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Plasmidsaurus plasmid sequencing
Improvement of in vitro transcription conditions for synthesizing precise precursor linear RNAs. ( A ) Sanger <t>sequencing</t> of 3′ ends of the linear RNA transcribed from unmodified DNA templates showed heterogeneity. ( B ) Schematic of amplification of DNA templates using reverse primer with two 2′-O-methyl RNA nucleosides at 3′ end to synthesize transcripts with homogeneous ends. ( C ) Sanger sequencing revealed homogenous ends of RNAs transcribed from modified DNA templates. ( D ) RNAfold modeled structures showed that addition of dinucleotides CC and CU at the 3′ends of linear precursor drastically impact the structure of the downstream circRNAs while the addition of G/T/C had no influence.
Plasmid Sequencing, supplied by Plasmidsaurus, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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plasmid sequencing - by Bioz Stars, 2026-05
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86
Plasmidsaurus whole plasmid sequencing
Benchmarking predictions of CRISPR-Cas9ko guide behavior at putative off-target sites with GUIDE-seq data (A) The specificity-defining region (SDR) refers to positions 4–20 along a guide <t>sequence</t> and the PAM. (B) Left: off-target site counts indicate the total number of potential off-target sites across the 114 guides in the GUIDE-seq dataset, stratified by the maximum number of mismatches in the SDR tolerated in the off-target search. “Bulge” off-target sites represent those with 4+ mismatches in the SDR, yet this mismatch count can be reduced with a single-nucleotide shift within the sequence. Right: the fraction of each set of off-target sites as reported on the left in each range of activity rates, which are measured by the percentage of GUIDE-seq reads at the off-target site relative to the guide’s on-target site. (C) Association between CFD scores and activity at all predicted off-target sites for the 114 guides in the GUIDE-seq dataset. Off-target sites that exceed two mismatches in the SDR are excluded due to their low propensity for activity. Off-target sites are binned into groups of 37 by their CFD score, and those with at least 1% as many GUIDE-seq reads as the on-target site are classified as active. For each bin, the median CFD value indicates the predicted activity rate, and the fraction of active off-target sites indicates the experimental activity rate, yielding a Pearson correlation of 0.93. Horizontal error bars capture the minimum and maximum CFD score within each bin. See also .
Whole Plasmid Sequencing, supplied by Plasmidsaurus, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/whole plasmid sequencing/product/Plasmidsaurus
Average 86 stars, based on 1 article reviews
whole plasmid sequencing - by Bioz Stars, 2026-05
86/100 stars
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86
Plasmidsaurus nanopore whole plasmid sequencing
Benchmarking predictions of CRISPR-Cas9ko guide behavior at putative off-target sites with GUIDE-seq data (A) The specificity-defining region (SDR) refers to positions 4–20 along a guide <t>sequence</t> and the PAM. (B) Left: off-target site counts indicate the total number of potential off-target sites across the 114 guides in the GUIDE-seq dataset, stratified by the maximum number of mismatches in the SDR tolerated in the off-target search. “Bulge” off-target sites represent those with 4+ mismatches in the SDR, yet this mismatch count can be reduced with a single-nucleotide shift within the sequence. Right: the fraction of each set of off-target sites as reported on the left in each range of activity rates, which are measured by the percentage of GUIDE-seq reads at the off-target site relative to the guide’s on-target site. (C) Association between CFD scores and activity at all predicted off-target sites for the 114 guides in the GUIDE-seq dataset. Off-target sites that exceed two mismatches in the SDR are excluded due to their low propensity for activity. Off-target sites are binned into groups of 37 by their CFD score, and those with at least 1% as many GUIDE-seq reads as the on-target site are classified as active. For each bin, the median CFD value indicates the predicted activity rate, and the fraction of active off-target sites indicates the experimental activity rate, yielding a Pearson correlation of 0.93. Horizontal error bars capture the minimum and maximum CFD score within each bin. See also .
Nanopore Whole Plasmid Sequencing, supplied by Plasmidsaurus, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 86 stars, based on 1 article reviews
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Improvement of in vitro transcription conditions for synthesizing precise precursor linear RNAs. ( A ) Sanger sequencing of 3′ ends of the linear RNA transcribed from unmodified DNA templates showed heterogeneity. ( B ) Schematic of amplification of DNA templates using reverse primer with two 2′-O-methyl RNA nucleosides at 3′ end to synthesize transcripts with homogeneous ends. ( C ) Sanger sequencing revealed homogenous ends of RNAs transcribed from modified DNA templates. ( D ) RNAfold modeled structures showed that addition of dinucleotides CC and CU at the 3′ends of linear precursor drastically impact the structure of the downstream circRNAs while the addition of G/T/C had no influence.

