simpls algorithm Search Results


simple triage and rapid treatment (start)  (Jumpstart Fertility)
90
Jumpstart Fertility simple triage and rapid treatment (start)
Simple Triage And Rapid Treatment (Start), supplied by Jumpstart Fertility, 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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simple clustering algorithm varclus  (SAS institute)
90
SAS institute simple clustering algorithm varclus
Simple Clustering Algorithm Varclus, supplied by SAS institute, 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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simple algorithm for very efficient multiplexing of oxford nanopore experiments for you  (Oxford Nanopore)
90
Oxford Nanopore simple algorithm for very efficient multiplexing of oxford nanopore experiments for you
(a) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. (b) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in (a). Of note, “omitted” represents reads that did not cover the focused position. (c) Probability logo plot. Statistical significance (−log 10 [P value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in (a) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. (d) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the “worst-case scenario”, i.e., top panel in (b), the probability of incorrect classification of read was calculated assuming that two plasmids that differ by indicated base(s) were mixed. (e) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the “worst-case scenario”, i.e., top panel in (b), the indicated number of reads were generated in silico, and the consensus base calling was calculated using <t>SAVEMONEY.</t> The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling.
Simple Algorithm For Very Efficient Multiplexing Of Oxford Nanopore Experiments For You, supplied by Oxford Nanopore, 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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Average 90 stars, based on 1 article reviews
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simple adaptive difference algorithm  (Shibaura Mechatronics)
90
Shibaura Mechatronics simple adaptive difference algorithm
(a) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. (b) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in (a). Of note, “omitted” represents reads that did not cover the focused position. (c) Probability logo plot. Statistical significance (−log 10 [P value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in (a) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. (d) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the “worst-case scenario”, i.e., top panel in (b), the probability of incorrect classification of read was calculated assuming that two plasmids that differ by indicated base(s) were mixed. (e) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the “worst-case scenario”, i.e., top panel in (b), the indicated number of reads were generated in silico, and the consensus base calling was calculated using <t>SAVEMONEY.</t> The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling.
Simple Adaptive Difference Algorithm, supplied by Shibaura Mechatronics, 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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lorawan—simple rate adaptation recommended algorithm class a/b specification  (SEMTech Inc)
90
SEMTech Inc lorawan—simple rate adaptation recommended algorithm class a/b specification
(a) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. (b) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in (a). Of note, “omitted” represents reads that did not cover the focused position. (c) Probability logo plot. Statistical significance (−log 10 [P value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in (a) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. (d) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the “worst-case scenario”, i.e., top panel in (b), the probability of incorrect classification of read was calculated assuming that two plasmids that differ by indicated base(s) were mixed. (e) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the “worst-case scenario”, i.e., top panel in (b), the indicated number of reads were generated in silico, and the consensus base calling was calculated using <t>SAVEMONEY.</t> The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling.
Lorawan—Simple Rate Adaptation Recommended Algorithm Class A/B Specification, supplied by SEMTech Inc, 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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simple kriging algorithm  (Esri inc)
90
Esri inc simple kriging algorithm
(a) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. (b) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in (a). Of note, “omitted” represents reads that did not cover the focused position. (c) Probability logo plot. Statistical significance (−log 10 [P value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in (a) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. (d) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the “worst-case scenario”, i.e., top panel in (b), the probability of incorrect classification of read was calculated assuming that two plasmids that differ by indicated base(s) were mixed. (e) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the “worst-case scenario”, i.e., top panel in (b), the indicated number of reads were generated in silico, and the consensus base calling was calculated using <t>SAVEMONEY.</t> The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling.
Simple Kriging Algorithm, supplied by Esri inc, 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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simple hash algorithm (sha)  (National Institute of Standards and Technology)
90
National Institute of Standards and Technology simple hash algorithm (sha)
(a) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. (b) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in (a). Of note, “omitted” represents reads that did not cover the focused position. (c) Probability logo plot. Statistical significance (−log 10 [P value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in (a) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. (d) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the “worst-case scenario”, i.e., top panel in (b), the probability of incorrect classification of read was calculated assuming that two plasmids that differ by indicated base(s) were mixed. (e) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the “worst-case scenario”, i.e., top panel in (b), the indicated number of reads were generated in silico, and the consensus base calling was calculated using <t>SAVEMONEY.</t> The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling.
Simple Hash Algorithm (Sha), supplied by National Institute of Standards and Technology, 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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simple hash algorithm (sha) - by Bioz Stars, 2026-03
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simple pressure-velocity coupling algorithm  (Siemens AG)
90
Siemens AG simple pressure-velocity coupling algorithm
(a) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. (b) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in (a). Of note, “omitted” represents reads that did not cover the focused position. (c) Probability logo plot. Statistical significance (−log 10 [P value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in (a) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. (d) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the “worst-case scenario”, i.e., top panel in (b), the probability of incorrect classification of read was calculated assuming that two plasmids that differ by indicated base(s) were mixed. (e) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the “worst-case scenario”, i.e., top panel in (b), the indicated number of reads were generated in silico, and the consensus base calling was calculated using <t>SAVEMONEY.</t> The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling.
Simple Pressure Velocity Coupling Algorithm, supplied by Siemens AG, 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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Average 90 stars, based on 1 article reviews
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mapping algorithm simple metric procedure  (Nokia Corporation)
90
Nokia Corporation mapping algorithm simple metric procedure
(a) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. (b) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in (a). Of note, “omitted” represents reads that did not cover the focused position. (c) Probability logo plot. Statistical significance (−log 10 [P value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in (a) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. (d) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the “worst-case scenario”, i.e., top panel in (b), the probability of incorrect classification of read was calculated assuming that two plasmids that differ by indicated base(s) were mixed. (e) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the “worst-case scenario”, i.e., top panel in (b), the indicated number of reads were generated in silico, and the consensus base calling was calculated using <t>SAVEMONEY.</t> The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling.
Mapping Algorithm Simple Metric Procedure, supplied by Nokia Corporation, 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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Average 90 stars, based on 1 article reviews
mapping algorithm simple metric procedure - by Bioz Stars, 2026-03
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compressed pattern matching automata  (Takeda)
90
Takeda compressed pattern matching automata
(a) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. (b) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in (a). Of note, “omitted” represents reads that did not cover the focused position. (c) Probability logo plot. Statistical significance (−log 10 [P value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in (a) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. (d) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the “worst-case scenario”, i.e., top panel in (b), the probability of incorrect classification of read was calculated assuming that two plasmids that differ by indicated base(s) were mixed. (e) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the “worst-case scenario”, i.e., top panel in (b), the indicated number of reads were generated in silico, and the consensus base calling was calculated using <t>SAVEMONEY.</t> The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling.
Compressed Pattern Matching Automata, supplied by Takeda, 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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simple estimation-of-distribution algorithms (edas) such as the umda  (PopulationGenetics)
90
PopulationGenetics simple estimation-of-distribution algorithms (edas) such as the umda
(a) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. (b) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in (a). Of note, “omitted” represents reads that did not cover the focused position. (c) Probability logo plot. Statistical significance (−log 10 [P value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in (a) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. (d) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the “worst-case scenario”, i.e., top panel in (b), the probability of incorrect classification of read was calculated assuming that two plasmids that differ by indicated base(s) were mixed. (e) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the “worst-case scenario”, i.e., top panel in (b), the indicated number of reads were generated in silico, and the consensus base calling was calculated using <t>SAVEMONEY.</t> The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling.
Simple Estimation Of Distribution Algorithms (Edas) Such As The Umda, supplied by PopulationGenetics, 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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partial least squares calculated with the simpls algorithm  (Eigenvector Research Inc)
90
Eigenvector Research Inc partial least squares calculated with the simpls algorithm
(a) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. (b) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in (a). Of note, “omitted” represents reads that did not cover the focused position. (c) Probability logo plot. Statistical significance (−log 10 [P value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in (a) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. (d) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the “worst-case scenario”, i.e., top panel in (b), the probability of incorrect classification of read was calculated assuming that two plasmids that differ by indicated base(s) were mixed. (e) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the “worst-case scenario”, i.e., top panel in (b), the indicated number of reads were generated in silico, and the consensus base calling was calculated using <t>SAVEMONEY.</t> The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling.
Partial Least Squares Calculated With The Simpls Algorithm, supplied by Eigenvector Research Inc, 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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partial least squares calculated with the simpls algorithm - by Bioz Stars, 2026-03
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Image Search Results


