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First BASE Laboratories e. coli k12 mg1655 strain
Average F1-scores of the five algorithms compared in this study on the <t>E.</t> <t>coli</t> and S. cerevisiae rRNA test dataset ( NC : Nanocompore; DRM : Drummer; E - DSE: Epinano Delta-Sum-Error; E - LR : Epinano Linear Regression). The E. coli and S. cerevisiae rRNA datasets comprise 10 independent samples. Each sample contains eight subsamples with coverage-depths ranging from 10 to 2000. Different coverage-depths were used since algorithm performance depends on the coverage-depth, as indicated by recent studies ( , ) and also confirmed by our results. Note that all positions are treated as either positive or negative since unsupervised algorithms, do not distinguish between different modification types. In line with this, we do not compute separate F1-scores for each modification type separately, but rather only one F1-score for the whole dataset (for the given coverage-depth). As shown, Modena outperformed other algorithms across all coverage-depths; in some cases by a large margin (e.g. at coverage-depths of 50, 75, 100 and 200). The performance of all algorithms was very stable across the 10 independent samples . Thus, although the figure above shows average F1-scores, the results are highly consistent across all Samples 1–10.
E. Coli K12 Mg1655 Strain, supplied by First BASE Laboratories, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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1) Product Images from "Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering"

Article Title: Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

Journal: Nucleic Acids Research

doi: 10.1093/nar/gkae1168

Average F1-scores of the five algorithms compared in this study on the E. coli and S. cerevisiae rRNA test dataset ( NC : Nanocompore; DRM : Drummer; E - DSE: Epinano Delta-Sum-Error; E - LR : Epinano Linear Regression). The E. coli and S. cerevisiae rRNA datasets comprise 10 independent samples. Each sample contains eight subsamples with coverage-depths ranging from 10 to 2000. Different coverage-depths were used since algorithm performance depends on the coverage-depth, as indicated by recent studies ( , ) and also confirmed by our results. Note that all positions are treated as either positive or negative since unsupervised algorithms, do not distinguish between different modification types. In line with this, we do not compute separate F1-scores for each modification type separately, but rather only one F1-score for the whole dataset (for the given coverage-depth). As shown, Modena outperformed other algorithms across all coverage-depths; in some cases by a large margin (e.g. at coverage-depths of 50, 75, 100 and 200). The performance of all algorithms was very stable across the 10 independent samples . Thus, although the figure above shows average F1-scores, the results are highly consistent across all Samples 1–10.
Figure Legend Snippet: Average F1-scores of the five algorithms compared in this study on the E. coli and S. cerevisiae rRNA test dataset ( NC : Nanocompore; DRM : Drummer; E - DSE: Epinano Delta-Sum-Error; E - LR : Epinano Linear Regression). The E. coli and S. cerevisiae rRNA datasets comprise 10 independent samples. Each sample contains eight subsamples with coverage-depths ranging from 10 to 2000. Different coverage-depths were used since algorithm performance depends on the coverage-depth, as indicated by recent studies ( , ) and also confirmed by our results. Note that all positions are treated as either positive or negative since unsupervised algorithms, do not distinguish between different modification types. In line with this, we do not compute separate F1-scores for each modification type separately, but rather only one F1-score for the whole dataset (for the given coverage-depth). As shown, Modena outperformed other algorithms across all coverage-depths; in some cases by a large margin (e.g. at coverage-depths of 50, 75, 100 and 200). The performance of all algorithms was very stable across the 10 independent samples . Thus, although the figure above shows average F1-scores, the results are highly consistent across all Samples 1–10.

Techniques Used: Modification

Precision–Recall curves (PR curves) for Sample 1 ( E. coli and S. cerevisiae rRNA dataset) for different coverage-depths. As shown, resampling increases the area under the PR curves (i.e. AUPRC scores) across all coverage-depths. Kuiper test further improves AUPRC scores across all coverage-depths, although to a lesser extent.
Figure Legend Snippet: Precision–Recall curves (PR curves) for Sample 1 ( E. coli and S. cerevisiae rRNA dataset) for different coverage-depths. As shown, resampling increases the area under the PR curves (i.e. AUPRC scores) across all coverage-depths. Kuiper test further improves AUPRC scores across all coverage-depths, although to a lesser extent.

