to assess the resolution of ChAR-seq does not seem to be appropriate, since the preparation of sequencing libraries may be very different between these two datasets.  The authors should provide more reasoning for this approach.How were the replicates analyzed?.Were they merged? What are the correlations between replicates?h) How was the z-score was calculated? It seems like it is calculated as comparing between the values in each bin vs. the rest of the genome. If this is the case, there may be no need to calculate a z-score as every window will have the same denominator, and it may be just as informative as using the read counts. Also, it may be informative to use the chromosome-specific mean (instead of whole genome mean) to further identify interesting local events . Essential revisions:The authors need to address the following main points:1) Compare CharSeq performance in more detail to previously published MARGI and ChIRP-Seq methods: what are the key difference, improvements? Can you quantify this?We have now quantitatively compared our data to ChIRP-seq and MARGI with new analysis in the main text and figures. We have also included a comparison to another recently published method called GRID-seq (described below).Comparison to ChIRP-seq:ChAR-seq produces multiplexed RNA-chromatin contact data for every RNA in the cell without any requirement for the identity of the RNA, whereas ChIRP-seq requires production of oligos against a single target RNA of known sequence. They are complementary technologies, in that ChAR-seq is suited for discovery of interactions and interaction patterns in an unbiased assay, while ChIRP-seq provides targeted, higher-coverage data to explore the contact profiles of RNAs that have already been identified as interesting targets.To address this point, we added a signal-to-noise calculation between ChIRP-seq and ChAR-seq for both roX1 and roX2 . Briefly , we used the same signal-to-noise (SNR) metric employed by Howard Chang’s Lab in a related ChIRP-seq paper . This metric considers the chrX bins or peaks with the most roX RNA binding events to be ‘signal’ and treats roX RNA binding in randomly chosen autosomal bins to be ‘noise’. To quantitatively compare these two very different methods (genome-wide vs single-RNA), we were careful to re-analyze published ChIRP-seq signal from the original paper  with identical filtering and random bin selection. This resulted in higher SNR values for ChAR-seq than ChIRP-seq , despite having ~100-fold fewer roX reads per data set. We believe this quantitative analysis demonstrates the high sensitivity of our genome-wide method, in addition to validating it as identifying specifically bound RNAs.In addition, we previously included several direct comparisons between ChIRP- and ChAR-seq, including genome-wide correlation plot for roX1 and roX2 across different bin sizes ranging over 5 orders of magnitude  that revealed strong correlation over appropriate bin sizes (Spearman rho = 0.7-0.85). We also display roX1 and roX2 RNA binding signal across a 250kb region of chrX for both methods .Comparison to MARGI:MARGI and ChAR-seq both ligate a 5-App oligonucleotide ‘bridge’ or ‘linker’ to the 3’-end of RNA. In MARGI, the linker molecule is then ligated to either restriction digested genomic DNA or sonicated DNA. Beyond this, the methods differ greatly. In MARGI, the proximal RNA and DNA are attached via circularization, and the linker molecule is cut with a restriction enzyme to form templates for sequencing primers at the end of the molecule. We list the two primary advantages of our method over MARGI in the main text discussion: first, ChAR-seq is performed in situ in intact nuclei, whereas the MARGI proximity ligations are performed on beads. As with Hi-C, it is expected that in vitro based proximity ligation will have more spurious ligation events than those from intact nuclei. Second, in ChAR-seq the final RNA-bridge-DNA molecule is sequenced with long single-end reads, which preserves the original ligation junctions and allows us to detect multiple bridges and other aberrant ligation events.Drosophila cells. However, we did quantify signal-to-noise in MARGI by calculating the fold-enrichment of Xist on the X-chr as compared to autosomes:For many types of analyses, we cannot compare ChAR-seq directly to MARGI because MARGI analyzed mammalian cells, whereas we analyzed We used MARGI data from HEK293 cells, which contain two stable Barr bodies  and thus avoid uncertainties about incomplete X inactivation associated with stem cells. Because pxMARGI  data had insufficient association reads , we analyzed distal RNA (diMARGI) contacts on chr X vs. autosomes. We assumed that autosomal bound Xist constitutes noise because FISH in HEK293 cells does not show widespread localization of Xist RNA . Because the data were also too sparse to call high-confidence X association peaks within the X chromosome, we divided the genome into 1 Mb bins, keeping only bins that (a) contained at least 1 diMARGI Xist read, (b) did not contain any ENCODE blacklisted regions, and (c) were the full 1 Mb length. The X chromosome and autosomes contain similar fractions of bins with zero Xist reads: 31% and 26%, respectively. To account for chromatin accessibility or HaeIII site bias, autosomal bins were chosen to have similar accessibility (measured by ENCODE H3K27ac ChIP) and similar HaeIII site density to the chosen bins on the X chromosome.Xist diMARGI contact counts in these matched bins are remarkably similar across autosomes and