Figure S3. Error rate correlation among different error-correction schemes. (a) Linear regression between true mutations and different error-correction methods. The model yâźx+a was adapted to do regression. Every dot represents a position on the target sequence and the values on x-axis and y-axis represent error rates of combined consensus and certain consensus, respectively. Colored lines are regression result. (b) Barplot of the intercepts a from the linear regression. Error bar is standard error. The colors represents different error-correction schemes, which are labeled in the graph. (EPS 477 kb
Feature contribution analysis in our CNN model. Table S2. Results of simple co-occurrence-based meth...
Sample sequencing data from MAQC, mutation screening re-sequencing, ENCODE, and PhiX DNA data sets. ...
Figure S3. Hierarchical clustering of expression levels, based on the rank of the count of exon per ...
BackgroundThe high error rate of next generation sequencing (NGS) restricts some of its applications...
Supplementary Figures S1-S13. Figure S1. Comparison of mutant allele fraction (MAF) in diluted sampl...
Table S1. Consensus sequence and gene rank correlation with case-control pairs using different metho...
Supplementary Tables S1-S5. Table S1. Datasets used. Information provided includes data type, provid...
Table S1. PacBio reads of insert protocol output metrics. Table S2. Padded and barcoded primer seque...
Background: Next-generation sequencing allows the analysis of an unprecedented number of viral seque...
(A) Distributions of the number of substitutions to the nearest deep read from the high and low-temp...
With read lengths of currently up to 2 × 300 bp, high throughput and low sequencing costs Illumina's...
Metagenomics and its processes have a great impact in biological advances. With the introduction of ...
A. False positive rate distributions for datasets A1s–A3s and A1m–A3m. Violin plot of distributions ...
Figure S1. Improvements in TGS will lead to further adoption. Figure S2. Performance plots on sensit...
© 2016 IEEE. Error correction is a critical initial step in next-generation sequencing (NGS) data an...
Feature contribution analysis in our CNN model. Table S2. Results of simple co-occurrence-based meth...
Sample sequencing data from MAQC, mutation screening re-sequencing, ENCODE, and PhiX DNA data sets. ...
Figure S3. Hierarchical clustering of expression levels, based on the rank of the count of exon per ...
BackgroundThe high error rate of next generation sequencing (NGS) restricts some of its applications...
Supplementary Figures S1-S13. Figure S1. Comparison of mutant allele fraction (MAF) in diluted sampl...
Table S1. Consensus sequence and gene rank correlation with case-control pairs using different metho...
Supplementary Tables S1-S5. Table S1. Datasets used. Information provided includes data type, provid...
Table S1. PacBio reads of insert protocol output metrics. Table S2. Padded and barcoded primer seque...
Background: Next-generation sequencing allows the analysis of an unprecedented number of viral seque...
(A) Distributions of the number of substitutions to the nearest deep read from the high and low-temp...
With read lengths of currently up to 2 × 300 bp, high throughput and low sequencing costs Illumina's...
Metagenomics and its processes have a great impact in biological advances. With the introduction of ...
A. False positive rate distributions for datasets A1s–A3s and A1m–A3m. Violin plot of distributions ...
Figure S1. Improvements in TGS will lead to further adoption. Figure S2. Performance plots on sensit...
© 2016 IEEE. Error correction is a critical initial step in next-generation sequencing (NGS) data an...
Feature contribution analysis in our CNN model. Table S2. Results of simple co-occurrence-based meth...
Sample sequencing data from MAQC, mutation screening re-sequencing, ENCODE, and PhiX DNA data sets. ...
Figure S3. Hierarchical clustering of expression levels, based on the rank of the count of exon per ...