scholarly journals Error filtering, pair assembly and error correction for next-generation sequencing reads

2015 ◽  
Vol 31 (21) ◽  
pp. 3476-3482 ◽  
Author(s):  
Robert C. Edgar ◽  
Henrik Flyvbjerg
Keyword(s):  
Next Generation Sequencing ◽  
Error Correction ◽  
Next Generation ◽  
Generation Sequencing ◽  
Error Filtering

scholarly journals Error correction of next-generation sequencing data and reliable estimation of HIV quasispecies

2010 ◽  
Vol 38 (21) ◽  
pp. 7400-7409 ◽  
Author(s):  
Osvaldo Zagordi ◽  
Rolf Klein ◽  
Martin Däumer ◽  
Niko Beerenwinkel
Keyword(s):  
Next Generation Sequencing ◽  
Error Correction ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Reliable Estimation ◽  
Generation Sequencing

scholarly journals A systematic comparison of error correction enzymes by next-generation sequencing

2017 ◽  
Author(s):  
Nathan B. Lubock ◽  
Di Zhang ◽  
George M. Church ◽  
Sriram Kosuri
Keyword(s):  
Next Generation Sequencing ◽  
Error Correction ◽  
Effective Means ◽  
Cost Effective ◽  
Error Rates ◽  
Gene Synthesis ◽  
Average Error ◽  
Synthesis Methods ◽  
Next Generation ◽  
Generation Sequencing

AbstractGene synthesis, the process of assembling gene-length fragments from shorter groups of oligonucleotides (oligos), is becoming an increasingly important tool in molecular and synthetic biology. The length, quality, and cost of gene synthesis is limited by errors produced during oligo synthesis and subsequent assembly. Enzymatic error correction methods are cost-effective means to ameliorate errors in gene synthesis. Previous analyses of these methods relied on cloning and Sanger sequencing to evaluate their efficiencies, limiting quantitative assessment and throughput. Here we develop a method to quantify errors in synthetic DNA by next-generation sequencing. We analyzed errors in a model gene assembly and systematically compared six different error correction enzymes across 11 conditions. We find that ErrASE and T7 Endonuclease I are the most effective at decreasing average error rates (up to 5.8-fold relative to the input), whereas MutS is the best for increasing the number of perfect assemblies (up to 25.2-fold). We are able to quantify differential specificities such as ErrASE preferentially corrects C/G → G/C transversions whereas T7 Endonuclease I preferentially corrects A/T → T/A transversions. More generally, this experimental and computational pipeline is a fast, scalable, and extensible way to analyze errors in gene assemblies, to profile error correction methods, and to benchmark DNA synthesis methods.

scholarly journals Benchmarking of computational error-correction methods for next-generation sequencing data

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Keith Mitchell ◽  
Jaqueline J. Brito ◽  
Igor Mandric ◽  
Qiaozhen Wu ◽  
Sergey Knyazev ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Error Correction ◽  
Next Generation Sequencing Data ◽  
Computational Error ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing

scholarly journals genBaRcode: a comprehensive R-package for genetic barcode analysis

2019 ◽  
Vol 36 (7) ◽  
pp. 2189-2194 ◽  
Author(s):  
Lars Thielecke ◽  
Kerstin Cornils ◽  
Ingmar Glauche
Keyword(s):  
User Interface ◽  
Next Generation Sequencing ◽  
Error Correction ◽  
Graphical User Interface ◽  
R Package ◽  
Next Generation ◽  
Analysis Strategies ◽  
Genetic Sequences ◽  
Reproducible Analysis ◽  
Generation Sequencing

Abstract Motivation Genetic barcodes have been established as an efficient method to trace clonal progeny of uniquely labeled cells by introducing artificial genetic sequences into the corresponding genomes. The assessment of those sequences relies on next generation sequencing and the subsequent analysis aiming to identify sequences of interest and correctly quantifying their abundance. Results We developed the genBaRcode package as a toolbox combining the flexibility of digesting next generation sequencing reads with or without a sophisticated barcode structure, with a variety of error-correction approaches and the availability of several types of visualization routines. Furthermore, a graphical user interface was incorporated to allow also less experienced R users package-based analyses. Finally, the provided tool is intended to bridge the gap between generating and analyzing barcode data and thereby supporting the establishment of standardized and reproducible analysis strategies. Availability and implementation The genBaRcode package is available at CRAN (https://cran.r-project.org/package=genBaRcode).

scholarly journals A systematic comparison of error correction enzymes by next-generation sequencing

2017 ◽  
Vol 45 (15) ◽  
pp. 9206-9217 ◽  
Author(s):  
Nathan B. Lubock ◽  
Di Zhang ◽  
Angus M. Sidore ◽  
George M. Church ◽  
Sriram Kosuri
Keyword(s):  
Next Generation Sequencing ◽  
Error Correction ◽  
Next Generation ◽  
Systematic Comparison ◽  
Generation Sequencing
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