Browse
Submit Data
Databases
API
Help

Dataset Information

21 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Accurate genomic variant detection in single cells with primary template-directed amplification.

ABSTRACT: Improvements in whole genome amplification (WGA) would enable new types of basic and applied biomedical research, including studies of intratissue genetic diversity that require more accurate single-cell genotyping. Here, we present primary template-directed amplification (PTA), an isothermal WGA method that reproducibly captures >95% of the genomes of single cells in a more uniform and accurate manner than existing approaches, resulting in significantly improved variant calling sensitivity and precision. To illustrate the types of studies that are enabled by PTA, we developed direct measurement of environmental mutagenicity (DMEM), a tool for mapping genome-wide interactions of mutagens with single living human cells at base-pair resolution. In addition, we utilized PTA for genome-wide off-target indel and structural variant detection in cells that had undergone CRISPR-mediated genome editing, establishing the feasibility for performing single-cell evaluations of biopsies from edited tissues. The improved precision and accuracy of variant detection with PTA overcomes the current limitations of accurate WGA, which is the major obstacle to studying genetic diversity and evolution at cellular resolution.

SUBMITTER: Gonzalez-Pena V

PROVIDER: S-EPMC8214697 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Accurate SNV detection in single cells by transposon-based whole-genome amplification of complementary strands.

Project description:Single-nucleotide variants (SNVs), pertinent to aging and disease, occur sporadically in the human genome, hence necessitating single-cell measurements. However, detection of single-cell SNVs suffers from false positives (FPs) due to intracellular single-stranded DNA damage and the process of whole-genome amplification (WGA). Here, we report a single-cell WGA method termed multiplexed end-tagging amplification of complementary strands (META-CS), which eliminates nearly all FPs by virtue of DNA complementarity, and achieved the highest accuracy thus far. We validated META-CS by sequencing kindred cells and human sperm, and applied it to other human tissues. Investigation of mature single human neurons revealed increasing SNVs with age and potentially unrepaired strand-specific oxidative guanine damage. We determined SNV frequencies along the genome in differentiated single human blood cells, and identified cell type-dependent mutational patterns for major types of lymphocytes.

| S-EPMC7923680 | biostudies-literature

Performance of four modern whole genome amplification methods for copy number variant detection in single cells.

Project description:Whole genome amplification (WGA) has become an invaluable tool to perform copy number variation (CNV) detection in single, or a limited number of cells. Unfortunately, current WGA methods introduce representation bias that limits the detection of small CNVs. New WGA methods have been introduced that might have the potential to reduce this bias. We compared the performance of PicoPLEX DNA-Seq (Picoseq), DOPlify, REPLI-g and Ampli-1 WGA for aneuploidy screening and copy number analysis using shallow whole genome massively parallel sequencing (MPS), starting from single or a limited number of cells. Although the four WGA methods perform differently, they are all suited for this application.

| S-EPMC5469777 | biostudies-literature

Variant analysis pipeline for accurate detection of genomic variants from transcriptome sequencing data.

Project description:The wealth of information deliverable from transcriptome sequencing (RNA-seq) is significant, however current applications for variant detection still remain a challenge due to the complexity of the transcriptome. Given the ability of RNA-seq to reveal active regions of the genome, detection of RNA-seq SNPs can prove valuable in understanding the phenotypic diversity between populations. Thus, we present a novel computational workflow named VAP (Variant Analysis Pipeline) that takes advantage of multiple RNA-seq splice aware aligners to call SNPs in non-human models using RNA-seq data only. We applied VAP to RNA-seq from a highly inbred chicken line and achieved high accuracy when compared with the matching whole genome sequencing (WGS) data. Over 65% of WGS coding variants were identified from RNA-seq. Further, our results discovered SNPs resulting from post transcriptional modifications, such as RNA editing, which may reveal potentially functional variation that would have otherwise been missed in genomic data. Even with the limitation in detecting variants in expressed regions only, our method proves to be a reliable alternative for SNP identification using RNA-seq data. The source code and user manuals are available at https://modupeore.github.io/VAP/.

| S-EPMC6756534 | biostudies-literature

Monovar: single-nucleotide variant detection in single cells.