Techniques Used:

Violin plots of Modena score distributions for positive and negative test cases across different coverage-depths for Sample 1 of the E. coli / S. cerevisiae benchmark dataset are shown. Two well-separated clusters can be seen for all coverage-depths. The final Step 5 of our algorithm (1D score-clustering) leverages this separation to determine the classification threshold. Note that this represents a different paradigm from the standardly used P -value based thresholds. As shown in our study, this approach is not limited to Modena and can, in principle, be applied to any threshold-based unsupervised algorithm.
Figure Legend Snippet: Violin plots of Modena score distributions for positive and negative test cases across different coverage-depths for Sample 1 of the E. coli / S. cerevisiae benchmark dataset are shown. Two well-separated clusters can be seen for all coverage-depths. The final Step 5 of our algorithm (1D score-clustering) leverages this separation to determine the classification threshold. Note that this represents a different paradigm from the standardly used P -value based thresholds. As shown in our study, this approach is not limited to Modena and can, in principle, be applied to any threshold-based unsupervised algorithm.

Techniques Used:

Average F1-scores (for Samples 1 through 10, E.coli / S. cerevisiae dataset) with coverage-depths ranging from 10 to 2000 are shown. Drummer : original Drummer algorithm with P -value and odds ratio-based threshold; Drummer + 1D clustering : Drummer algorithm (i.e. G-test statistic) with 1D score-clustering step (see Figure ). For detailed results across all samples, see and .
Figure Legend Snippet: Average F1-scores (for Samples 1 through 10, E.coli / S. cerevisiae dataset) with coverage-depths ranging from 10 to 2000 are shown. Drummer : original Drummer algorithm with P -value and odds ratio-based threshold; Drummer + 1D clustering : Drummer algorithm (i.e. G-test statistic) with 1D score-clustering step (see Figure ). For detailed results across all samples, see and .

Techniques Used:

Average F1-scores (for Samples 1 through 10, E.coli / S. cerevisiae dataset) with coverage-depths ranging from 10 to 2000 are depicted. Epinano: Epinano-DSE algorithm with z-score based threshold; Epinano + 1D clustering : Epinano-DSE algorithm with 1D score-clustering step (see Figure ). For detailed results across all samples, see and .
Figure Legend Snippet: Average F1-scores (for Samples 1 through 10, E.coli / S. cerevisiae dataset) with coverage-depths ranging from 10 to 2000 are depicted. Epinano: Epinano-DSE algorithm with z-score based threshold; Epinano + 1D clustering : Epinano-DSE algorithm with 1D score-clustering step (see Figure ). For detailed results across all samples, see and .

Techniques Used:



