FreeHi-C simulates high-fidelity Hi-C data for benchmarking and data augmentation.
Ontology highlight
ABSTRACT:
SUBMITTER: Zheng Y
PROVIDER: S-EPMC8136837 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Single-cell Hi-C technology provides an unprecedented opportunity to reveal chromatin structure in individual cells. However, high sequencing cost impedes the generation of biological Hi-C data with high sequencing depths and multiple replicates for downstream analysis. Here, we developed a single-cell Hi-C simulator (scHi-CSim) that generates high-fidelity data for benchmarking. scHi-CSim merges neighboring cells to overcome the sparseness of data, samples interactions in distance-stratified chromosomes to maintain the heterogeneity of single cells, and estimates the empirical distribution of restriction fragments to generate simulated data. We demonstrated that scHi-CSim can generate high-fidelity data by comparing the performance of single-cell clustering and detection of chromosomal high-order structures with raw data. Furthermore, scHi-CSim is flexible to change sequencing depth and the number of simulated replicates. We showed that increasing sequencing depth could improve the accuracy of detecting topologically associating domains. We also used scHi-CSim to generate a series of simulated datasets with different sequencing depths to benchmark scHi-C clustering methods.
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Project description:In high-throughput sequencing data, performance comparisons between computational tools are essential for making informed decisions at each step of a project. Simulations are a critical part of method comparisons, but for standard Illumina sequencing of genomic DNA, they are often oversimplified, which leads to optimistic results for most tools. ReSeq improves the authenticity of synthetic data by extracting and reproducing key components from real data. Major advancements are the inclusion of systematic errors, a fragment-based coverage model and sampling-matrix estimates based on two-dimensional margins. These improvements lead to more faithful performance evaluations. ReSeq is available at https://github.com/schmeing/ReSeq .
| S-EPMC7896392 | biostudies-literature
Project description:BackgroundRecent advances in long-read sequencing technologies have enabled accurate identification of all genetic variants in individuals or cells; this procedure is known as variant calling. However, benchmarking studies on variant calling using different long-read sequencing technologies are still lacking.ResultsWe used two Caenorhabditis elegans strains to measure several variant calling metrics. These two strains shared true-positive genetic variants that were introduced during strain generation. In addition, both strains contained common and distinguishable variants induced by DNA damage, possibly leading to false-positive estimation. We obtained accurate and noisy long reads from both strains using high-fidelity (HiFi) and continuous long-read (CLR) sequencing platforms, and compared the variant calling performance of the two platforms. HiFi identified a 1.65-fold higher number of true-positive variants on average, with 60% fewer false-positive variants, than CLR did. We also compared read-based and assembly-based variant calling methods in combination with subsampling of various sequencing depths and demonstrated that variant calling after genome assembly was particularly effective for detection of large insertions, even with 10 × sequencing depth of accurate long-read sequencing data.ConclusionsBy directly comparing the two long-read sequencing technologies, we demonstrated that variant calling after genome assembly with 10 × or more depth of accurate long-read sequencing data allowed reliable detection of true-positive variants. Considering the high cost of HiFi sequencing, we herein propose appropriate methodologies for performing cost-effective and high-quality variant calling: 10 × assembly-based variant calling. The results of the present study may facilitate the development of methods for identifying all genetic variants at the population level.