(a) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. (b) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in (a). Of note, “omitted” represents reads that did not cover the focused position. (c) Probability logo plot. Statistical significance (−log 10 [P value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in (a) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. (d) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the “worst-case scenario”, i.e., top panel in (b), the probability of incorrect classification of read was calculated assuming that two plasmids that differ by indicated base(s) were mixed. (e) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the “worst-case scenario”, i.e., top panel in (b), the indicated number of reads were generated in silico, and the consensus base calling was calculated using SAVEMONEY. The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling.

Journal: bioRxiv

Article Title: Barcode-free multiplex plasmid sequencing using Bayesian analysis and nanopore sequencing

doi: 10.1101/2023.04.12.536413

Figure Lengend Snippet: (a) Density plot of the rate of reads with correct base calling. The rate was calculated at each position of plasmids and displayed using representative nanopore sequencing results. (b) Averaged quality score distribution of reads with correct rate of less than 0.7 (upper panel) and more than 0.9 (bottom panel). The corresponding regions are displayed with dashed red frames in (a). Of note, “omitted” represents reads that did not cover the focused position. (c) Probability logo plot. Statistical significance (−log 10 [P value]) was calculated for a 5-mer around the positions that showed correct rate lower than 0.7 in (a) using those that showed more than 0.9 as a background. Enriched residues are stacked on the top, whereas depleted residues are stacked on the bottom. (d) Estimated probability of incorrect classification. Based on the match/mismatch/deletion ratio of reads obtained in the “worst-case scenario”, i.e., top panel in (b), the probability of incorrect classification of read was calculated assuming that two plasmids that differ by indicated base(s) were mixed. (e) Estimated probability of correct/incorrect consensus base calling. Based on the quality score distribution obtained in the “worst-case scenario”, i.e., top panel in (b), the indicated number of reads were generated in silico, and the consensus base calling was calculated using SAVEMONEY. The simulation was performed 10,000 times for each condition to calculate the probability of correct/incorrect consensus base calling.

Article Snippet: We develop a computational approach termed Simple Algorithm for Very Efficient Multiplexing of Oxford Nanopore Experiments for You (SAVEMONEY) that guides researchers to mix multiple plasmids and subsequently computationally de-mixes the resultant sequences.

Techniques: Nanopore Sequencing, Generated, In Silico