Project description:Current variant callers are not suitable for single-cell DNA sequencing, as they do not account for allelic dropout, false-positive errors and coverage nonuniformity. We developed Monovar (https://bitbucket.org/hamimzafar/monovar), a statistical method for detecting and genotyping single-nucleotide variants in single-cell data. Monovar exhibited superior performance over standard algorithms on benchmarks and in identifying driver mutations and delineating clonal substructure in three different human tumor data sets.

| S-EPMC4887298 | biostudies-literature

DNA template strand sequencing of single-cells maps genomic rearrangements at high resolution.

Project description:DNA rearrangements such as sister chromatid exchanges (SCEs) are sensitive indicators of genomic stress and instability, but they are typically masked by single-cell sequencing techniques. We developed Strand-seq to independently sequence parental DNA template strands from single cells, making it possible to map SCEs at orders-of-magnitude greater resolution than was previously possible. On average, murine embryonic stem (mES) cells exhibit eight SCEs, which are detected at a resolution of up to 23 bp. Strikingly, Strand-seq of 62 single mES cells predicts that the mm 9 mouse reference genome assembly contains at least 17 incorrectly oriented segments totaling nearly 1% of the genome. These misoriented contigs and fragments have persisted through several iterations of the mouse reference genome and have been difficult to detect using conventional sequencing techniques. The ability to map SCE events at high resolution and fine-tune reference genomes by Strand-seq dramatically expands the scope of single-cell sequencing.

| S-EPMC3580294 | biostudies-literature

Droplet microfluidics for amplification-free genetic detection of single cells.

Project description:In this article we present a novel droplet microfluidic chip enabling amplification-free detection of single pathogenic cells. The device streamlines multiple functionalities to carry out sample digitization, cell lysis, probe-target hybridization for subsequent fluorescent detection. A peptide nucleic acid fluorescence resonance energy transfer probe (PNA beacon) is used to detect 16S rRNA present in pathogenic cells. Initially the sensitivity and quantification abilities of the platform are tested using a synthetic target mimicking the actual expression level of 16S rRNA in single cells. The capability of the device to perform "sample-to-answer" pathogen detection of single cells is demonstrated using E. coli as a model pathogen.

| S-EPMC3696383 | biostudies-literature

A Sensitive and Accurate Recombinase Polymerase Amplification Assay for Detection of the Primary Bacterial Pathogens Causing Bovine Respiratory Disease.

Project description:Rapid and accurate diagnosis of bovine respiratory disease (BRD) presents a substantial challenge to the North American cattle industry. Here we utilize recombinase polymerase amplification (RPA), a fast and sensitive isothermal DNA-based technology for the detection of four BRD pathogens (Mannheimia haemolytica, Pasteurella multocida, Histophilus somni, Mycoplasma bovis), genes coding antimicrobial resistance (AMR) and integrative conjugative elements (ICE) which can harbor AMR genes. Eleven RPA assays were designed and validated including: a) one conventional species-specific multiplex assay targeting the 4 BRD pathogens, b) two species-specific real-time multiplex RPA assays targeting M. haemolytica/M. bovis and P. multocida/H. somni, respectively with a novel competitive internal amplification control, c) seven conventional assays targeting AMR genes (tetH, tetR, msrE, mphE, sul2, floR, erm42), and d) one real-time assay targeting ICE. Each real-time RPA assay was tested on 100 deep nasopharyngeal swabs (DNPS) collected from feedlot cattle previously assessed for targets using either culture methods and/or polymerase chain reaction (PCR) verification (TC-PCR). The developed RPA assays enabled sensitive and accurate identification of BRD agents and AMR/ICE genes directly from DNPS, in a shorter period than TC-PCR, showing considerable promise as a tool for point-of-care identification of BRD pathogens and antimicrobial resistance genes.

| S-EPMC7212441 | biostudies-literature

Accurate somatic variant detection using weakly supervised deep learning.