| S-EPMC10045170 | biostudies-literature
Project description:BACKGROUND:Chromatin conformation capture techniques have evolved rapidly over the last few years and have provided new insights into genome organization at an unprecedented resolution. Analysis of Hi-C data is complex and computationally intensive involving multiple tasks and requiring robust quality assessment. This has led to the development of several tools and methods for processing Hi-C data. However, most of the existing tools do not cover all aspects of the analysis and only offer few quality assessment options. Additionally, availability of a multitude of tools makes scientists wonder how these tools and associated parameters can be optimally used, and how potential discrepancies can be interpreted and resolved. Most importantly, investigators need to be ensured that slight changes in parameters and/or methods do not affect the conclusions of their studies. RESULTS:To address these issues (compare, explore and reproduce), we introduce HiC-bench, a configurable computational platform for comprehensive and reproducible analysis of Hi-C sequencing data. HiC-bench performs all common Hi-C analysis tasks, such as alignment, filtering, contact matrix generation and normalization, identification of topological domains, scoring and annotation of specific interactions using both published tools and our own. We have also embedded various tasks that perform quality assessment and visualization. HiC-bench is implemented as a data flow platform with an emphasis on analysis reproducibility. Additionally, the user can readily perform parameter exploration and comparison of different tools in a combinatorial manner that takes into account all desired parameter settings in each pipeline task. This unique feature facilitates the design and execution of complex benchmark studies that may involve combinations of multiple tool/parameter choices in each step of the analysis. To demonstrate the usefulness of our platform, we performed a comprehensive benchmark of existing and new TAD callers exploring different matrix correction methods, parameter settings and sequencing depths. Users can extend our pipeline by adding more tools as they become available. CONCLUSIONS:HiC-bench consists an easy-to-use and extensible platform for comprehensive analysis of Hi-C datasets. We expect that it will facilitate current analyses and help scientists formulate and test new hypotheses in the field of three-dimensional genome organization.
| S-EPMC5217551 | biostudies-literature
Project description:The Asian swamp eel (Monopterus albus) is an excellent model species for studying sex change and chromosome evolution. M. albus is also widely reared in East Asia and South-East Asia because of its great nutritional value. The low fecundity of this species (about 300 eggs per fish) greatly hinders fries production and breeding programs. Interestingly, about 3-5% of the eels could remain as females for 3 years and lay more than 3,000 eggs per fish, which are referred to as non-sex-reversal (NSR) females. Here, we presented a new chromosome-level genome assembly of such NSR females using Illumina, HiFi, and Hi-C sequencing technologies. The new assembly (Mal.V2_NSR) is 838.39 Mb in length, and the N50 of the contigs is 49.8 Mb. Compared with the previous assembly obtained using the continuous long-read sequencing technology (Mal.V1_CLR), we found a remarkable increase of continuity in the new assembly Mal.V2_NSR with a 20-times longer contig N50. Chromosomes 2 and 12 were assembled into a single contig, respectively. Meanwhile, two highly contiguous haplotype assemblies were also obtained, with contig N50 being 14.54 and 12.13 Mb, respectively. BUSCO and Merqury analyses indicate completeness and high accuracy of these three assemblies. A comparative genomic analysis revealed substantial structural variations (SVs) between Mal.V2_NSR and Mal.V1_CLR and two phased haplotype assemblies, as well as whole chromosome fusion events when compared with the zig-zag eel. Additionally, our newly obtained assembly provides a genomic view of sex-related genes and a complete landscape of the MHC genes. Therefore, these high-quality genome assemblies would provide great help for future breeding works of the swamp eel, and it is a valuable new reference for genetic and genomic studies of this species.
| S-EPMC9165713 | biostudies-literature
Project description:Core microRNA (miRNA) sequences exist as populations of variants called isomiRs made up of different lengths and nucleotide compositions. In particular, the short sequences of miRNA make single-base isomiR mismatches very difficult to be discriminated. Non-specific hybridizations often arise when DNA probe-miRNA target hybridization is the primary, or initial, mode of detection. These errors then become exacerbated through subsequent amplification steps. Here, we present the design of DNA probes modified with poly-guanine (PG) tracts that were induced to form G-quadruplexes (G4) for hi-fidelity discrimination of miRNA core target sequence from single-base mismatched isomiRs. We demonstrate that, when compared to unmodified probes, this G4 'gate-keeping' function within the G4-modified probes enables more stringent hybridization of complementary core miRNA target transcripts while limiting non-specific hybridizations. This increased discriminatory power of the G4-modified probes over unmodified probes is maintained even after further reverse transcriptase extension of probe-target hybrids. Enzymatic extension also enhanced the clarity and sensitivity of readouts and allows different isomiRs to be distinguished from one another via the relative positions of the mismatches.
