scholarly journals Hot Start 7-Deaza-dGTP Improves Sanger Dideoxy Sequencing Data of GC-Rich Targets

10.5772/37922 ◽  
2012 ◽  
Author(s):  
Sabrina Shore ◽  
Elena Hidalgo ◽  
Natasha Paul
Keyword(s):  
Sequencing Data ◽  
Dideoxy Sequencing ◽  
Hot Start

DNA dideoxy sequencing with T7 DNA polymerase: improved sequencing data by the addition of manganese chloride

1990 ◽  
Vol 6 ◽  
pp. 2-3 ◽  
Author(s):  
Tom Kristensen ◽  
Hartmut Voss ◽  
Wilhelm Ansorge ◽  
Hans Prydz
Keyword(s):  
Dna Polymerase ◽  
Manganese Chloride ◽  
Sequencing Data ◽  
Dideoxy Sequencing

scholarly journals Improved Efficiency and Reliability of NGS Amplicon Sequencing Data Analysis for Genetic Diagnostic Procedures Using AGSA Software

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Axel Poulet ◽  
Maud Privat ◽  
Flora Ponelle ◽  
Sandrine Viala ◽  
Stephanie Decousus ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Low Cost ◽  
Familial Risk ◽  
454 Sequencing ◽  
Diagnostic Procedures ◽  
Amplicon Sequencing ◽  
Sequencing Data ◽  
Dideoxy Sequencing ◽  
Ideal Situation

Screening forBRCAmutations in women with familial risk of breast or ovarian cancer is an ideal situation for high-throughput sequencing, providing large amounts of low cost data. However, 454, Roche, and Ion Torrent, Thermo Fisher, technologies produce homopolymer-associated indel errors, complicating their use in routine diagnostics. We developed software, named AGSA, which helps to detect false positive mutations in homopolymeric sequences. Seventy-two familial breast cancer cases were analysed in parallel by amplicon 454 pyrosequencing and Sanger dideoxy sequencing for genetic variations of theBRCAgenes. All 565 variants detected by dideoxy sequencing were also detected by pyrosequencing. Furthermore, pyrosequencing detected 42 variants that were missed with Sanger technique. Six amplicons contained homopolymer tracts in the coding sequence that were systematically misread by the software supplied by Roche. Read data plotted as histograms by AGSA software aided the analysis considerably and allowed validation of the majority of homopolymers. As an optimisation, additional 250 patients were analysed using microfluidic amplification of regions of interest (Access Array Fluidigm) of the BRCA genes, followed by 454 sequencing and AGSA analysis. AGSA complements a complete line of high-throughput diagnostic sequence analysis, reducing time and costs while increasing reliability, notably for homopolymer tracts.

Prime-editors (nickases), hRad51–Cas9 nickase fusions and dCas9 have the same problem as conventional CRISPR-Cas9 of plasmid/Cas9 integration after making a double stranded break

2019 ◽  
Author(s):  
Sandeep Chakraborty
Keyword(s):  
Cellular Response ◽  
Sequencing Data ◽  
Dna Breaks ◽  
Precise Integration ◽  
Guide Rna ◽  
Double Strand Dna Breaks ◽  
Human Therapy ◽  
P53 Activation ◽  
Programmable Nucleases

‘Prime-editing’ proposes to replace traditional programmable nucleases (CRISPR-Cas9) using a catalytically impaired Cas9 (dCas9) connected to a engineered reverse transcriptase, and a guide RNA encoding both the target site and the desired change. With just a ‘nick’ on one strand, it is hypothe- sized, the negative, uncontrollable effects arising from double-strand DNA breaks (DSBs) - translocations, complex proteins, integrations and p53 activation - will be eliminated. However, sequencing data pro- vided (Accid:PRJNA565979) reveal plasmid integration, indicating that DSBs occur. Also, looking at only 16 off-targets is inadequate to assert that Prime-editing is more precise. Integration of plasmid occurs in all three versions (PE1/2/3). Interestingly, dCas9 which is known to be toxic in E. coli and yeast, is shown to have residual endonuclease activity. This also affects studies that use dCas9, like base- editors and de/methylations systems. Previous work using hRad51–Cas9 nickases also show significant integration in on-targets, as well as off-target integration [1]. Thus, we show that cellular response to nicking involves DSBs, and subsequent plasmid/Cas9 integration. This is an unacceptable outcome for any in vivo application in human therapy.

Clustering Count-based RNA Methylation Data Using a Nonparametric Generative Model

2018 ◽  
Vol 14 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Lin Zhang ◽  
Yanling He ◽  
Huaizhi Wang ◽  
Hui Liu ◽  
Yufei Huang ◽  
...  
Keyword(s):  
Clustering Analysis ◽  
Methylation Level ◽  
Optimal Number ◽  
Generative Model ◽  
Methylation Data ◽  
Sequencing Data ◽  
Number Of Clusters ◽  
Rna Methylation ◽  
Clustering Effect ◽  
Optimal Number Of Clusters

Background: RNA methylome has been discovered as an important layer of gene regulation and can be profiled directly with count-based measurements from high-throughput sequencing data. Although the detailed regulatory circuit of the epitranscriptome remains uncharted, clustering effect in methylation status among different RNA methylation sites can be identified from transcriptome-wide RNA methylation profiles and may reflect the epitranscriptomic regulation. Count-based RNA methylation sequencing data has unique features, such as low reads coverage, which calls for novel clustering approaches. <P><P> Objective: Besides the low reads coverage, it is also necessary to keep the integer property to approach clustering analysis of count-based RNA methylation sequencing data. <P><P> Method: We proposed a nonparametric generative model together with its Gibbs sampling solution for clustering analysis. The proposed approach implements a beta-binomial mixture model to capture the clustering effect in methylation level with the original count-based measurements rather than an estimated continuous methylation level. Besides, it adopts a nonparametric Dirichlet process to automatically determine an optimal number of clusters so as to avoid the common model selection problem in clustering analysis. <P><P> Results: When tested on the simulated system, the method demonstrated improved clustering performance over hierarchical clustering, K-means, MClust, NMF and EMclust. It also revealed on real dataset two novel RNA N6-methyladenosine (m6A) co-methylation patterns that may be induced directly by METTL14 and WTAP, which are two known regulatory components of the RNA m6A methyltransferase complex. <P><P> Conclusion: Our proposed DPBBM method not only properly handles the count-based measurements of RNA methylation data from sites of very low reads coverage, but also learns an optimal number of clusters adaptively from the data analyzed. <P><P> Availability: The source code and documents of DPBBM R package are freely available through the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/DPBBM/.

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