Project description:Identification of somatic mutations in tumor samples is commonly based on statistical methods in combination with heuristic filters. Here we develop VarNet, an end-to-end deep learning approach for identification of somatic variants from aligned tumor and matched normal DNA reads. VarNet is trained using image representations of 4.6 million high-confidence somatic variants annotated in 356 tumor whole genomes. We benchmark VarNet across a range of publicly available datasets, demonstrating performance often exceeding current state-of-the-art methods. Overall, our results demonstrate how a scalable deep learning approach could augment and potentially supplant human engineered features and heuristic filters in somatic variant calling.

| S-EPMC9307817 | biostudies-literature

A duplex probe-directed recombinase amplification assay for detection of single nucleotide polymorphisms on 8q24 associated with prostate cancer.

Project description:Single nucleotide polymorphisms (SNPs) have important application value in the research of population genetics, hereditary diseases, tumors, and drug development. Conventional methods for detecting SNPs are typically based on PCR or DNA sequencing, which is time-consuming, costly, and requires complex instrumentation. In this study, we present a duplex probe-directed recombinase amplification (duplex-PDRA) assay that can perform real-time detection of two SNPs (rs6983267 and rs1447295) in four reactions in two tubes at 39°C within 30 min. The sensitivity of duplex-PDRA was 2×103-104 copies per reaction and no cross-reactivity was observed. A total of 382 clinical samples (179 prostate cancer patients and 203 controls) from northern China were collected and tested by duplex-PDRA assay and direct sequencing. The genotyping results were completely identical. In addition, the association analysis of two SNPs with prostate cancer risk and bone metastasis was conducted. We found that the TT genotype of rs6983267 (OR: 0.42; 95%CI: 0.23-0.78; P=0.005) decreased the risk of prostate cancer, while the CA genotype of rs1447295 (OR: 1.89; 95%CI: 1.20-2.96; P=0.005) increased the risk of prostate cancer. However, no association between the two SNPs (rs6983267 and rs1447295) and bone metastasis in prostate cancer was found in this study (P>0.05). In conclusion, the duplex-PDRA assay is an effective method for the simultaneous detection of two SNPs and shows great potential for widespread use in research and clinical settings.

| S-EPMC7695445 | biostudies-literature

MotorPlex provides accurate variant detection across large muscle genes both in single myopathic patients and in pools of DNA samples.

Project description:Mutations in ~100 genes cause muscle diseases with complex and often unexplained genotype/phenotype correlations. Next-generation sequencing studies identify a greater-than-expected number of genetic variations in the human genome. This suggests that existing clinical monogenic testing systematically miss very relevant information.We have created a core panel of genes that cause all known forms of nonsyndromic muscle disorders (MotorPlex). It comprises 93 loci, among which are the largest and most complex human genes, such as TTN, RYR1, NEB and DMD. MotorPlex captures at least 99.2% of 2,544 exons with a very accurate and uniform coverage. This quality is highlighted by the discovery of 20-30% more variations in comparison with whole exome sequencing. The coverage homogeneity has also made feasible to apply a cost-effective pooled sequencing strategy while maintaining optimal sensitivity and specificity.We studied 177 unresolved cases of myopathies for which the best candidate genes were previously excluded. We have identified known pathogenic variants in 52 patients and potential causative ones in further 56 patients. We have also discovered 23 patients showing multiple true disease-associated variants suggesting complex inheritance. Moreover, we frequently detected other nonsynonymous variants of unknown significance in the largest muscle genes. Cost-effective combinatorial pools of DNA samples were similarly accurate (97-99%). MotorPlex is a very robust platform that overcomes for power, costs, speed, sensitivity and specificity the gene-by-gene strategy. The applicability of pooling makes this tool affordable for the screening of genetic variability of muscle genes also in a larger population. We consider that our strategy can have much broader applications.

| S-EPMC4172906 | biostudies-other

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data