Systematic benchmarking of tools for CpG methylation detection from nanopore sequencing.
Ontology highlight
ABSTRACT:
SUBMITTER: Yuen ZW
PROVIDER: S-EPMC8187371 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: Not available
| S-EPMC11342253 | biostudies-literature
Project description:Fascioscapulohumeral muscular dystrophy (FSHD) is caused by a unique genetic mechanism that relies on contraction and hypomethylation of the D4Z4 macrosatellite array on the Chromosome 4q telomere allowing ectopic expression of the DUX4 gene in skeletal muscle. Genetic analysis is difficult because of the large size and repetitive nature of the array, a nearly identical array on the 10q telomere, and the presence of divergent D4Z4 arrays scattered throughout the genome. Here, we combine nanopore long-read sequencing with Cas9-targeted enrichment of 4q and 10q D4Z4 arrays for comprehensive genetic analysis including determination of the length of the 4q and 10q D4Z4 arrays with base-pair resolution. In the same assay, we differentiate 4q from 10q telomeric sequences, determine A/B haplotype, identify paralogous D4Z4 sequences elsewhere in the genome, and estimate methylation for all CpGs in the array. Asymmetric, length-dependent methylation gradients were observed in the 4q and 10q D4Z4 arrays that reach a hypermethylation point at approximately 10 D4Z4 repeat units, consistent with the known threshold of pathogenic D4Z4 contractions. High resolution analysis of individual D4Z4 repeat methylation revealed areas of low methylation near the CTCF/insulator region and areas of high methylation immediately preceding the DUX4 transcriptional start site. Within the DUX4 exons, we observed a waxing/waning methylation pattern with a 180-nucleotide periodicity, consistent with phased nucleosomes. Targeted nanopore sequencing complements recently developed molecular combing and optical mapping approaches to genetic analysis for FSHD by adding precision of the length measurement, base-pair resolution sequencing, and quantitative methylation analysis.
| S-EPMC10620044 | biostudies-literature
Project description:Fascioscapulohumeral muscular dystrophy (FSHD) is caused by a unique genetic mechanism that relies on contraction and hypomethylation of the D4Z4 macrosatellite array on the chromosome 4q telomere allowing ectopic expression of the DUX4 gene in skeletal muscle. Genetic analysis is difficult due to the large size and repetitive nature of the array, a nearly identical array on the 10q telomere, and the presence of divergent D4Z4 arrays scattered throughout the genome. Here, we combine nanopore long-read sequencing with Cas9-targeted enrichment of 4q and 10q D4Z4 arrays for comprehensive genetic analysis including determination of the length of the 4q and 10q D4Z4 arrays with base-pair resolution. In the same assay, we differentiate 4q from 10q telomeric sequences, determine A/B haplotype, identify paralogous D4Z4 sequences elsewhere in the genome, and estimate methylation for all CpGs in the array. Asymmetric, length-dependent methylation gradients were observed in the 4q and 10q D4Z4 arrays that reach a hypermethylation point at approximately 10 D4Z4 repeat units, consistent with the known threshold of pathogenic D4Z4 contractions. High resolution analysis of individual D4Z4 repeat methylation revealed areas of low methylation near the CTCF/insulator region and areas of high methylation immediately preceding the DUX4 transcriptional start site. Within the DUX4 exons, we observed a waxing/waning methylation pattern with a 180-nucleotide periodicity, consistent with phased nucleosomes. Targeted nanopore sequencing complements recently developed molecular combing and optical mapping approaches to genetic analysis for FSHD by adding precision of the length measurement, base-pair resolution sequencing, and quantitative methylation analysis.
| S-EPMC9949141 | biostudies-literature
Project description:RNA, like DNA and proteins, can undergo modifications. To date, over 170 RNA modifications have been identified, leading to the emergence of a new research area known as epitranscriptomics. RNA editing is the most frequent RNA modification in mammalian transcriptomes, and two types have been identified: (1) the most frequent, adenosine to inosine (A-to-I); and (2) the less frequent, cysteine to uracil (C-to-U) RNA editing. Unlike other epitranscriptomic marks, RNA editing can be readily detected from RNA sequencing (RNA-seq) data without any chemical conversions of RNA before sequencing library preparation. Furthermore, analyzing RNA editing patterns from transcriptomic data provides an additional layer of information about the epitranscriptome. As the significance of epitranscriptomics, particularly RNA editing, gains recognition in various fields of biology and medicine, there is a growing interest in detecting RNA editing sites (RES) by analyzing RNA-seq data. To cope with this increased interest, several bioinformatic tools are available. However, each tool has its advantages and disadvantages, which makes the choice of the most appropriate tool for bench scientists and clinicians difficult. Here, we have benchmarked bioinformatic tools to detect RES from RNA-seq data. We provide a comprehensive view of each tool and its performance using previously published RNA-seq data to suggest recommendations on the most appropriate for utilization in future studies.
| S-EPMC10527054 | biostudies-literature
Project description:Whole genome sequencing provides rapid insight into key information about the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), such as virus typing and key mutation site, and this information is important for precise prevention, control and tracing of coronavirus disease 2019 (COVID-19) outbreak in conjunction with the epidemiological information of the case. Nanopore sequencing is widely used around the world for its short sample-to-result time, simple experimental operation and long sequencing reads. However, because nanopore sequencing is a relatively new sequencing technology, many researchers still have doubts about its accuracy. The combination of the newly launched nanopore sequencing Q20+ kit (LSK112) and flow cell R10.4 is a qualitative improvement over the accuracy of the previous kits. In this study, we firstly used LSK112 kit with flow cell R10.4 to sequence the SARS-CoV-2 whole genome, and summarized the sequencing results of the combination of LSK112 kit and flow cell R10.4 for the 1200bp amplicons of SARS-CoV-2. We found that the proportion of sequences with an accuracy of more than 99% reached 30.1%, and the average sequence accuracy reached 98.34%, while the results of the original combination of LSK109 kit and flow cell R9.4.1 were 0.61% and 96.52%, respectively. The mutation site analysis showed that it was completely consistent with the final consensus sequence of next generation sequencing (NGS). The results showed that the combination of LSK112 kit and flow cell R10.4 allowed rapid whole-genome sequencing of SARS-CoV-2 without the need for verification of NGS.