the X chromosome, indicating a signal to noise ratio of ~1. The ratio of the medians is 1 (2 counts per 1 Mb bin). The ratio of the means is 3.4 (X:autosome) which is largely due to a single outlier bin on the X chromosome with a count of 555, as opposed to the maximum count of 13 counts in sampled autosomal bins. This X chromosome bin contains the Xist locus and although proximal diMARGI contacts are excluded, the area around the Xist gene nevertheless contains a high density of contacts. This is not surprising, but the dramatic drop in Xist contact density from the locus of transcription to other sites on the X chromosome is not in agreement with FISH data which show Xist coating the entire Barr bodies . We conclude that, by this metric, the signal-to-noise in MARGI is low, and ChAR-seq may have distinct advantages as an approach to determine genome-wide RNA-DNA contacts (see Discussion).We have attached the MARGI signal-to-noise figures for reviewers, but decided not to put this analysis in the text because we did not directly analyze mammalian data.Comparison to GRID-seq:While this paper was in revision, another genome-wide RNA-DNA method was published  that uses a methodology similar to ChAR-seq. While many details differ, GRID-seq also involves proximity ligation of a directional bridge to RNA and DNA to construct an RNA-DNA contact map. One key difference is that GRID-seq uses the restriction enzyme MMeI to cut the cDNA and gDNA fragments distal to the linker/bridge, which produces identically sized fragments. This has the advantage over ChAR-seq of being able to size select and sequence only molecules with RNA and DNA information. A critical disadvantage, however, is that reads are 19-23bp, which have a greater chance of falsely mapping to the wrong place in the genome than the 20-100bp RNA and DNA fragments obtained via ChAR-seq. We now mention the above difference in the Discussion.The available GRID-seq data (from NCBI GEO:GSE82312) is heavily processed and some contacts were removed via a background model subtraction. We attempted to perform a signal-to-noise analysis after aligning the raw reads from NCBI SRA and filtering repeats but were unable to determine an appropriate SNR metric as with ChIRP-seq. This is because GRID-seq has lower sequencing depth for roX2 reads, and therefore the vast majority of genomic bins on the autosomes have zero reads, precluding the proper calculation. However, we analyzed the correlation of signal density between GRID and ChAR, GRID and ChIRP, or ChAR and ChIRP for roX2 reads over 20kb chrX bins. GRID and ChAR-seq match well with ChIRP  and with each other (GRID-ChAR = 0.79), suggesting that – at a coarse level – ChAR-seq and GRID-seq have similar ability to detect true RNA-DNA contacts. This analysis is now in Figure 3—figure supplement 1, along with signal tracks along a 2Mb region of the genome.False RNA-DNA contact% calculation, and comparison to MARGI and GRID-seq:In addition to the above comparisons, we spiked in exogenous RNA species  at varying levels relative to total RNA and identified their DNA contacts. This provides us with a measure of false positive contacts, which was always equal to or less than 0.5% in the condition where we spike in a contaminant up to 10% of the total RNA by mass . This value compares favorably to the false positive values published by MARGI (~2.2%) and GRID-seq , with the caveat that a cross-species analysis was used instead of exogenous RNAs.2) Can you add metrics describing sensitivity and specificity of the method?As mentioned above, we have performed a spike-in experiment to address this question about specificity where we added exogenous RNA to our lysed cells in order to mimic the RNA that might be released during our fragmentation step, and which could theoretically cross-ligate to distant sites owing to non-specific binding. In this experiment, we isolated the total soluble RNA after lysis and fragmentation, added back a percentage of spike in RNA by mass at 0.1, 1 and 10%, and then proceeded with our library prep and quantified the number of false positives by alignment to the reference sequences used to generate the in vitro transcribed RNAs. Even in the condition where 10% of the total RNA was a spiked in contaminant, our false positive rate was less than 0.5%, indicating that the subsequent washes and steps after fragmentation but before ligation are sufficient to efficiently remove the contaminating RNA.We have added signal-to-noise metrics and false-positive estimates, as described above, to the text. These reveal that ChAR-seq can detect RNAs with both high sensitivity  and high specificity . In addition, we showed that roX1 and roX2 bind near previously identified dosage compensation peaks from ChIRP-seq data.Other evidence for high sensitivity and specificity are presented in the following figures:- Figure 1—figure supplement 4: 80% of ligation reads are specific to RNA.- Figure 2E: A large subset of RNAs, including roX1 and roX2, are found enriched on chromatin after controlling for expression levels .- Figure 3—figure supplement 2, and Figure 4C, D: When comparing female Kc167 cells to male CME-W1-cl8+ cells, roX1 and roX2 are clearly found enriched in male cells, as expected.