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Average F1-scores of the five algorithms compared in this study on the <t>E.</t> <t>coli</t> and S. cerevisiae rRNA test dataset ( NC : Nanocompore; DRM : Drummer; E - DSE: Epinano Delta-Sum-Error; E - LR : Epinano Linear Regression). The E. coli and S. cerevisiae rRNA datasets comprise 10 independent samples. Each sample contains eight subsamples with coverage-depths ranging from 10 to 2000. Different coverage-depths were used since algorithm performance depends on the coverage-depth, as indicated by recent studies ( , ) and also confirmed by our results. Note that all positions are treated as either positive or negative since unsupervised algorithms, do not distinguish between different modification types. In line with this, we do not compute separate F1-scores for each modification type separately, but rather only one F1-score for the whole dataset (for the given coverage-depth). As shown, Modena outperformed other algorithms across all coverage-depths; in some cases by a large margin (e.g. at coverage-depths of 50, 75, 100 and 200). The performance of all algorithms was very stable across the 10 independent samples . Thus, although the figure above shows average F1-scores, the results are highly consistent across all Samples 1–10.
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Average F1-scores of the five algorithms compared in this study on the <t>E.</t> <t>coli</t> and S. cerevisiae rRNA test dataset ( NC : Nanocompore; DRM : Drummer; E - DSE: Epinano Delta-Sum-Error; E - LR : Epinano Linear Regression). The E. coli and S. cerevisiae rRNA datasets comprise 10 independent samples. Each sample contains eight subsamples with coverage-depths ranging from 10 to 2000. Different coverage-depths were used since algorithm performance depends on the coverage-depth, as indicated by recent studies ( , ) and also confirmed by our results. Note that all positions are treated as either positive or negative since unsupervised algorithms, do not distinguish between different modification types. In line with this, we do not compute separate F1-scores for each modification type separately, but rather only one F1-score for the whole dataset (for the given coverage-depth). As shown, Modena outperformed other algorithms across all coverage-depths; in some cases by a large margin (e.g. at coverage-depths of 50, 75, 100 and 200). The performance of all algorithms was very stable across the 10 independent samples . Thus, although the figure above shows average F1-scores, the results are highly consistent across all Samples 1–10.
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Average F1-scores of the five algorithms compared in this study on the <t>E.</t> <t>coli</t> and S. cerevisiae rRNA test dataset ( NC : Nanocompore; DRM : Drummer; E - DSE: Epinano Delta-Sum-Error; E - LR : Epinano Linear Regression). The E. coli and S. cerevisiae rRNA datasets comprise 10 independent samples. Each sample contains eight subsamples with coverage-depths ranging from 10 to 2000. Different coverage-depths were used since algorithm performance depends on the coverage-depth, as indicated by recent studies ( , ) and also confirmed by our results. Note that all positions are treated as either positive or negative since unsupervised algorithms, do not distinguish between different modification types. In line with this, we do not compute separate F1-scores for each modification type separately, but rather only one F1-score for the whole dataset (for the given coverage-depth). As shown, Modena outperformed other algorithms across all coverage-depths; in some cases by a large margin (e.g. at coverage-depths of 50, 75, 100 and 200). The performance of all algorithms was very stable across the 10 independent samples . Thus, although the figure above shows average F1-scores, the results are highly consistent across all Samples 1–10.
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Average F1-scores of the five algorithms compared in this study on the <t>E.</t> <t>coli</t> and S. cerevisiae rRNA test dataset ( NC : Nanocompore; DRM : Drummer; E - DSE: Epinano Delta-Sum-Error; E - LR : Epinano Linear Regression). The E. coli and S. cerevisiae rRNA datasets comprise 10 independent samples. Each sample contains eight subsamples with coverage-depths ranging from 10 to 2000. Different coverage-depths were used since algorithm performance depends on the coverage-depth, as indicated by recent studies ( , ) and also confirmed by our results. Note that all positions are treated as either positive or negative since unsupervised algorithms, do not distinguish between different modification types. In line with this, we do not compute separate F1-scores for each modification type separately, but rather only one F1-score for the whole dataset (for the given coverage-depth). As shown, Modena outperformed other algorithms across all coverage-depths; in some cases by a large margin (e.g. at coverage-depths of 50, 75, 100 and 200). The performance of all algorithms was very stable across the 10 independent samples . Thus, although the figure above shows average F1-scores, the results are highly consistent across all Samples 1–10.
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Average F1-scores of the five algorithms compared in this study on the <t>E.</t> <t>coli</t> and S. cerevisiae rRNA test dataset ( NC : Nanocompore; DRM : Drummer; E - DSE: Epinano Delta-Sum-Error; E - LR : Epinano Linear Regression). The E. coli and S. cerevisiae rRNA datasets comprise 10 independent samples. Each sample contains eight subsamples with coverage-depths ranging from 10 to 2000. Different coverage-depths were used since algorithm performance depends on the coverage-depth, as indicated by recent studies ( , ) and also confirmed by our results. Note that all positions are treated as either positive or negative since unsupervised algorithms, do not distinguish between different modification types. In line with this, we do not compute separate F1-scores for each modification type separately, but rather only one F1-score for the whole dataset (for the given coverage-depth). As shown, Modena outperformed other algorithms across all coverage-depths; in some cases by a large margin (e.g. at coverage-depths of 50, 75, 100 and 200). The performance of all algorithms was very stable across the 10 independent samples . Thus, although the figure above shows average F1-scores, the results are highly consistent across all Samples 1–10.
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Figure 3. Structure of the HrpA-bound disome complex (A) Cryo-EM density map and molecular model of the E. coli HrpA-bound disome. The composite map consists of isolated densities of the disome, HrpA Hook/a + b and b-meander/HB2 domains, and focused refined HrpA helicase domain (Figures S2–S5 and STAR Methods). (B and C) Front and back view of the molecular model demonstrating the recognition of the collided disome by HrpA. (D) HrpA domains and their interactions with the disome. sta, stalled 70S; col, collided 70S.

