πŸ’° DNA-RNA immunoprecipitation (DRIP) protocol | Abcam

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Thus, the hybrid map generated by S1-DRIP-seq led to the identification of the The use of cross-linking agents or an alternative fragmentation protocol with.


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I'm trying to do a DNA/RNA immunoprecipitation (DRIP) using the S antibody and am getting I would appreciate any DRIP-seq protocol for cultured cells.


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DRIP is typically followed by mapping DNA fragments on a few loci or even across the whole genome with qPCR, microarray hybridization, or deep sequencing.


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Compared with DRIP-seq, in which the immunoprecipitated DNA is directly sequenced, DRIPc-seq permits the Dan Dominissini et al., , Nature Protocols.


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DRIP is typically followed by mapping DNA fragments on a few loci or even across the whole genome with qPCR, microarray hybridization, or deep sequencing.


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DRIP is typically followed by mapping DNA fragments on a few loci or even across the whole genome with qPCR, microarray hybridization, or deep sequencing.


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DRIP is typically followed by mapping DNA fragments on a few loci or even across the whole genome with qPCR, microarray hybridization, or deep sequencing.


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I'm trying to do a DNA/RNA immunoprecipitation (DRIP) using the S antibody and am getting I would appreciate any DRIP-seq protocol for cultured cells.


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Compared with DRIP-seq, in which the immunoprecipitated DNA is directly sequenced, DRIPc-seq permits the Dan Dominissini et al., , Nature Protocols.


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Thank you very much. DRIP (DNA-RNA immunoprecipitation using S antibody​.) and followed by DNA sequencing.


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Next Generation Sequencing 2: Purifying DNA Samples with Magnetic Beads - Eric Chow (UCSF)