| S-EPMC5690596 | biostudies-literature
Project description:Recent experiments have provided Hi-C data at resolution as high as 1 kbp. However, 3D structural inference from high-resolution Hi-C datasets is often computationally unfeasible using existing methods.We have developed miniMDS, an approximation of multidimensional scaling (MDS) that partitions a Hi-C dataset, performs high-resolution MDS separately on each partition, and then reassembles the partitions using low-resolution MDS. miniMDS is faster, more accurate, and uses less memory than existing methods for inferring the human genome at high resolution (10 kbp).A Python implementation of miniMDS is available on GitHub: https://github.com/seqcode/miniMDS .mahony@psu.edu.Supplementary data are available at Bioinformatics online.
| S-EPMC5870652 | biostudies-literature
Project description:Compared with second-generation sequencing technologies, third-generation single-molecule RNA sequencing has unprecedented advantages; the long reads it generates facilitate isoform-level transcript characterization. In particular, the Oxford Nanopore Technology sequencing platforms have become more popular in recent years owing to their relatively high affordability and portability compared with other third-generation sequencing technologies. To aid the development of analytical tools that leverage the power of this technology, simulated data provide a cost-effective solution with ground truth. However, a nanopore sequence simulator targeting transcriptomic data is not available yet. We introduce Trans-NanoSim, a tool that simulates reads with technical and transcriptome-specific features learnt from nanopore RNA-sequncing data. We comprehensively benchmarked Trans-NanoSim on direct RNA and complementary DNA datasets describing human and mouse transcriptomes. Through comparison against other nanopore read simulators, we show the unique advantage and robustness of Trans-NanoSim in capturing the characteristics of nanopore complementary DNA and direct RNA reads. As a cost-effective alternative to sequencing real transcriptomes, Trans-NanoSim will facilitate the rapid development of analytical tools for nanopore RNA-sequencing data. Trans-NanoSim and its pre-trained models are freely accessible at https://github.com/bcgsc/NanoSim.
| S-EPMC7285873 | biostudies-literature
Project description:BACKGROUND:Hi-C is derived from chromosome conformation capture (3C) and targets chromatin contacts on a genomic scale. This method has also been used frequently in scaffolding nucleotide sequences obtained by de novo genome sequencing and assembly, in which the number of resultant sequences rarely converges to the chromosome number. Despite its prevalent use, the sample preparation methods for Hi-C have not been intensively discussed, especially from the standpoint of genome scaffolding. RESULTS:To gain insight into the best practice of Hi-C scaffolding, we performed a multifaceted methodological comparison using vertebrate samples and optimized various factors during sample preparation, sequencing, and computation. As a result, we identified several key factors that helped improve Hi-C scaffolding, including the choice and preparation of tissues, library preparation conditions, the choice of restriction enzyme(s), and the choice of scaffolding program and its usage. CONCLUSIONS:This study provides the first comparison of multiple sample preparation kits/protocols and computational programs for Hi-C scaffolding by an academic third party. We introduce a customized protocol designated "inexpensive and controllable Hi-C (iconHi-C) protocol," which incorporates the optimal conditions identified in this study, and demonstrate this technique on chromosome-scale genome sequences of the Chinese softshell turtle Pelodiscus sinensis.
| S-EPMC6952475 | biostudies-literature
Project description:Deep learning is widely used in modern orchard production for various inspection missions, which helps improve the efficiency of orchard operations. In the mission of visual detection during fruit picking, most current lightweight detection models are not yet effective enough to detect multi-type occlusion targets, severely affecting automated fruit-picking efficiency. This study addresses this problem by proposing the pioneering design of a multi-type occlusion apple dataset and an augmentation method of data balance. We divided apple occlusion into eight types and used the proposed method to balance the number of annotation boxes for multi-type occlusion apple targets. Finally, a validation experiment was carried out using five popular lightweight object detection models: yolox-s, yolov5-s, yolov4-s, yolov3-tiny, and efficidentdet-d0. The results show that, using the proposed augmentation method, the average detection precision of the five popular lightweight object detection models improved significantly. Specifically, the precision increased from 0.894 to 0.974, recall increased from 0.845 to 0.972, and mAP0.5 increased from 0.982 to 0.919 for yolox-s. This implies that the proposed augmentation method shows great potential for different fruit detection missions in future orchard applications.
| S-EPMC9460715 | biostudies-literature