Journal: Molecular cell

Article Title: The RNA helicase HrpA rescues collided ribosomes in E. coli.

doi: 10.1016/j.molcel.2025.01.018

Figure Lengend Snippet: Figure 3. Structure of the HrpA-bound disome complex (A) Cryo-EM density map and molecular model of the E. coli HrpA-bound disome. The composite map consists of isolated densities of the disome, HrpA Hook/a + b and b-meander/HB2 domains, and focused refined HrpA helicase domain (Figures S2–S5 and STAR Methods). (B and C) Front and back view of the molecular model demonstrating the recognition of the collided disome by HrpA. (D) HrpA domains and their interactions with the disome. sta, stalled 70S; col, collided 70S.

Article Snippet: REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies Monoclonal ANTI-FLAG M2-Peroxidase (HRP) Sigma Cat#A8592 Purified anti-E. coli RNA Polymerase b Antibody BioLegend Cat#663006 Goat anti-Mouse IgG (H+L) Secondary Antibody, HRP ThermoFisher Cat#32430 Anti-NanoLuc Monoclonal Antibody Promega Cat#N7000 Anti-Digoxigenin-AP, Fab fragments Roche Cat#11093274910 Bacterial and virus strains E. coli K12 MG1655 E. coli Genetic Resource Center Cat#6300 BL21(DE3) Competent E. coli NEB Cat#C2527H SKEC120 (DsmrB) Saito et al.9 N/A AC014 (DhrpA) This work N/A AC018 (DsmrB DhrpA) This work N/A AC016 (HrpA-FLAG) This work N/A Chemicals, peptides, and recombinant proteins 4–12% Criterion XT Bis-Tris protein gel Bio-Rad Cat#3450124 XT MES Running Buffer Bio-Rad Cat#1610789 SuperSignal West Pico PLUS Chemiluminescent Substrate Thermo Scientific Cat#34580 SuperSignal West Femto Maximum Sensitivity Substrate Thermo Scientific Cat#34096 Carbenicillin RPI Cat#C46000 Chloramphenicol Fisher Scientific Cat#BP904 Tetracycline Hydrochloride Thermo Scientific Cat#A39246 Erythromycin MilliPore Sigma Cat#E5389 Spectinomycin dihydrochloride pentahydrate MilliPore Sigma Cat#S4014 Fusidic acid Sigma Aldrich Cat#F0881 Kanamycin Sulfate MilliPore Sigma Cat#K1377 Phenol R99.0% saturated with buffer for biotechnology pH 4.5 VWR Cat#0981 Formaldehyde 37% MilliPore Sigma Cat#F8775 UltraPure SSC, 20X Thermo Scientific Cat#15557036 PerfectHyb Plus Hybridization Buffer MilliPore Sigma Cat#H7033 CDP-Star , ready-to-use Millipore Sigma Cat#12041677001 Agencourt RNAClean XP, 40 mL Beckman Coulter Cat#A63987 MOPS EZ Rich Defined Medium kit Teknova Cat#M2105 Micrococcal Nuclease NEB Cat#M0247S Chloroform Millipore Sigma Cat#C2432 15% Criterion TBE-Urea Polyacrylamide Gel Bio-Rad Cat# 3450091 SYBR Gold Thermo Fisher Cat#S11494 10% Criterion TBE-Urea Polyacrylamide Gel Bio-Rad Cat#3450088 GlycoBlue Coprecipitant (15 mg/mL) Thermo Fisher Cat#AM9516 cOmplete , EDTA-free Protease Inhibitor Cocktail Millipore Sigma Cat#5056489001 PureCube Ni-NTA agarose Cube Biotech Cat#31105 Creatine phosphate Millipore Sigma Cat#10621714001 Creatine kinase Millipore Sigma Cat#10127566001 (Continued on next page) e1 Molecular Cell 85, 1–9.e1–e7, March 6, 2025

