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In silico error correction improves cfDNA mutation calling.

ABSTRACT: Motivation:Circulating-free DNA (cfDNA) profiling by sequencing is an important minimally invasive protocol for monitoring the mutation profile of solid tumours in cancer patients. Since the concentration of available cfDNA is limited, sample library generation relies on multiple rounds of PCR amplification, during which the accumulation of errors results in reduced sensitivity and lower accuracy. Results:We present PCR Error Correction (PEC), an algorithm to identify and correct errors in short read sequencing data. It exploits the redundancy that arises from multiple rounds of PCR amplification. PEC is particularly well suited to applications such as single-cell sequencing and circulating tumour DNA (ctDNA) analysis, in which many cycles of PCR are used to generate sufficient DNA for sequencing from small amounts of starting material. When applied to ctDNA analysis, PEC significantly improves mutation calling accuracy, achieving similar levels of performance to more complex strategies that require additional protocol steps and access to calibration DNA datasets. Availability and implementation:PEC is available under the GPL-v3 Open Source licence, and is freely available from: https://github.com/CRUKMI-ComputationalBiology/PCR_Error_Correction.git. Supplementary information:Supplementary figures are available at Bioinformatics online.

SUBMITTER: Kim CS

PROVIDER: S-EPMC6612818 | biostudies-other | 2018 Dec

REPOSITORIES: biostudies-other

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In silico error correction improves cfDNA mutation calling.

Kim Chang Sik CS Mohan Sumitra S Ayub Mahmood M Rothwell Dominic G DG Dive Caroline C Brady Ged G Miller Crispin C

Bioinformatics (Oxford, England) 20190701 14

<h4>Motivation</h4>Circulating-free DNA (cfDNA) profiling by sequencing is an important minimally invasive protocol for monitoring the mutation profile of solid tumours in cancer patients. Since the concentration of available cfDNA is limited, sample library generation relies on multiple rounds of PCR amplification, during which the accumulation of errors results in reduced sensitivity and lower accuracy.<h4>Results</h4>We present PCR Error Correction (PEC), an algorithm to identify and correct ...[more]

PMID: 30520956

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Project description:BackgroundSeveral standalone error correction tools have been proposed to correct sequencing errors in Illumina data in order to facilitate de novo genome assembly. However, in a recent survey, we showed that state-of-the-art assemblers often did not benefit from this pre-correction step. We found that many error correction tools introduce new errors in reads that overlap highly repetitive DNA regions such as low-complexity patterns or short homopolymers, ultimately leading to a more fragmented assembly.ResultsWe propose BrownieCorrector, an error correction tool for Illumina sequencing data that focuses on the correction of only those reads that overlap short DNA patterns that are highly repetitive in the genome. BrownieCorrector extracts all reads that contain such a pattern and clusters them into different groups using a community detection algorithm that takes into account both the sequence similarity between overlapping reads and their respective paired-end reads. Each cluster holds reads that originate from the same genomic region and hence each cluster can be corrected individually, thus providing a consistent correction for all reads within that cluster.ConclusionsBrownieCorrector is benchmarked using six real Illumina datasets for different eukaryotic genomes. The prior use of BrownieCorrector improves assembly results over the use of uncorrected reads in all cases. In comparison with other error correction tools, BrownieCorrector leads to the best assembly results in most cases even though less than 2% of the reads within a dataset are corrected. Additionally, we investigate the impact of error correction on hybrid assembly where the corrected Illumina reads are supplemented with PacBio data. Our results confirm that BrownieCorrector improves the quality of hybrid genome assembly as well. BrownieCorrector is written in standard C++11 and released under GPL license. BrownieCorrector relies on multithreading to take advantage of multi-core/multi-CPU systems. The source code is available at https://github.com/biointec/browniecorrector .

Project description:Sickle cell anaemia (SCA) is one of the common autosomal recessive monogenic disorders, caused by a transverse point mutation (GAG > GTG) at the sixth codon of the beta-globin gene, which results in haemolytic anaemia due to the fragile RBCs. Recent progress in genome editing has gained attention for the therapeutic cure for SCA. Direct correction of SCA mutation by homology-directed repair relies on a double-strand break (DSB) at the target site and carries the risk of generating beta-thalassaemic mutations if the editing is not error-free. On the other hand, base editors cannot correct the pathogenic SCA mutation resulting from A > T base transversion. Prime editor (PE), the recently described CRISPR/Cas 9 based gene editing tool that enables precise gene manipulations without DSB and unintended nucleotide changes, is a viable approach for the treatment of SCA. However, the major limitation with the use of prime editing is the lower efficiency especially in human erythroid cell lines and primary cells. To overcome these limitations, we developed a modular lenti-viral based prime editor system and demonstrated its use for the precise modelling of SCA mutation and its subsequent correction in human erythroid cell lines. We achieved highly efficient installation of SCA mutation (up to 72%) and its subsequent correction in human erythroid cells. For the first time, we demonstrated the functional restoration of adult haemoglobin without any unintended nucleotide changes or indel formations using the PE2 system. We also validated that the off-target effects mediated by the PE2 system is very minimal even with very efficient on-target conversion, making it a safe therapeutic option. Taken together, the modular lenti-viral prime editor system developed in this study not only expands the range of cell lines targetable by prime editor but also improves the efficiency considerably, enabling the use of prime editor for myriad molecular, genetic, and translational studies.

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In silico error correction improves cfDNA mutation calling.

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In silico error correction improves cfDNA mutation calling.

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