BreakNet: detecting deletions using long reads and a deep learning approach

Long-read sequencing of extrachromosomal circular DNA and genome assembly of a Solanum lycopersicum breeding line revealed active LTR retrotransposons originating from S. Peruvianum L. introgressions

Transposable elements (TEs) are a major force in the evolution of plant genomes. Differences in the transposition activities and landscapes of TEs can vary substantially, even in closely related species. Interspecific hybridization, a widely employed technique in tomato breeding, results in the creation of novel combinations of TEs from distinct species. The implications of this process for TE transposition activity have not been studied in modern cultivars. In this study, we used nanopore sequencing of extrachromosomal circular DNA (eccDNA) and identified two highly active Ty1/Copia LTR retrotransposon families of tomato (Solanum lycopersicum), called Salsa and Ketchup. Elements of these families produce thousands of eccDNAs under controlled conditions and epigenetic stress. EccDNA sequence analysis revealed that the major parts of eccDNA produced by Ketchup and Salsa exhibited low similarity to the S. lycopersicum genomic sequence. To trace the origin of these TEs, whole-genome nanopore sequencing and de novo genome assembly were performed. We found that these TEs occurred in a tomato breeding line via interspecific introgression from S. peruvianum. Our findings collectively show that interspecific introgressions can contribute to both genetic and phenotypic diversity not only by introducing novel genetic variants, but also by importing active transposable elements from other species.

A comprehensive review of deep learning-based variant calling methods

Genome sequencing data have become increasingly important in the field of personalized medicine and diagnosis. However, accurately detecting genomic variations remains a challenging task. Traditional variation detection methods rely on manual inspection or predefined rules, which can be time-consuming and prone to errors. Consequently, deep learning-based approaches for variation detection have gained attention due to their ability to automatically learn genomic features that distinguish between variants. In our review, we discuss the recent advancements in deep learning-based algorithms for detecting small variations and structural variations in genomic data, as well as their advantages and limitations.

LSnet: detecting and genotyping deletions using deep learning network

The role and biological impact of structural variation (SV) are increasingly evident. Deletion accounts for 40% of SV and is an important type of SV. Therefore, it is of great significance to detect and genotype deletions. At present, high accurate long reads can be obtained as HiFi reads. And, through a combination of error-prone long reads and high accurate short reads, we can also get accurate long reads. These accurate long reads are helpful for detecting and genotyping SVs. However, due to the complexity of genome and alignment information, detecting and genotyping SVs remain a challenging task. Here, we propose LSnet, an approach for detecting and genotyping deletions with a deep learning network. Because of the ability of deep learning to learn complex features in labeled datasets, it is beneficial for detecting SV. First, LSnet divides the reference genome into continuous sub-regions. Based on the alignment between the sequencing data (the combination of error-prone long reads and short reads or HiFi reads) and the reference genome, LSnet extracts nine features for each sub-region, and these features are considered as signal of deletion. Second, LSnet uses a convolutional neural network and an attention mechanism to learn critical features in every sub-region. Next, in accordance with the relationship among the continuous sub-regions, LSnet uses a gated recurrent units (GRU) network to further extract more important deletion signatures. And a heuristic algorithm is present to determine the location and length of deletions. Experimental results show that LSnet outperforms other methods in terms of the F1 score. The source code is available from GitHub at https://github.com/eioyuou/LSnet.

cnnLSV: detecting structural variants by encoding long-read alignment information and convolutional neural network

BACKGROUND

Genomic structural variant detection is a significant and challenging issue in genome analysis. The existing long-read based structural variant detection methods still have space for improvement in detecting multi-type structural variants.

RESULTS

In this paper, we propose a method called cnnLSV to obtain detection results with higher quality by eliminating false positives in the detection results merged from the callsets of existing methods. We design an encoding strategy for four types of structural variants to represent long-read alignment information around structural variants into images, input the images into a constructed convolutional neural network to train a filter model, and load the trained model to remove the false positives to improve the detection performance. We also eliminate mislabeled training samples in the training model phase by using principal component analysis algorithm and unsupervised clustering algorithm k-means. Experimental results on both simulated and real datasets show that our proposed method outperforms existing methods overall in detecting insertions, deletions, inversions, and duplications. The program of cnnLSV is available at https://github.com/mhuidong/cnnLSV .

CONCLUSIONS

The proposed cnnLSV can detect structural variants by using long-read alignment information and convolutional neural network to achieve overall higher performance, and effectively eliminate incorrectly labeled samples by using the principal component analysis and k-means algorithms in training model stage.

Evaluation of the Available Variant Calling Tools for Oxford Nanopore Sequencing in Breast Cancer

The goal of biomarker testing, in the field of personalized medicine, is to guide treatments to achieve the best possible results for each patient. The accurate and reliable identification of everyone’s genome variants is essential for the success of clinical genomics, employing third-generation sequencing. Different variant calling techniques have been used and recommended by both Oxford Nanopore Technologies (ONT) and Nanopore communities. A thorough examination of the variant callers might give critical guidance for third-generation sequencing-based clinical genomics. In this study, two reference genome sample datasets (NA12878) and (NA24385) and the set of high-confidence variant calls provided by the Genome in a Bottle (GIAB) were used to allow the evaluation of the performance of six variant calling tools, including Human-SNP-wf, Clair3, Clair, NanoCaller, Longshot, and Medaka, as an integral step in the in-house variant detection workflow. Out of the six variant callers understudy, Clair3 and Human-SNP-wf that has Clair3 incorporated into it achieved the highest performance rates in comparison to the other variant callers. Evaluation of the results for the tool was expressed in terms of Precision, Recall, and F1-score using Hap.py tools for the comparison. In conclusion, our findings give important insights for identifying accurate variants from third-generation sequencing of personal genomes using different variant detection tools available for long-read sequencing.