Techniques: Cryo-EM Sample Prep, Isolation

Figure 4. HrpA requires ATP, its CTR, and mRNA for splitting (A–C) In vitro reactions with polysomes from low-dose CAM-treated cells, IF3, HrpA, and ATP. (D) As above, except the polysomes were pre-treated with RNase A before incubation with HrpA. (E) Growth of strains expressing HrpA mutants on plates with antibiotics. (F) Model for the two ribosome rescue pathways in E. coli. (G) Comparison of HrpA-bound disomes (left) with RQT-bound 80S from S. cerevisiae (right).26 Arrows indicate the direction of movement of mRNA during 30–50

Journal: Molecular cell

Article Title: The RNA helicase HrpA rescues collided ribosomes in E. coli.

doi: 10.1016/j.molcel.2025.01.018

Figure Lengend Snippet: Figure 4. HrpA requires ATP, its CTR, and mRNA for splitting (A–C) In vitro reactions with polysomes from low-dose CAM-treated cells, IF3, HrpA, and ATP. (D) As above, except the polysomes were pre-treated with RNase A before incubation with HrpA. (E) Growth of strains expressing HrpA mutants on plates with antibiotics. (F) Model for the two ribosome rescue pathways in E. coli. (G) Comparison of HrpA-bound disomes (left) with RQT-bound 80S from S. cerevisiae (right).26 Arrows indicate the direction of movement of mRNA during 30–50

Article Snippet: REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies Monoclonal ANTI-FLAG M2-Peroxidase (HRP) Sigma Cat#A8592 Purified anti-E. coli RNA Polymerase b Antibody BioLegend Cat#663006 Goat anti-Mouse IgG (H+L) Secondary Antibody, HRP ThermoFisher Cat#32430 Anti-NanoLuc Monoclonal Antibody Promega Cat#N7000 Anti-Digoxigenin-AP, Fab fragments Roche Cat#11093274910 Bacterial and virus strains E. coli K12 MG1655 E. coli Genetic Resource Center Cat#6300 BL21(DE3) Competent E. coli NEB Cat#C2527H SKEC120 (DsmrB) Saito et al.9 N/A AC014 (DhrpA) This work N/A AC018 (DsmrB DhrpA) This work N/A AC016 (HrpA-FLAG) This work N/A Chemicals, peptides, and recombinant proteins 4–12% Criterion XT Bis-Tris protein gel Bio-Rad Cat#3450124 XT MES Running Buffer Bio-Rad Cat#1610789 SuperSignal West Pico PLUS Chemiluminescent Substrate Thermo Scientific Cat#34580 SuperSignal West Femto Maximum Sensitivity Substrate Thermo Scientific Cat#34096 Carbenicillin RPI Cat#C46000 Chloramphenicol Fisher Scientific Cat#BP904 Tetracycline Hydrochloride Thermo Scientific Cat#A39246 Erythromycin MilliPore Sigma Cat#E5389 Spectinomycin dihydrochloride pentahydrate MilliPore Sigma Cat#S4014 Fusidic acid Sigma Aldrich Cat#F0881 Kanamycin Sulfate MilliPore Sigma Cat#K1377 Phenol R99.0% saturated with buffer for biotechnology pH 4.5 VWR Cat#0981 Formaldehyde 37% MilliPore Sigma Cat#F8775 UltraPure SSC, 20X Thermo Scientific Cat#15557036 PerfectHyb Plus Hybridization Buffer MilliPore Sigma Cat#H7033 CDP-Star , ready-to-use Millipore Sigma Cat#12041677001 Agencourt RNAClean XP, 40 mL Beckman Coulter Cat#A63987 MOPS EZ Rich Defined Medium kit Teknova Cat#M2105 Micrococcal Nuclease NEB Cat#M0247S Chloroform Millipore Sigma Cat#C2432 15% Criterion TBE-Urea Polyacrylamide Gel Bio-Rad Cat# 3450091 SYBR Gold Thermo Fisher Cat#S11494 10% Criterion TBE-Urea Polyacrylamide Gel Bio-Rad Cat#3450088 GlycoBlue Coprecipitant (15 mg/mL) Thermo Fisher Cat#AM9516 cOmplete , EDTA-free Protease Inhibitor Cocktail Millipore Sigma Cat#5056489001 PureCube Ni-NTA agarose Cube Biotech Cat#31105 Creatine phosphate Millipore Sigma Cat#10621714001 Creatine kinase Millipore Sigma Cat#10127566001 (Continued on next page) e1 Molecular Cell 85, 1–9.e1–e7, March 6, 2025