| S-EPMC9612837 | biostudies-literature
Project description:MOTIVATION:As the number of studies looking at differences between DNA methylation increases, there is a growing demand to develop and benchmark statistical methods to analyse these data. To date no objective approach for the comparison of these methods has been developed and as such it remains difficult to assess which analysis tool is most appropriate for a given experiment. As a result, there is an unmet need for a DNA methylation data simulator that can accurately reproduce a wide range of experimental setups, and can be routinely used to compare the performance of different statistical models. RESULTS:We have developed WGBSSuite, a flexible stochastic simulation tool that generates single-base resolution DNA methylation data genome-wide. Several simulator parameters can be derived directly from real datasets provided by the user in order to mimic real case scenarios. Thus, it is possible to choose the most appropriate statistical analysis tool for a given simulated design. To show the usefulness of our simulator, we also report a benchmark of commonly used methods for differential methylation analysis. AVAILABILITY AND IMPLEMENTATION:WGBS code and documentation are available under GNU licence at http://www.wgbssuite.org.uk/ CONTACT:: owen.rackham@imperial.ac.uk or l.bottolo@imperial.ac.uk SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC4495289 | biostudies-literature
Project description:State-of-the-art algorithms for m6A detection and quantification via nanopore direct RNA sequencing have been continuously developed, little is known about their capacities and limitations, which makes a comprehensive assessment in urgent need. Therefore, we performed comprehensive benchmarking of 10 computational tools relying on current-based and base-calling “errors” strategies for m6A detection by nanopore sequencing.
2023-03-01 | GSE195618 | GEO
Project description:Computational omics methods packaged as software have become essential to modern biological research. The increasing dependence of scientists on these powerful software tools creates a need for systematic assessment of these methods, known as benchmarking. Adopting a standardized benchmarking practice could help researchers who use omics data to better leverage recent technological innovations. Our review summarizes benchmarking practices from 25 recent studies and discusses the challenges, advantages, and limitations of benchmarking across various domains of biology. We also propose principles that can make computational biology benchmarking studies more sustainable and reproducible, ultimately increasing the transparency of biomedical data and results.
| S-EPMC6437167 | biostudies-literature
Project description:Structural variants (SVs) are one of the significant types of DNA mutations and are typically defined as larger-than-50-bp genomic alterations that include insertions, deletions, duplications, inversions, and translocations. These modifications can profoundly impact the phenotypic characteristics and contribute to disorders like cancer, response to treatment, and infections. Four long-read aligners and five SV callers have been evaluated using three Oxford Nanopore NGS human genome datasets in terms of precision, recall, and F1-score statistical metrics, depth of coverage, and speed of analysis. The best SV caller regarding recall, precision, and F1-score when matched with different aligners at different coverage levels tend to vary depending on the dataset and the specific SV types being analyzed. However, based on our findings, Sniffles and CuteSV tend to perform well across different aligners and coverage levels, followed by SVIM, PBSV, and SVDSS in the last place. The CuteSV caller has the highest average F1-score (82.51%) and recall (78.50%), and Sniffles has the highest average precision value (94.33%). Minimap2 as an aligner and Sniffles as an SV caller act as a strong base for the pipeline of SV calling because of their high speed and reasonable accomplishment. PBSV has a lower average F1-score, precision, and recall and may generate more false positives and overlook some actual SVs. Our results are valuable in the comprehensive evaluation of popular SV callers and aligners as they provide insight into the performance of several long-read aligners and SV callers and serve as a reference for researchers in selecting the most suitable tools for SV detection.
| S-EPMC10940726 | biostudies-literature
Project description:BackgroundNanopore long-read sequencing technology greatly expands the capacity of long-range, single-molecule DNA-modification detection. A growing number of analytical tools have been developed to detect DNA methylation from nanopore sequencing reads. Here, we assess the performance of different methylation-calling tools to provide a systematic evaluation to guide researchers performing human epigenome-wide studies.ResultsWe compare seven analytic tools for detecting DNA methylation from nanopore long-read sequencing data generated from human natural DNA at a whole-genome scale. We evaluate the per-read and per-site performance of CpG methylation prediction across different genomic contexts, CpG site coverage, and computational resources consumed by each tool. The seven tools exhibit different performances across the evaluation criteria. We show that the methylation prediction at regions with discordant DNA methylation patterns, intergenic regions, low CG density regions, and repetitive regions show room for improvement across all tools. Furthermore, we demonstrate that 5hmC levels at least partly contribute to the discrepancy between bisulfite and nanopore sequencing. Lastly, we provide an online DNA methylation database ( https://nanome.jax.org ) to display the DNA methylation levels detected by nanopore sequencing and bisulfite sequencing data across different genomic contexts.ConclusionsOur study is the first systematic benchmark of computational methods for detection of mammalian whole-genome DNA modifications in nanopore sequencing. We provide a broad foundation for cross-platform standardization and an evaluation of analytical tools designed for genome-scale modified base detection using nanopore sequencing.
| S-EPMC8524990 | biostudies-literature