Journal: Nucleic Acids Research

Article Title: Generation of precise and accurate engineered circRNAs using enzymatic ligation

doi: 10.1093/nar/gkag405

Figure Lengend Snippet: Improvement of in vitro transcription conditions for synthesizing precise precursor linear RNAs. ( A ) Sanger sequencing of 3′ ends of the linear RNA transcribed from unmodified DNA templates showed heterogeneity. ( B ) Schematic of amplification of DNA templates using reverse primer with two 2′-O-methyl RNA nucleosides at 3′ end to synthesize transcripts with homogeneous ends. ( C ) Sanger sequencing revealed homogenous ends of RNAs transcribed from modified DNA templates. ( D ) RNAfold modeled structures showed that addition of dinucleotides CC and CU at the 3′ends of linear precursor drastically impact the structure of the downstream circRNAs while the addition of G/T/C had no influence.

Article Snippet: Plasmid sequences were confirmed by whole plasmid sequencing (Plasmidsaurus).

Techniques: In Vitro, Sequencing, Amplification, Modification

Evaluation of circularization efficiency and accuracy by different ligases. ( A ) Schematic depicting requirements and features of DNA ligase, RNA ligase 1, and RNA ligase 2. ( B ) Workflow of circRNA generation using enzymatic ligation and RNase R based purification which can be improved by addition of poly(A) tails to linear RNAs. ( C ) 3% urea–PAGE showed that all ligases were able to circularize 5′-monophosphate RNAs. Boxed bands depict the circRNAs that run slower than their linear counterparts. Contaminating RNAs of lower and higher size than circular or linear RNA were also observed suggesting that poly(A) tailing and RNase R treatments were insufficient to degrade them. Efficiency of ligation was calculated as percentage yields of RNAs remaining after all treatments divided by input RNA for each ligation reaction. CircRNAs derived from modified transcription templates (mod) had higher efficiencies particularly for DNA ligase and RNA ligase 2 than those derived from unmodified templates (unmod). RNA ligase 2 had the highest circularization efficiency, especially with circRNAs derived from mod templates in presence of an RNA splint. Representative data are from a mean of n = 3 technical replicates with SEM; (*) P ≤.05 (unpaired t -test). ( D ) Sanger sequencing of ligation junctions showed accurate sequences with circRNAs derived from modified transcription templates using all ligases. CircRNAs made with RNA Ligase 1 had errors with the linear RNAs from unmodified templates which were corrected with the use of modified templates.

Journal: Nucleic Acids Research

Article Title: Generation of precise and accurate engineered circRNAs using enzymatic ligation

doi: 10.1093/nar/gkag405

Figure Lengend Snippet: Evaluation of circularization efficiency and accuracy by different ligases. ( A ) Schematic depicting requirements and features of DNA ligase, RNA ligase 1, and RNA ligase 2. ( B ) Workflow of circRNA generation using enzymatic ligation and RNase R based purification which can be improved by addition of poly(A) tails to linear RNAs. ( C ) 3% urea–PAGE showed that all ligases were able to circularize 5′-monophosphate RNAs. Boxed bands depict the circRNAs that run slower than their linear counterparts. Contaminating RNAs of lower and higher size than circular or linear RNA were also observed suggesting that poly(A) tailing and RNase R treatments were insufficient to degrade them. Efficiency of ligation was calculated as percentage yields of RNAs remaining after all treatments divided by input RNA for each ligation reaction. CircRNAs derived from modified transcription templates (mod) had higher efficiencies particularly for DNA ligase and RNA ligase 2 than those derived from unmodified templates (unmod). RNA ligase 2 had the highest circularization efficiency, especially with circRNAs derived from mod templates in presence of an RNA splint. Representative data are from a mean of n = 3 technical replicates with SEM; (*) P ≤.05 (unpaired t -test). ( D ) Sanger sequencing of ligation junctions showed accurate sequences with circRNAs derived from modified transcription templates using all ligases. CircRNAs made with RNA Ligase 1 had errors with the linear RNAs from unmodified templates which were corrected with the use of modified templates.

Article Snippet: Plasmid sequences were confirmed by whole plasmid sequencing (Plasmidsaurus).

Techniques: Ligation, Purification, Derivative Assay, Modification, Sequencing

Purification of circRNAs and extending the RNA ligase 2 (RL2)-dependent circularization method to other RNAs. ( A ) CircRNAs synthesized with RNA ligase 2 using DNA splint were purified using three different approaches: from 3% urea–PAGE using crush and soak method, or from EX E-gels either using the crush and soak method or using column-based kit. As a control, linear RNAs were also extracted using the same methods. CircRNAs extracted from 3% urea–PAGE or EX E-gel using a crush and soak method had more intact circRNAs with less nicking compared to those extracted from EX E-gel using column-based kits. Linear RNAs on the other hand remained intact with each of the approaches. ( B ) Schematic of RNase-H based circularity confirmation assay that uses a short ssDNA probe which cleaves intact circRNAs into a single linear band, while nicked circRNAs or linear RNAs are cut into two shorter bands. ( C ) RNase-H based assay confirmed circularity of EGFP-IRES circRNAs. Linear RNAs were cleaved into two shorter bands of expected sizes while circRNAs derived from modified DNA templates were linearized to the size of full-length linear precursor. ( D, E ) 5′-monophosphate linear precursors of human immunodeficiency virus (HIV) and mCherry were ligated using RNA ligase 2 and respective DNA splints. For circHIV, urea–PAGE purification of circRNAs derived from modified templates had the highest yields with the least contaminating RNAs. Yields of mCherry circRNAs were much higher with polyA + RNase R approach on RNAs from modified template ligated using RNA ligase 2, however urea–PAGE showed higher and lower sized undesired RNAs. Representative data are from a mean of n = 3 technical replicates with SEM. ( F ) Sanger sequencing confirmed accuracy of circRNAs. Clean chromatograms were observed for ligation junctions of both HlV and mCherry circRNAs derived from modified DNA templates purified either through poly(A) tailing and RNase R treatment or from urea–PAGE purification.

Journal: Nucleic Acids Research

Article Title: Generation of precise and accurate engineered circRNAs using enzymatic ligation

doi: 10.1093/nar/gkag405

Figure Lengend Snippet: Purification of circRNAs and extending the RNA ligase 2 (RL2)-dependent circularization method to other RNAs. ( A ) CircRNAs synthesized with RNA ligase 2 using DNA splint were purified using three different approaches: from 3% urea–PAGE using crush and soak method, or from EX E-gels either using the crush and soak method or using column-based kit. As a control, linear RNAs were also extracted using the same methods. CircRNAs extracted from 3% urea–PAGE or EX E-gel using a crush and soak method had more intact circRNAs with less nicking compared to those extracted from EX E-gel using column-based kits. Linear RNAs on the other hand remained intact with each of the approaches. ( B ) Schematic of RNase-H based circularity confirmation assay that uses a short ssDNA probe which cleaves intact circRNAs into a single linear band, while nicked circRNAs or linear RNAs are cut into two shorter bands. ( C ) RNase-H based assay confirmed circularity of EGFP-IRES circRNAs. Linear RNAs were cleaved into two shorter bands of expected sizes while circRNAs derived from modified DNA templates were linearized to the size of full-length linear precursor. ( D, E ) 5′-monophosphate linear precursors of human immunodeficiency virus (HIV) and mCherry were ligated using RNA ligase 2 and respective DNA splints. For circHIV, urea–PAGE purification of circRNAs derived from modified templates had the highest yields with the least contaminating RNAs. Yields of mCherry circRNAs were much higher with polyA + RNase R approach on RNAs from modified template ligated using RNA ligase 2, however urea–PAGE showed higher and lower sized undesired RNAs. Representative data are from a mean of n = 3 technical replicates with SEM. ( F ) Sanger sequencing confirmed accuracy of circRNAs. Clean chromatograms were observed for ligation junctions of both HlV and mCherry circRNAs derived from modified DNA templates purified either through poly(A) tailing and RNase R treatment or from urea–PAGE purification.

Article Snippet: Plasmid sequences were confirmed by whole plasmid sequencing (Plasmidsaurus).

Techniques: Purification, Synthesized, Control, Rnase H Assay, Derivative Assay, Modification, Virus, Sequencing, Ligation

Benchmarking predictions of CRISPR-Cas9ko guide behavior at putative off-target sites with GUIDE-seq data (A) The specificity-defining region (SDR) refers to positions 4–20 along a guide sequence and the PAM. (B) Left: off-target site counts indicate the total number of potential off-target sites across the 114 guides in the GUIDE-seq dataset, stratified by the maximum number of mismatches in the SDR tolerated in the off-target search. “Bulge” off-target sites represent those with 4+ mismatches in the SDR, yet this mismatch count can be reduced with a single-nucleotide shift within the sequence. Right: the fraction of each set of off-target sites as reported on the left in each range of activity rates, which are measured by the percentage of GUIDE-seq reads at the off-target site relative to the guide’s on-target site. (C) Association between CFD scores and activity at all predicted off-target sites for the 114 guides in the GUIDE-seq dataset. Off-target sites that exceed two mismatches in the SDR are excluded due to their low propensity for activity. Off-target sites are binned into groups of 37 by their CFD score, and those with at least 1% as many GUIDE-seq reads as the on-target site are classified as active. For each bin, the median CFD value indicates the predicted activity rate, and the fraction of active off-target sites indicates the experimental activity rate, yielding a Pearson correlation of 0.93. Horizontal error bars capture the minimum and maximum CFD score within each bin. See also .

Journal: Cell Genomics

Article Title: Balancing off-target and on-target considerations for optimized CRISPR-Cas9 knockout library design

doi: 10.1016/j.xgen.2026.101190

Figure Lengend Snippet: Benchmarking predictions of CRISPR-Cas9ko guide behavior at putative off-target sites with GUIDE-seq data (A) The specificity-defining region (SDR) refers to positions 4–20 along a guide sequence and the PAM. (B) Left: off-target site counts indicate the total number of potential off-target sites across the 114 guides in the GUIDE-seq dataset, stratified by the maximum number of mismatches in the SDR tolerated in the off-target search. “Bulge” off-target sites represent those with 4+ mismatches in the SDR, yet this mismatch count can be reduced with a single-nucleotide shift within the sequence. Right: the fraction of each set of off-target sites as reported on the left in each range of activity rates, which are measured by the percentage of GUIDE-seq reads at the off-target site relative to the guide’s on-target site. (C) Association between CFD scores and activity at all predicted off-target sites for the 114 guides in the GUIDE-seq dataset. Off-target sites that exceed two mismatches in the SDR are excluded due to their low propensity for activity. Off-target sites are binned into groups of 37 by their CFD score, and those with at least 1% as many GUIDE-seq reads as the on-target site are classified as active. For each bin, the median CFD value indicates the predicted activity rate, and the fraction of active off-target sites indicates the experimental activity rate, yielding a Pearson correlation of 0.93. Horizontal error bars capture the minimum and maximum CFD score within each bin. See also .

Article Snippet: Purified plasmids were verified by restriction enzyme digest and whole plasmid sequencing through Plasmidsaurus.

Techniques: CRISPR, Sequencing, Activity Assay

Assessment of the Jacquere library screening performance (A) The Pearson correlation between biological replicates and across cell lines for the Jacquere library screened with the pRDA_734 vector. Correlation is calculated upon the log fold change in guide abundance at day 21 post-transduction relative to plasmid DNA (pDNA) abundance. (B) Receiver operating characteristic analysis of cell viability screening data for the Jacquere library (pRDA_734 vector) and Brunello library screened in A375 cells. False positive rates are determined by nonessential genes and plotted against the true positive rate, which is determined by essential genes. (C) Precision-recall curve to compare the classification of essential genes between Jacquere and Brunello screening data. (D) Stratified analysis of false positive rates and false negative rates in screening data of the Jacquere and Brunello libraries. Guide Z scores are calculated relative to the mean and standard deviation of intergenic control guides, and the plots demonstrate the percentage of guides targeting essential (left) or nonessential (right) genes that deplete at the Z score cutoff, as indicated on the x axis. (E) Rule set 3 sequence+target (Chen tracrRNA) on-target efficacy scores for guides selected by each library to target the essential genes that deplete in Jacquere but not Brunello A375 screening data, thus representing false negatives unique to the Brunello library. For this subset of genes, the guides selected for the Jacquere library feature a higher distribution of efficacy scores (Mann-Whitney two-sided U test; p < 0.001). (F) Aggregate CFD scores for guides selected by each library to target the nonessential genes that deplete in the Brunello but not Jacquere A375 screening data represent false positives unique to the Brunello library. For this subset of genes, the guides selected for the Jacuere library feature a lower distribution of Aggregrate CFD scores (Mann-Whitney two-sided U test; p < 0.001). (G) Comparison of off-target viability effects with the use of single-guide and dual-guide vectors. The A375 screening data of the Jacquere library described in this manuscript are compared to screening data of the Vienna-single and Vienna-double libraries presented in Lukasiak et al. The bar color indicates the number of constructs (out of three) targeting a nonessential gene that depletes relative to intergenic controls. See also .

Journal: Cell Genomics

Article Title: Balancing off-target and on-target considerations for optimized CRISPR-Cas9 knockout library design

doi: 10.1016/j.xgen.2026.101190

Figure Lengend Snippet: Assessment of the Jacquere library screening performance (A) The Pearson correlation between biological replicates and across cell lines for the Jacquere library screened with the pRDA_734 vector. Correlation is calculated upon the log fold change in guide abundance at day 21 post-transduction relative to plasmid DNA (pDNA) abundance. (B) Receiver operating characteristic analysis of cell viability screening data for the Jacquere library (pRDA_734 vector) and Brunello library screened in A375 cells. False positive rates are determined by nonessential genes and plotted against the true positive rate, which is determined by essential genes. (C) Precision-recall curve to compare the classification of essential genes between Jacquere and Brunello screening data. (D) Stratified analysis of false positive rates and false negative rates in screening data of the Jacquere and Brunello libraries. Guide Z scores are calculated relative to the mean and standard deviation of intergenic control guides, and the plots demonstrate the percentage of guides targeting essential (left) or nonessential (right) genes that deplete at the Z score cutoff, as indicated on the x axis. (E) Rule set 3 sequence+target (Chen tracrRNA) on-target efficacy scores for guides selected by each library to target the essential genes that deplete in Jacquere but not Brunello A375 screening data, thus representing false negatives unique to the Brunello library. For this subset of genes, the guides selected for the Jacquere library feature a higher distribution of efficacy scores (Mann-Whitney two-sided U test; p < 0.001). (F) Aggregate CFD scores for guides selected by each library to target the nonessential genes that deplete in the Brunello but not Jacquere A375 screening data represent false positives unique to the Brunello library. For this subset of genes, the guides selected for the Jacuere library feature a lower distribution of Aggregrate CFD scores (Mann-Whitney two-sided U test; p < 0.001). (G) Comparison of off-target viability effects with the use of single-guide and dual-guide vectors. The A375 screening data of the Jacquere library described in this manuscript are compared to screening data of the Vienna-single and Vienna-double libraries presented in Lukasiak et al. The bar color indicates the number of constructs (out of three) targeting a nonessential gene that depletes relative to intergenic controls. See also .

Article Snippet: Purified plasmids were verified by restriction enzyme digest and whole plasmid sequencing through Plasmidsaurus.

Techniques: Library Screening, Plasmid Preparation, Transduction, Standard Deviation, Control, Sequencing, MANN-WHITNEY, Comparison, Construct