Techniques: In Vitro, Incubation, Expressing, Comparison

Average F1-scores of the five algorithms compared in this study on the E. coli and S. cerevisiae rRNA test dataset ( NC : Nanocompore; DRM : Drummer; E - DSE: Epinano Delta-Sum-Error; E - LR : Epinano Linear Regression). The E. coli and S. cerevisiae rRNA datasets comprise 10 independent samples. Each sample contains eight subsamples with coverage-depths ranging from 10 to 2000. Different coverage-depths were used since algorithm performance depends on the coverage-depth, as indicated by recent studies ( , ) and also confirmed by our results. Note that all positions are treated as either positive or negative since unsupervised algorithms, do not distinguish between different modification types. In line with this, we do not compute separate F1-scores for each modification type separately, but rather only one F1-score for the whole dataset (for the given coverage-depth). As shown, Modena outperformed other algorithms across all coverage-depths; in some cases by a large margin (e.g. at coverage-depths of 50, 75, 100 and 200). The performance of all algorithms was very stable across the 10 independent samples . Thus, although the figure above shows average F1-scores, the results are highly consistent across all Samples 1–10.

Journal: Nucleic Acids Research

Article Title: Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

doi: 10.1093/nar/gkae1168

Figure Lengend Snippet: Average F1-scores of the five algorithms compared in this study on the E. coli and S. cerevisiae rRNA test dataset ( NC : Nanocompore; DRM : Drummer; E - DSE: Epinano Delta-Sum-Error; E - LR : Epinano Linear Regression). The E. coli and S. cerevisiae rRNA datasets comprise 10 independent samples. Each sample contains eight subsamples with coverage-depths ranging from 10 to 2000. Different coverage-depths were used since algorithm performance depends on the coverage-depth, as indicated by recent studies ( , ) and also confirmed by our results. Note that all positions are treated as either positive or negative since unsupervised algorithms, do not distinguish between different modification types. In line with this, we do not compute separate F1-scores for each modification type separately, but rather only one F1-score for the whole dataset (for the given coverage-depth). As shown, Modena outperformed other algorithms across all coverage-depths; in some cases by a large margin (e.g. at coverage-depths of 50, 75, 100 and 200). The performance of all algorithms was very stable across the 10 independent samples . Thus, although the figure above shows average F1-scores, the results are highly consistent across all Samples 1–10.

Article Snippet: The E. coli K12 MG1655 strain was grown in 1× LB Broth Miller (1st Base, Singapore) without antibiotics at 37°C at 160 rpm shaking.

Techniques: Modification

Precision–Recall curves (PR curves) for Sample 1 ( E. coli and S. cerevisiae rRNA dataset) for different coverage-depths. As shown, resampling increases the area under the PR curves (i.e. AUPRC scores) across all coverage-depths. Kuiper test further improves AUPRC scores across all coverage-depths, although to a lesser extent.