In contrast to the theoretical fragment size distribution, we observed a broad DNA size range in a real digestion reaction between β€”10, bp Supplemental Fig. Analysis of restriction sites over genic and intergenic regions. Biased genome sampling, related to the nonrandom distribution of restriction enzyme recognition sequences, was even more pronounced over exons Fig. Also, RNase H1 digestion of the fragmented nucleic acid was kept as an obligatory negative control of the immunoprecipitation. The observed electrophoretic mobility shift was prevalent on supercoiled, nicked-circular, and linearized DNA templates. A high-confidence R-loop peak set was generated from the identified binding sites, and their chromosomal distribution was characterized. Lower two tracks: the precise genomic position of R-loops was resolved in the sonicated group of samples. Biased genome sampling severely compromised mapping resolution and prevented the assignment of precise biological function to a significant fraction of R-loops. Precise genomic position of R-loops could be resolved by sonication. One can reveal technical heterogeneities 1 in terms of the studied model organisms and cell types, 2 in whether the cells were fixed by formaldehyde HCHO or not, 3 in whether the immunoprecipitation was chromatin-based or DNA-based ChIP vs. R-loop sites were underrepresented at protein coding exons, similarly to earlier DRIP experiments performed with sonicated nucleic acid; however, restriction enzyme-fragmented DRIP samples were positively biased toward exons. Summary of available human DRIP-seq experiments. The increasing recognition of RNA-DNA hybrid structures in the physiology and pathology of chromosomes has prompted us to develop an analytical approach to estimate the inherent biases and errors of existing DRIP protocols and to assess the power of the technology. We also showed that genome fragmentation by restriction enzymes led to the overrepresentation of long DRIP fragments over ORFs, which was especially enhanced over the first exons of protein coding genes Figs. At other annotation categories gene body, introns, and promoters , the difference was not significant between the two groups. For instance, R-loops 1 drive embryonic stem cell differentiation via modulating the chromosomal binding of chromatin-regulatory complexes Chen et al. S8 , demonstrating the reliability of the tested DRIP protocols in other cell types. On the contrary, some DRIP workflows performed unreliably and generated random answers exp. To derive the parameters of the DRIP classifiers, known positive and negative examples genomic sites could be chosen from the scientific literature based on their known R-loop profiles; however, the heterogeneity of the available DRIP-qPCR and DRIP-seq data sets see Introduction prompted us to establish our independent R-loop training set. The upper four rows represent DRIP experiments fragmenting the nucleic acid by sonication, while the lower five rows highlight restriction enzyme-digested DRIP samples. In a pathological context, perturbation or mutation of any of the following factors causes the chromosomal accumulation of RNA-DNA hybrids and consequent genomic instability: 1 mRNA splicing factors and RNA export factors e. We also show that some of the workflows perform poorly and generate random answers. Suboptimal DRIP conditions might prevent the assignment of precise biological function to a significant fraction of R-loops. Biased genome sampling severely compromised mapping resolution and, as a consequence, the assignment of clear biological function to a fraction of R-loops. Peak length distributions differ significantly between the two fragmentation methods. Red and blue slices, marking the rarest restriction site frequencies, are prevalent over genic elements in each pie chart. A Bar chart showing the number of identified R-loop peaks in human Jurkat cells and naive T cells this study. C ROC curves of the top four experiments. B Experiments 17β€”24 test the impact of acoustic sharing performed on a chromatin prep rather than on naked nucleic acid, similarly to the ChIP protocol. Positive and negative test regions were selected from the identified R-loop set Supplemental Fig. A Restriction fragment lengths over genic regions gene bodies, exons, first exons are significantly larger compared to intergenic regions. Greater equal than one read: the restriction site was uncut in a fraction of cells. Sonicated and restriction enzyme-digested samples were strikingly different in their R-loop length distributions narrow: β€” bp vs. Similar or even higher ROC parameters were obtained in a repeated experiment using a B lymphoblastoid cell line Supplemental Fig. These observations necessitate the proper control of DNA fragment length distribution in DRIP samples that derive from restriction enzyme-fragmented nucleic acid. Horizontal dotted lines represent the cutoff value calculated from the ROC curves separating the true R-loop signal from background. The plot shows the difference of genic observed and intergenic expected fragment sizes in base pairs. Experiments 1β€”16 were processed in parallel at both temperatures, while exp. S9, S A statistically significant difference was obtained for RNase A-treated vs. Biological implications of having too wide peak sizes will be discussed later. Colors indicate the proportion of cutting sites in each category. See the model of cutting efficiency in panel F. There were uncut reads sites over half of the theoretical restriction sites. Consequently, genic regions void of suitable restriction sites appear as long DRIP fragments that potentially compromise mapping resolution. The temperature variable is not depicted in the cartoon, but it is referred in the main text. We quantitated the relative trade-offs between true positive hits and experimental errors false R-loop associations by performing ROC analysis Robin et al. This allowed us to assess whether the S9. Error bars represent the confidence interval of AUCs. Based on the available workflows of published DRIP protocols and considering the main technical variables that might contribute to the observed heterogeneities, we designed forty DRIP experimental schemes binary classifiers so that we assess how they rank different test loci according to their known RNA-DNA hybrid status Fig. Values and cell colors represent pairwise and unique overlap ratios between each peak set. Based on the above experiences, we suggest the following refinements of DRIP workflows to obtain accurate estimates of RNA-DNA hybrid occupancies: 1 Omission of HCHO-fixation and RNase A treatment, isolation of nucleic acid by silica membrane kit purification, nucleic acid fragmentation by sonication, followed by immunoprecipitation with the S9.{/INSERTKEYS}{/PARAGRAPH} Although the average DNA fragment size resulting from restriction enzyme digestion fits the requirements of the DRIP assay, we found that the frequency of cutting sites was significantly higher within intergenic regions, producing lengthy restriction fragments over protein coding ORFs Fig. Furthermore, we found that the most commonly used genome fragmentation method restriction enzyme digestion led to the overrepresentation of lengthy DRIP fragments over coding ORFs, and this bias was enhanced at the first exons. The former highlights the yield of immunoprecipitation, while the latter is a quantitative measure of true and false R-loop associations. The level of statistical significance was 0. For instance, the worst and best DRIP schemes exp. S2C,D applying fluorescent microscopic detection. B β€” D The number of restriction sites over genic regions is significantly lower compared to intergenic regions. With the observed variances in mind, our consensus R-loop set was regarded as an amenable reference to benchmark the DRIP classifiers. As opposed to restriction enzyme digestion, sonication creates random DNA fragments with a typical size of β€” bp that dictate the spatial resolution of the DRIP assay Supplemental Fig. B Annotation of R-loop binding sites over functional genomic elements. When a budding yeast genomic DNA was digested in a parallel experiment, we managed to obtain the expected in silico fragment size distribution Supplemental Fig. For instance, correct estimation of evolutionary conservation between R-loop binding sites, relying on sequence homologies of exons that are associated with R-loops Sanz et al. The proportion of uncut reads was even higher within gene coding regions compared to intergenic regions. Consequently, high DRIP enrichment is not necessarily accompanied by increased accuracy, and vice versa. In the current study, we aimed to assess possible confounding effects related to key experimental variables of the DRIP procedure. The above examples clearly illustrate the massive progress in the field that has been driven by technological advancements of R-loop detection methods. In the tested experimental conditions, we managed to find and verify DRIP workflows that were able to distinguish complex or weak DRIP-qPCR signals from a noisy background with high confidence across a number of genomic regions exp. The sensitivity, specificity, and the area under the curve AUC values were extracted from the ROC plots Supplemental Table S2 and used as an objective measure of the robustness of the 40 experiments. We attribute these differences to the extensive variation of R-loop lengths and heterogeneities of the studied cell types. This might create ectopic R-loop sites or abolish physiological R-loop contacts. This might prevent a fraction of R-loops from being detected. {PARAGRAPH}{INSERTKEYS}The impact of R-loops on the physiology and pathology of chromosomes has been demonstrated extensively by chromatin biology research. Median peak length and 2. AUC values close to 0. Based on these considerations, the top four DRIP classifiers were: exp. The peaks were significantly enriched at gene promoters and repetitive elements Fig. As shown in Supplemental Figure S2B , the hybrids were indeed resistant to RNase A digestion at high ionic strength, but they became highly sensitive to RNase A as a function of decreasing monovalent concentration. Experimental design: constructing DRIP schemes. As a control, we repeated the restriction enzyme cleavage in varying reaction conditions without detecting any improvement in the digestion efficacy Supplemental Fig. Obviously, each of these variables can introduce substantial bias that might obscure the overall outcome of the experiment, but their consequence, alone or in combination, has remained unexplored. Because of the above, the mode of DNA fragmentation restriction enzymes and sonication was introduced as an important parameter in our DRIP pipeline Fig. Green boxes represent R-loop enriched regions predicted by the peak callers. Large restriction fragments over gene bodies cause uncertainty in the precise localization of R-loops, potentially impeding their functional annotation. Pairwise comparison of the main experimental variables Fig. These techniques involve, for instance, electrophoretic mobility shift assays Yu et al. The difference between the two nucleic acid fragmentation methods is clearly apparent, as peak sets from the same fragmentation process better resemble each other highlighted in black. C Density plots showing the distribution of R-loop peak sizes, classified by fragmentation method restriction enzyme vs. Most of the DRIP protocols use the experimental design that was developed by a few laboratories, without paying attention to the potential caveats that might affect the outcome of RNA-DNA hybrid mapping. Good DRIP practice. Zero read: the restriction site was cut. S6, S8. Experiments 1β€”16 consider the effect of 1 formaldehyde HCHO fixation, 2 the method of nucleic acid isolation, 3 removal of free RNA, 4 the mode of nucleic acid fragmentation Fig. The other half was digested just before the S9. To assess the accuracy and utility of this technology, we pursued an analytical approach to estimate inherent biases and errors in the DRIP protocol. However, excessive sonication can introduce strand breaks in the DNA or simply shake off a subset of R-loops from the chromosomes, potentially compromising their detectability by qPCR. The original protocols are still being used without paying attention to their potential caveats: several critical points have remained exceedingly heterogeneous among the DRIP studies Supplemental Table S1 that might account for at least some of the contradictory results Ginno et al. The choice of restriction enzymes defines the cleavage pattern of DNA that is critical to achieve optimal fragment length distribution and mapping resolution.