- Figure 3—figure supplement 1: roX enrichment on chrX.3) Please clarify concerns about how many times experiments were performed and add details on statistical analysis of reproducibility of data and analyses.We have now explicitly stated the number of biological and technical replicates used. Critical information relevant to each ChAR-Seq library is now presented in Supplementary file 1. For the primary data set, 4 biological replicates were performed and sequenced, along with one technical replicate sequenced at much higher depth. Additional datasets generated during revision provide three new technical replicates.We also have added several graphs that show reproducibility of independent replicates, including a comparison between ChAR-seq replicates with respect to number of RNA-DNA contacts , reproducibility of chromatin enriched RNAs across 3 different replicates, at 2 different expression levels , and reproducibility across different cell lines .Finnaly, we have included all details for statistical analysis in the main text, figure legends, or Materials and methods. These include the statistical tests, p-value, null hypothesis (where appropriate), as well as a text description of how the data inputs were generated or processed.4) Can you make the results available in a more user-friendly way, i.e. make the results available as a list of RNAs and loci they associate with.We have now deposited a user-friendly file into NCBI GEO (accession GSE97131) that contains all RNA-DNA contacts in BED format . This file contains 39 additional columns with information regarding:- RNA transcript details .- DNA contact details.- RNA alignment info .- DNA alignment information.This text file can be easily manipulated using bedtools or shell commands to extract answers to various questions related to the dataset as a whole (what are the 100 RNAs with the most DNA contacts?) or to specific RNA or DNA locations (where does my favorite RNA bind in the genome?). We expect such a file to be valuable to biologists with or without genomics analysis experience.In addition, we now include a supplementary table (Supplementary file 2) that lists 8822 unique RNAs, as well as: their total number of DNA contacts, RNA length, common name, Flybase ID, RNA-seq expression level, normalized RNA-DNA contact number, and chromatin enrichment (over expression level).Finally, we will provide the code for our of analysis pipeline (from raw reads to the above user-friendly file) in a publicly accessible format for download on Gitlab (https://gitlab.com/charseq/flypipe).5) Explore RNAs interacting with heterochromatin, rDNA etc. These are repetitive DNA sequences, but a meta-analysis of RNAs associating with defined repeats is of considerable interest and would enhance the paper.Drosophila genome is incomplete, including some centromeric sequence; 2) given 1, the sequence space covered by a given short repeat element is unknown, making it difficult to make any meaningful conclusion regarding DNA abundance after alignment to these RNAs; 3) repeats are not easily mapped, or mapped to, with current alignment tools. For example, we removed all non-uniquely mapping reads here, but would need to set different multi-mapping threshold parameters for different repeat classes. We also attempted to analyze where RNAs that emanate from DNA repeats (such as transposons) bind; specifically, do RNAs expressed by DNA repeats bind nearby, or to other repetitive DNA loci at a greater frequency? However, this analysis was also inconclusive, primarily due to the multi-mapping issue described above.We agree that an analysis of heterochromatin and repeats would be of great interest, and a great use of the ChAR-seq method. We attempted to analyze which RNAs were bound to repetitive DNA sequences, but found that it was difficult to make any conclusions about data since: 1) much of the repetitive portion of the My laboratory has a strong interest in repeat regions of the genome from our studies of vertebrate centromeres Thus, we are particularly interested in pursuing this analysis in human cells where high quality models for centromere repeats have been generated by Karen Miga, Jim Kent, David Haussler and Hunt Willard and long-read sequence information has enabled the first complete assembly of the human Y centromere. However, we feel that these experiments are outside the scope of this current manuscript.Reviewer #2:[…] 1) Overall, the authors show that ChRA-seq works well to identify both RNAs that are nascent and thus still connected with DNA in cis, and RNAs that do not directly interact with DNA, but rather with chromatin factors in trans. The ChRA-seq approach is validated with the RNAs that are known to be functioning in chr. X dosage compensation, in fact these RNAs (roX1 and roX2) are found to bind chr. X chromatin in the Drosophila male cells but not in the female cells. In addition to the similarity of the ChRA-seq approach to the ChIRP-seq by Quinn et al., Nat Biotech 2014, which although is limited to RNA-chromatin interactions, as pointed out by the Authors, ChRA-seq is almost identical to the method used in Sridhar et al., 2017. Thus, the novelty of the study by Bell et al. is in part reduced because of this recent publication, which exploits a very similar ligation method to capture sites of RNA interaction with chromatin and DNA.Other points:2) What is described in the first paragraph of Results does not match perfectly with Figure 1A, because the RT step in the figure is shown after the bridge is ligated to DNA, while in the text it is described before the bridge is ligated to DNA. The text should match the figure description.We thank the reviewer for pointing out this discrepancy in the figure. Figure 1A has been changed to reflect the protocol as described in the text.3) It would be good to add the polarity of the RNA molecule in red in the Figure 1A. It would be good also to explain that the approach captures the 3' RNA ends of RNA molecules. Also Figure 1A should be bigger to clearly show which RNA end is ligated to the bridge, to clearly indicate the polarity of ends and the structure at the junction. If the App has a free 3' end, and this is ligated to 3' end RNA, is this a 3'-3' ligation? Or is the App removed in the ligation process? This should be explained well because it is not evident.We have added a polarity indicator arrow to the illustration in Figure 1B, showing how the first-strand cDNA sequence (representing the RNA) is always 5’ of the adaptor sequence top strand (which does not contain the biotin modification) and the DNA sequence is always 3’ of the adaptor sequence top strand.4) The statement "the 5'-adenylated end (5'App) enables increased ligation specificity for 3'-terminated ssRNA" is not clear: 'increased specificity' relative to what?We have clarified the language in the manuscript to indicate that the ligation is performed without free ATP, making 5’ adenylation a requirement for ligating the 5’ ends. This adenylation and ligation scheme ensures that only bridge 5’ ends are ligated to RNA, preventing non-specific ligation between non-adenylated RNA 5’ ends and RNA 3’ ends.5) Is the sequence of RNA and DNA oligos presented in Figure 1—figure supplement 2 provided? It is not clear what the molar excess of DNA over RNA is, please clarify. Also the legend of this figure should explain what is shown in every lane. In lane 1 there is a high mw band, what is this, what is its size? The ladder sizes are provided only for the bottom part of the gel, they should be provided also for the top part, considering that there is a high mw band. How many times was this experiment repeated? Is there any statistical analysis of these data? This, possibly, should be explained in the legend.We added the missing label to Lane 3 to indicate that it represents the reaction using R55K K227Q mutant T4 Rnl2tr ligase and added additional labels for the ladder used (a combination of NEB microRNA and low range ssRNA ladders). The high molecular weight band is most likely long concatemers of the ssDNA substrate created by the Thermostable 5’App DNA/RNA ligase. It is possible that this enzyme is able to remove the 3’ block from the ssDNA oligo used in this optimization experiment, or that the block was incomplete. This enzyme is known to have higher activity in ssDNA-ssDNA ligation than the other two. We have modified the figure legend to reflect this explanation and to provide more detailed information on the enzymes used. We only use these results to make a qualitative judgment about which enzyme performed best. Because the conclusion of the experiment would not be altered by further quantification, we proceeded with the approach based on this qualitative assessment. We have also updated the legend to state that the experiment was performed once.The DNA sequence used in Figure 1—figure supplement 2 is the universal miRNA cloning linker: CTGTAGGCACCATCAAT and the RNA sequence was that of the CENP-B box: TTTCGTTGGAAGCGGGA. These details have been added to the Figure 1—figure supplement 2 legend.6) The statement: "This RNase-treatment dramatically reduced in the number of bridge molecules identified, demonstrating that bridge ligation is indeed RNA-dependent " is not strongly supported by the data shown in this figure. In addition to a typo in this statement, looking at the figure, there seems to be a factor of six reduction, which does not represent a 'dramatic' reduction. Importantly, there appears to be no repeats of this experiment and no statistical analysis of the data. Was this experiment reproducible, are there repeats that can be shown?We have changed the manuscript text to be more specific, indicating the fold-reduction in bridge ligation events. The experiment was performed once for this control. In response to reviewer 1, question 3, we discuss why an RNase treatment could still result in the low presence of bridge-containing sequences.7) Legend to Figure 1E has a typo: "Zoomed in region of shown".The figure legend has been updated to fix this typo.8) Typo in Results: "we performed RNA-seq to for".Thank you for pointing it out. This typo has been corrected in the manuscript.9) The correlation between expression level of the RNA and FPKM contacts is not very clear, because in Figure 2—figure supplement 1 and 2 this does not seem to be the case.The correlation the reviewer describes can be observed in Figure 2E, in which we plot the total RNA expression (measured by RNA-seq) vs. RNA-to-DNA contacts (measured by ChAR-seq). For most RNAs, there is a strong correlation between expression level and ChAR-seq contacts observed. The red dots represent RNAs that are enriched by at least 10-fold in the ChAR-seq dataset over the average level of contacts for a given expression level. This correlation is already normalized in the bar plot on the right side of Figure 2—figure supplement 1 and the quantity being displayed is fold-enrichment of ChAR-seq contacts over the expected contacts for a given expression level. The left bar plot is the raw expression level. What this figure illustrates is that the level of chromatin enrichment for a given RNA is not correlated with the total number of contacts we observe – enrichment is not merely a property of being above some limit of detection, it is a property of the RNA itself.Reviewer #3:[…] 1) Data completeness and transparency:To provide a more complete resource for the community, all potentially interesting RNAs should be named/identified in a table, including those that were initially included but didn't match criteria for 'chromatin associated'.a) There should be a table listing the 1797 RNAs , and relevant information, such as% and numbers of cis and trans contacts, RNA expression level, RNA-DNA contacts, and a way to access information about their genomic distributions, etc. – essentially anything that may be of use to scientists interested in whether or not their favorite RNA is present, or in performing their own analyses of the data.We agree that the 1797  most abundant transcripts are of interest. We have now generated a 39 column ‘masterfile’ that contains all RNA-DNA contacts and related information, as described in our response to the editor’s Essential revisions. These data are publicly available on NCBI GEO (GSE97131), In addition, we provide Supplementary file 1 that includes the top 8822 unique RNAs, their RNA-DNA contact number (CPKM), RNA expression level, and chromatin enrichment values, which allows any scientist to search for a particular RNA of interest.b) I assume Figure 2E shows all 1797 as 'included', but the 'chromatin associated' RNAs in Figure 2E should be listed in a table. My impression is that some (73) but not all are listed in Figure 2—figure supplement 1 – are these the red dots in 2E? Even if all red dots are listed in the supplement, readers should be able to access identity and other data for all 1797, and especially the grey dots that are RNA-DNA contact outliers with high expression . Similarly, there appear to be RNAs with low expression that may be chromatin enriched, yet are not highlighted in red.The red dots in revised Figure 2E are the 138 RNAs with contacts enriched 10-fold over the expression levels, and that have more than 100 CPKM (DNA contacts). As stated above, the most abundant RNAs can be extracted from Table 1, along with all RNAs above any desired fold enrichment threshold.2) Genomics, Analysis and Statistics:a) Figure 1D, 2A and elsewhere. I could not find a description of what the black circles and darker grey areas on the chromosome graphics indicate. I assume black=centromere, but the rest is unclear.We have clarified these graphics in the figure legends. The cartoon chromosomes depicted in Figure 1D, 2A, 3C, and 3D are scaled visual representations of the chromosomes. The black circles are the centromeres, the light gray regions are the primary chromosome scaffolds, and the dark gray regions are the heterochromatic scaffolds.b) Why was release 5 used, given that the more complete release 6 has been available for at least a year?Release 5 (dm3) was used for several reasons. First and foremost, the ChIRP-seq roX1 and roX2 datasets that we used to for validation and the modENCODE data that we used for deeper analysis were all generated with the dm3 build. Many available datasets in NCBI-GEO lack either the raw read files  or lack the necessary analysis pipelines to proceed from raw reads to the available processed genome coverage files. For the processed files that are not at single bp resolution, it would be difficult to convert genome coordinates from release 5 to release 6.http://flybase.org/static_pages/feature/previous/articles/2014_07/FB2014_04.html). Finally, coordinate conversion is generally easier going from an older build to a newer one, so for the small number of affected regions or genes, researchers for whom this is necessary can use readily available conversion tools to re-map the ChAR-seq dataset, provided in NCBI GEO.Second, while major improvements on dm6 over release 5 exist in the heterochromatic scaffolds, the rest of the genome has only minor changes in dm6 compared to dm5  relative to the reference sequence, so the positions shown in the figures are likely to be incorrect, though we don't know where. No, the authors do not need to assemble the clone8 or Kc genomes, but they should acknowledge this issue.Drosophila cell lines had their DNA sequenced to determine ploidy across the genome . Of all 19 commonly used cell lines, the CME-W1-cl8+ line had, by far, the most optimum ploidy across the genome, which was very close to 1X throughout, whereas the commonly used S2 lines were largely 4X with variable regions up to 8X ploidy. The CME-W1-cl8+ line is also one of the modENCODE lines and maintained by the Drosophila Genomics Resource Center.We agree that the genome assemblies are lacking for some of these cell lines and have added a sentence to the Materials and methods section to indicate that there may exist variation in the positions of some datasets due to genomic differences between cell lines. We took great care in selecting a suitable cell line for a genome wide RNA-DNA mapping method: The clone 8 cell line (CME-W1-cl8+) used for the majority of the data collection in this work has a normal and stable karyotype, and is male so we could analyze the roX genes for validation. In a recent study, 19 c) It is unclear what parts of the genome assembly were included, for example were heterochromatic regions included, and if so where is that data presented? This information would help with the interpretations, especially in Figure 4D.We have now included which regions of the genome are included in each figure legend or extended Materials and methods, including Figure 4D. For the majority of analyses, chr2Het, chr3Het, and chrXHet were included, with exceptions noted in the figure legends or Materials and methods.d) A related point, for Figure 4D it would be helpful to report the genomic locations (middle of euchromatin? Pericentromeric? X vs. autosomes?) for RNAs whose chromatin contacts show strong correlation with specific chromatin marks. Also, please explain how the correlation signals were aggregated in Figure 4D – the methods described using 2kb bins while the figure showing 100kb bins.The whole reference genome was used, including the heterochromatin scaffolds; however, we filtered the regions of the build determined to have high frequency mapping artifacts  and repeats, which also produce mapping artifacts. The cluster analysis in Figure 4D is genome-wide, excluding only the chrU and chrUhet scaffolds, because those are of low quality and do not represent a real biological chromosome. The DpnII normalization  of ChAR-seq tracks was performed using 2-kb windows, while the Pearson correlation coefficient was calculated pairwise between each ChAR-seq track  and each modENCODE track . The Pearson correlation coefficient was calculated using 100 kb bins because we sought to use a window size slightly larger than our estimated resolution.e) Can the authors discuss how the use of PCR to amplify ligated RNA/DNA contacts, combined with normalization to expression levels, could exclude RNAs with lower transcription levels, even if they have many  DNA contacts? I think they are being appropriately conservative, but as with all such screens, and to help others who will certainly try it themselves, it would be good to have a brief discussion about the tradeoffs.It is true that using PCR to amplify our molecules during the library prep could introduce biases. However, we minimized the number of PCR cycles performed by using a side PCR to estimate the minimum number of cycles required to reach a stable exponential phase. We note that by normalizing to expression levels, RNAs with lower transcription levels but many DNA contacts may actually be more likely to be called as “chromatin-bound” after our enrichment calculations. Although our current dataset may have missed some RNAs expressed at very low levels, sequencing at greater depth with sufficient library complexity can mitigate this issue, as it does with RNA-seq.f) Some of the statements need quantitative support, such as:- Figure 2E: what is the threshold for identifying RNAs with more than expected chromatin interaction . We also required more than 100 ChAR-seq contacts, which we empirically found to be a rough minimum for which to explore various properties of the RNA-DNA contact .- How were p-values for rox1 and rox2 (7.6 fold and 8.1 fold enrichment) calculated?This enrichment was calculated using a one-tailed cumulative binomial test. A full description of this calculation is included in the Materials and methods.- Figure 3E: correlation between the DNA contact locations from ChIRP and ChAR- seq – is this simply the Pearson correlation coefficient?This is Spearman’s rank correlation, and this is now noted in the Materials and methods and new Figure 3 legend.- Figure 4—figure supplement 1: how was the base sequence similarity calculated and used to aggregate signals for different snRNAs?This is described in the Figure 4—figure supplement 1 legend. In short, we used GeniousR7 to calculate a pairwise distance score, then clustered the data using R.g) Using the correlations between ChIRP and ChAR-seq to assess the resolution of ChAR-seq does not seem to be appropriate, since the preparation of sequencing libraries may be very different between these two datasets.  The authors should provide more reasoning for this approach.ChIRP, RAP, and CHART are all well established RNA hybridization and chromatin pull down methods designed to map individual chromatin associated RNAs. ChIRP, in particular, has been applied in the CME-W1-cl8+ cell line, which is why we used this specific dataset to estimate our resolution. ChIRP data have a resolution of ~1-5kb, which provides a useful lower bound, and ChIRP peaks match well with 0.5-1kb ChIP-seq peaks for the dosage compensation complex protein MSL3. The reviewer is correct that the library preparation is very different, but this is advantageous for benchmarking our method: ChIRP was established as a method for mapping chromatin interaction patterns of single RNAs, and therefore comparing the performance of the highly multiplexed ChAR-seq protocol against ChIRP-seq by selecting the same target RNA from both data sets is the most direct comparison we can make.For both roX1 and roX2, the correlation curve increases, then plateaus. The small dip in correlation between a 250 kb bin size and a 1 Mb bin size after the plateau is likely due to the bin size being so large that differences in filtering, blacklisting, etc. between ChIRP and ChAR-seq data may cause different edge effects at ends of chromosomes or in large domains flanked by repetitive regions or heterochromatin.How were the replicates analyzed? Were they merged? What are the correlations between replicates?The replicates show excellent reproducibility and are now presented in Figure 1—figure supplement 5. The replicates were merged for most of the analysis, as described in the Materials and methods and the statistics included in new Supplementary file 1.h) How was the z-score was calculated? It seems like it is calculated as comparing between the values in each bin vs. the rest of the genome. If this is the case, there may be no need to calculate a z-score as every window will have the same denominator, and it may be just as informative as using the read counts. Also, it may be informative to use the chromosome-specific mean (instead of whole genome mean) to further identify interesting local events .We clarified this calculation in the extended Materials and methods section. The z-score is calculated to normalize ChAR-seq mapping events relative to the DpnII frequency for a given bin size.References:Chow JC, Hall LL, Baldry SEL, Thorogood NP, Lawrence JB, Brown CJ. Inducible XIST-dependent X-chromosome inactivation in human somatic cells is reversible. Proceedings of the National Academy of Sciences of the United States of America. 2007;104(24):10104-10109. doi:10.1073/pnas.0610946104.Gilbert SL, Pehrson JR, Sharp PA. XIST RNA associates with specific regions of the inactive X chromatin. J Biol Chem. 2000 Nov 24;275(47):36491-4. PubMed PMID:ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012 Sep 6;489(7414):57-74. doi: 10.1038/nature11247. Updated blacklist for hg38 downloaded from: http://mitra.stanford.edu/kundaje/akundaje/release/blacklists/hg38-human/hg38.blacklist.bed.gz.


Given the current cost-effectiveness of next-generation sequencing, the amount of DNA-seq and RNA-seq data generated is ever increasing. One of the primary objectives of NGS experiments is calling genetic variants. While highly accurate, most variant calling pipelines are not optimized to run efficiently on large data sets. However, as variant calling in genomic data has become common practice, several methods have been proposed to reduce runtime for DNA-seq analysis through the use of parallel computing. Determining the effectively expressed variants from transcriptomics (RNA-seq) data has only recently become possible, and as such does not yet benefit from efficiently parallelized workflows. We introduce Halvade-RNA, a parallel, multi-node RNA-seq variant calling pipeline based on the GATK Best Practices recommendations. Halvade-RNA makes use of the MapReduce programming model to create and manage parallel data streams on which multiple instances of existing tools such as STAR and GATK operate concurrently. Whereas the single-threaded processing of a typical RNA-seq sample requires ∼28h, Halvade-RNA reduces this runtime to ∼2h using a small cluster with two 20-core machines. Even on a single, multi-core workstation, Halvade-RNA can significantly reduce runtime compared to using multi-threading, thus providing for a more cost-effective processing of RNA-seq data. Halvade-RNA is written in Java and uses the Hadoop MapReduce 2.0 API. It supports a wide range of distributions of Hadoop, including Cloudera and Amazon EMR. Recently, a number of methods have been introduced to accelerate read mapping and variant calling through the use of parallel and distributed computing techniques: HugeSeq , MegaSeqhttp://picard.sourceforge.net/) for data preprocessing and GATK for variant calling. Processing a typical RNA-seq sample using this pipeline on a single CPU core takes ∼28 hours. Enabling multi-threading on a 20-core machine reduces runtime only by a factor of two, which indicates considerable loss of performance during execution.Nowadays, RNA-seq datasets are becoming increasingly available. Even though primarily intended to identify transcripts and quantify expression, RNA-seq data can equally be used to call single nucleotide variants . There ahttp://bioinformatics.intec.ugent.be/halvade under GPL license.Here we present Halvade-RNA, a parallel framework for variant calling from RNA-seq data that relies on the MapReduce programming model . MapReduhttps://software.broadinstitute.org/gatk/guide/article?id=3891. In this work, we adopt the GATK RNA-seq variant calling pipeline as described in In order to obtain accurate spliced read alignment, a two-step approach using the STAR aligner is used as first described in . During Because the RNA-seq variant calling pipeline involves two passes of the STAR aligner, it cannot be readily expressed in the Halvade MapReduce framework as implemented for DNA-seq variant calling . WhereasFirst, the input FASTQ files are interleaved (such that paired-end reads are adjacent to each other) and split into smaller file chunks. During the map phase of the first MapReduce job, these input chunks are processed in parallel by multiple instances of the STAR aligner. To avoid loading the reference genome index from disk by each STAR instance individually, the genome index is first loaded in shared memory, after which all the STAR instances on this node can access this genome index from RAM. During this first alignment pass, the actual read alignments  are ignored and only splice junction information is retained. For each read that spans multiple exons, STAR produces a record containing junction information, such as genomic location and strand. The Map tasks emit this information as intermediate <key, value> pairs, with the key holding tuples of integers containing the contig index and the position of the splice junction, and the value containing the STAR-generated string with splice junction information. When all Map tasks are finished, all intermediate <key, value> pairs are sent to a single reducer and written to file. In the reduce task, this file is subsequently used by STAR to build a new genome index that incorporates this slice junction information. This is a purely sequential step, which should be kept as short as possible. We therefore configure STAR to build a sparse index, where only a fraction of the genomic locations are indexed. While using a sparse index results in a slightly higher runtime during the second alignment phase, the reduced runtime during index construction  ensures the lowest overall runtime.The second MapReduce job is similar to the DNA-seq variant calling MapReduce implementation and we refer to  for moreDuring the Reduce phase, remaining data preprocessing and variant calling steps are performed in parallel through the concurrent processing of multiple genomic regions. To achieve this, multiple instances of Picard and GATK are run in parallel, each instance operating on a distinct genomic region. These steps are similar for the DNA-seq variant calling pipeline. The only notable difference is the addition of the Split‘N’Trim module. Called variants are written to VCF files, one VCF file per reduce task. Optionally, these partial VCF files can be merged into a single VCF file in a third, lightweight MapReduce job. It should be noted that Halvade-RNA can also be executed on existing BAM files. In that scenario, the first MapReduce job is skipped and the Map phase of the second job is modified in order to partition the provided BAM file.As RNA-seq analysis often involves the quantification of gene expression, Halvade-RNA provides the option to count the number of reads per exon. This is done by the FeatureCounts tool  and is rFinally we remark that the used tools are still often improved and features are added, so it is important to note that Halvade-RNA allows replacing the binaries of the tools with newer versions, assuming that the command line arguments remain the same. Similarly, new tools could easily be added by updating the source code and calling the new tools appropriately.Halvade-RNA was benchmarked on 9 RNA-seq samples  from the Cancer Cell Line Encyclopedia , each saAdditionally, Halvade-RNA was compared with GNU parallel  for multFour cases were set up to assess the performance of Halvade-RNA: i) the original RNA-seq variant calling pipeline on a single CPU core, ii) the original RNA-seq variant calling pipeline on 20 CPU cores, with multi-threading enabled in both STAR and GATK, iii) the Halvade-RNA pipeline on the same 20-core machine, and iv) the Halvade-RNA pipeline on two 20-core machines . For theNote that similar results were obtained on a public cloud platform (Amazon EMR). Using a single node of the type r3.8xlarge, we obtain an average execution time of 3h 29 min per sample . Using two nodes, the average runtime decreases to 2h 32 min per sample . The Amazon nodes have only 16 CPU cores and an additional data transfer from central S3 storage to the local worker nodes storage is required which explains the slightly higher runtimes. The average financial cost per sample was 14.15 US dollar when using a single node and 20.48 US dollar when using two nodes (pricing of December 2016).Given the large variability in gene expression within one RNA-seq sample, certain genomic chunks can have significantly more aligned reads. For example, in our experiment, coverage depth between genomic chunks could vary as much as thousand fold. As a consequence, there is large variability in execution time during the reduce phases, making it more difficult to balance load than in the Halvade-DNA framework, as coverage depth is more uniform in DNA-seq data. Finally, we show the per-sample concordance in the variants called by the original sequential pipeline and Halvade-RNA . On averWe have implemented a parallelized variant calling pipeline for RNA-seq data using a MapReduce approach, and compared the efficiency and accuracy of the pipeline to the original sequential implementation. Running the original pipeline using a single core requires on average 27.9 hours per sample. When enabling multi-threading on 20 CPU cores in STAR and GATK, the average runtime per sample decreases to 12.9 hours, largely due to the poor scaling behavior of GATK. In contrast, on the same node, when using Halvade-RNA configured to run 10 parallel instances of STAR (2 threads per instance) and 20 parallel instances (single threaded) of Picard and GATK, average runtime decreases to ∼3 hours, corresponding to a parallel speedup of 9.18 over sequential execution of the pipeline. Clearly, the use of Halvade on a single node strongly reduces average runtime and results in a more cost-effective use of the compute resources. On two nodes, the average runtime further decreases to ∼2 hours.Obtaining a good load balance across parallel tasks is challenging for RNA-seq data, given the large variability in gene expression –and thus coverage depth–across genomic regions. To minimize idle time introduced by slower jobs, Halvade-RNA can be operated in batch mode. In that case, idle slots can be used to start processing the next sample even though the current sample is not yet fully completed. Processing all of the 9 samples in batch mode on the two-node cluster yields a total runtime of 11.8 hours. In comparison, running the pipeline in batch mode gives a runtime of 21h on a two-node cluster. Even though batch mode does not decrease the per-sample processing time below 2 hours, it considerably increases the overall throughput through a more efficient use of compute infrastructure.On average, variants identified by Halvade-RNA and the sequential pipeline have a 93.8% overlap. The variability is almost exclusively caused by variability during read mapping. Variants called by either only Halvade-RNA or the sequential pipeline have a much lower number of supporting reads and hence correspond to low-confidence variants.