Journal: Nucleic Acids Research

Article Title: Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

doi: 10.1093/nar/gkae1168

Figure Lengend Snippet: Precision–Recall curves (PR curves) for Sample 1 ( E. coli and S. cerevisiae rRNA dataset) for different coverage-depths. As shown, resampling increases the area under the PR curves (i.e. AUPRC scores) across all coverage-depths. Kuiper test further improves AUPRC scores across all coverage-depths, although to a lesser extent.

Article Snippet: The E. coli K12 MG1655 strain was grown in 1× LB Broth Miller (1st Base, Singapore) without antibiotics at 37°C at 160 rpm shaking.

Techniques:

Violin plots of Modena score distributions for positive and negative test cases across different coverage-depths for Sample 1 of the E. coli / S. cerevisiae benchmark dataset are shown. Two well-separated clusters can be seen for all coverage-depths. The final Step 5 of our algorithm (1D score-clustering) leverages this separation to determine the classification threshold. Note that this represents a different paradigm from the standardly used P -value based thresholds. As shown in our study, this approach is not limited to Modena and can, in principle, be applied to any threshold-based unsupervised algorithm.

Journal: Nucleic Acids Research

Article Title: Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

doi: 10.1093/nar/gkae1168

Figure Lengend Snippet: Violin plots of Modena score distributions for positive and negative test cases across different coverage-depths for Sample 1 of the E. coli / S. cerevisiae benchmark dataset are shown. Two well-separated clusters can be seen for all coverage-depths. The final Step 5 of our algorithm (1D score-clustering) leverages this separation to determine the classification threshold. Note that this represents a different paradigm from the standardly used P -value based thresholds. As shown in our study, this approach is not limited to Modena and can, in principle, be applied to any threshold-based unsupervised algorithm.

Article Snippet: The E. coli K12 MG1655 strain was grown in 1× LB Broth Miller (1st Base, Singapore) without antibiotics at 37°C at 160 rpm shaking.

Techniques:

Average F1-scores (for Samples 1 through 10, E.coli / S. cerevisiae dataset) with coverage-depths ranging from 10 to 2000 are shown. Drummer : original Drummer algorithm with P -value and odds ratio-based threshold; Drummer + 1D clustering : Drummer algorithm (i.e. G-test statistic) with 1D score-clustering step (see Figure ). For detailed results across all samples, see and .

Journal: Nucleic Acids Research

Article Title: Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

doi: 10.1093/nar/gkae1168

Figure Lengend Snippet: Average F1-scores (for Samples 1 through 10, E.coli / S. cerevisiae dataset) with coverage-depths ranging from 10 to 2000 are shown. Drummer : original Drummer algorithm with P -value and odds ratio-based threshold; Drummer + 1D clustering : Drummer algorithm (i.e. G-test statistic) with 1D score-clustering step (see Figure ). For detailed results across all samples, see and .

Article Snippet: The E. coli K12 MG1655 strain was grown in 1× LB Broth Miller (1st Base, Singapore) without antibiotics at 37°C at 160 rpm shaking.

Techniques:

Average F1-scores (for Samples 1 through 10, E.coli / S. cerevisiae dataset) with coverage-depths ranging from 10 to 2000 are depicted. Epinano: Epinano-DSE algorithm with z-score based threshold; Epinano + 1D clustering : Epinano-DSE algorithm with 1D score-clustering step (see Figure ). For detailed results across all samples, see and .

Journal: Nucleic Acids Research

Article Title: Detecting a wide range of epitranscriptomic modifications using a nanopore-sequencing-based computational approach with 1D score-clustering

doi: 10.1093/nar/gkae1168

Figure Lengend Snippet: Average F1-scores (for Samples 1 through 10, E.coli / S. cerevisiae dataset) with coverage-depths ranging from 10 to 2000 are depicted. Epinano: Epinano-DSE algorithm with z-score based threshold; Epinano + 1D clustering : Epinano-DSE algorithm with 1D score-clustering step (see Figure ). For detailed results across all samples, see and .

Article Snippet: The E. coli K12 MG1655 strain was grown in 1× LB Broth Miller (1st Base, Singapore) without antibiotics at 37°C at 160 rpm shaking.

Techniques: