scholarly journals Gaussian decomposition of high-resolution melt curve derivatives for measuring genome-editing efficiency

2017 ◽  
Author(s):  
Michail Zaboikin ◽  
Carl Freter ◽  
Narasimhachar Srinivasakumar
Keyword(s):  
High Resolution ◽  
Genome Editing ◽  
Next Generation Sequencing Data ◽  
Gaussian Component ◽  
Sequencing Data ◽  
High Resolution Melt ◽  
Editing Efficiency ◽  
Target Sites ◽  
Corrected Data ◽  
Derivatives Of

AbstractWe describe a method for measuring genome editing efficiency from in silico analysis of high-resolution melt curve data. The melt curve data derived from amplicons of genome-edited or unmodified target sites were processed to remove the background fluorescent signal emanating from free fluorophore and then corrected for temperature-dependent quenching of fluorescence of double-stranded DNA-bound fluorophore. Corrected data were normalized and numerically differentiated to obtain the first derivatives of the melt curves. These were then mathematically modeled as a sum or superposition of minimal number of Gaussian components. Using Gaussian parameters determined by modeling of melt curve derivatives of unedited samples, we were able to model melt curve derivatives from genetically altered target sites where the mutant population could be accommodated using an additional Gaussian component. From this, the proportion contributed by the mutant component in the target region amplicon could be accurately determined. Mutant component computations compared well with the mutant frequency determination from next generation sequencing data. The results were also consistent with our earlier studies that used difference curve areas from high-resolution melt curves for determining the efficiency of genome-editing reagents. The advantage of the described method is that it does not require calibration curves to estimate proportion of mutants in amplicons of genome-edited target sites.

scholarly journals Gaussian decomposition of high-resolution melt curve derivatives for measuring genome-editing efficiency

PLoS ONE ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. e0190192 ◽  
Author(s):  
Michail Zaboikin ◽  
Carl Freter ◽  
Narasimhachar Srinivasakumar
Keyword(s):  
High Resolution ◽  
Genome Editing ◽  
High Resolution Melt ◽  
Editing Efficiency ◽  
Gaussian Decomposition

scholarly journals Bivartect: accurate and memory-saving breakpoint detection by direct read comparison

2020 ◽  
Vol 36 (9) ◽  
pp. 2725-2730
Author(s):  
Keisuke Shimmura ◽  
Yuki Kato ◽  
Yukio Kawahara
Keyword(s):  
Genome Editing ◽  
High Throughput Sequencing ◽  
Variant Calling ◽  
Simulated Data ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Single Nucleotide Variants ◽  
Single Node ◽  
Single Nucleotide ◽  
Target Sites

Abstract Motivation Genetic variant calling with high-throughput sequencing data has been recognized as a useful tool for better understanding of disease mechanism and detection of potential off-target sites in genome editing. Since most of the variant calling algorithms rely on initial mapping onto a reference genome and tend to predict many variant candidates, variant calling remains challenging in terms of predicting variants with low false positives. Results Here we present Bivartect, a simple yet versatile variant caller based on direct comparison of short sequence reads between normal and mutated samples. Bivartect can detect not only single nucleotide variants but also insertions/deletions, inversions and their complexes. Bivartect achieves high predictive performance with an elaborate memory-saving mechanism, which allows Bivartect to run on a computer with a single node for analyzing small omics data. Tests with simulated benchmark and real genome-editing data indicate that Bivartect was comparable to state-of-the-art variant callers in positive predictive value for detection of single nucleotide variants, even though it yielded a substantially small number of candidates. These results suggest that Bivartect, a reference-free approach, will contribute to the identification of germline mutations as well as off-target sites introduced during genome editing with high accuracy. Availability and implementation Bivartect is implemented in C++ and available along with in silico simulated data at https://github.com/ykat0/bivartect. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Peptide fusion improves prime editing efficiency

2021 ◽  
Author(s):  
Minja Velimirovic ◽  
Larissa Zanetti ◽  
Max W Shen ◽  
James D Fife ◽  
Lin Lin ◽  
...  
Keyword(s):  
Dna Repair ◽  
Amino Acid ◽  
Genome Editing ◽  
Cell Lines ◽  
High Throughput ◽  
Editing Efficiency ◽  
Human Dna ◽  
Target Sites ◽  
Multiple Cell ◽  
Related Proteins

Prime editing enables search-and-replace genome editing but is limited by low editing efficiency. We present a high-throughput approach, PepSEq, to measure how fusion of 12,000 85-amino acid peptides derived from human DNA repair-related proteins influences prime editing efficiency. We show that peptide fusion can enhance prime editing, prime-enhancing peptides combine productively, and a top dual peptide-prime editor increases prime editing significantly in multiple cell lines across dozens of target sites.

scholarly journals BATCH-GE: Batch analysis of Next-Generation Sequencing data for genome editing assessment

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Annekatrien Boel ◽  
Woutert Steyaert ◽  
Nina De Rocker ◽  
Björn Menten ◽  
Bert Callewaert ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Genome Editing ◽  
Targeted Mutagenesis ◽  
Read Length ◽  
Next Generation Sequencing Data ◽  
Model Organisms ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing ◽  
Batch Analysis

Abstract Targeted mutagenesis by the CRISPR/Cas9 system is currently revolutionizing genetics. The ease of this technique has enabled genome engineering in-vitro and in a range of model organisms and has pushed experimental dimensions to unprecedented proportions. Due to its tremendous progress in terms of speed, read length, throughput and cost, Next-Generation Sequencing (NGS) has been increasingly used for the analysis of CRISPR/Cas9 genome editing experiments. However, the current tools for genome editing assessment lack flexibility and fall short in the analysis of large amounts of NGS data. Therefore, we designed BATCH-GE, an easy-to-use bioinformatics tool for batch analysis of NGS-generated genome editing data, available from http://. BATCH-GE detects and reports indel mutations and other precise genome editing events and calculates the corresponding mutagenesis efficiencies for a large number of samples in parallel. Furthermore, this new tool provides flexibility by allowing the user to adapt a number of input variables. The performance of BATCH-GE was evaluated in two genome editing experiments, aiming to generate knock-out and knock-in zebrafish mutants. This tool will not only contribute to the evaluation of CRISPR/Cas9-based experiments, but will be of use in any genome editing experiment and has the ability to analyze data from every organism with a sequenced genome.

scholarly journals Analysis and comparison of genome editing using CRISPResso2

2018 ◽  
Author(s):  
Kendell Clement ◽  
Holly Rees ◽  
Matthew C. Canver ◽  
Jason M. Gehrke ◽  
Rick Farouni ◽  
...  
Keyword(s):  
Genome Editing ◽  
Deep Sequencing ◽  
Sequencing Data ◽  
Editing Efficiency ◽  
Deep Sequencing Data ◽  
Allele Specific

AbstractGenome editing technologies are rapidly evolving, and analysis of deep sequencing data from target or off-target regions is necessary for measuring editing efficiency and evaluating safety. However, no software exists to analyze base editors, perform allele-specific quantification or that incorporates biologically-informed and scalable alignment approaches. Here, we present CRISPResso2 to fill this gap and illustrate its functionality by experimentally measuring and analyzing the editing properties of six genome editing agents.

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