Introduction: Structural Variants (SVs) are a type of variation that can significantly influence phenotypes and cause diseases. Thus, the accurate detection of SVs is a vital part of modern genetic analysis. The advent of long-read sequencing technology ushers in a new era of more accurate and comprehensive SV calling, and many tools have been developed to call SVs using long-read data. Haplotype-tagging is a procedure that can tag haplotype information on reads and can thus potentially improve the SV detection; nevertheless, few methods make use of this information. In this article, we introduce HapKled, a new SV detection tool that can accurately detect SVs from Oxford Nanopore Technologies (ONT) long-read alignment data. Methods: HapKled utilizes haplotype information underlying alignment data by conducting haplotype-tagging using Whatshap on the reads to improve the detection performance, with three unique calling mechanics including altering clustering conditions according to haplotype information of signatures, determination of similar SVs based on haplotype information, and slack filtering conditions based on haplotype quality. Results: In our evaluations, HapKled outperformed state-of-the-art tools and can deliver better SV detection results on both simulated and real sequencing data. The code and experiments of HapKled can be obtained from https://github.com/CoREse/HapKled. Discussion: With the superb SV detection performance that HapKled can deliver, HapKled could be useful in bioinformatics research, clinical diagnosis, and medical research and development.

In cancer genomics, variant calling has advanced, but traditional mean accuracy evaluations are inadequate for biomarkers like tumor mutation burden, which vary significantly across samples, affecting immunotherapy patient selection and threshold settings. In this study, we introduce TMBstable, an innovative method that dynamically selects optimal variant calling strategies for specific genomic regions using a meta-learning framework, distinguishing it from traditional callers with uniform sample-wide strategies. The process begins with segmenting the sample into windows and extracting meta-features for clustering, followed by using a pre-trained meta-model to select suitable algorithms for each cluster, thereby addressing strategy-sample mismatches, reducing performance fluctuations and ensuring consistent performance across various samples. We evaluated TMBstable using both simulated and real non-small cell lung cancer and nasopharyngeal carcinoma samples, comparing it with advanced callers. The assessment, focusing on stability measures, such as the variance and coefficient of variation in false positive rate, false negative rate, precision and recall, involved 300 simulated and 106 real tumor samples. Benchmark results showed TMBstable\'s superior stability with the lowest variance and coefficient of variation across performance metrics, highlighting its effectiveness in analyzing the counting-based biomarker. The TMBstable algorithm can be accessed at https://github.com/hello-json/TMBstable for academic usage only.

Structural Variants (SVs) are a crucial type of genetic variant that can significantly impact phenotypes. Therefore, the identification of SVs is an essential part of modern genomic analysis. In this article, we present kled, an ultra-fast and sensitive SV caller for long-read sequencing data given the specially designed approach with a novel signature-merging algorithm, custom refinement strategies and a high-performance program structure. The evaluation results demonstrate that kled can achieve optimal SV calling compared to several state-of-the-art methods on simulated and real long-read data for different platforms and sequencing depths. Furthermore, kled excels at rapid SV calling and can efficiently utilize multiple Central Processing Unit (CPU) cores while maintaining low memory usage. The source code for kled can be obtained from https://github.com/CoREse/kled.

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.

The precisionFDA Truth Challenge V2 aimed to assess the state of the art of variant calling in challenging genomic regions. Starting with FASTQs, 20 challenge participants applied their variant-calling pipelines and submitted 64 variant call sets for one or more sequencing technologies (Illumina, PacBio HiFi, and Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with updated Genome in a Bottle benchmark sets and genome stratifications. Challenge submissions included numerous innovative methods, with graph-based and machine learning methods scoring best for short-read and long-read datasets, respectively. With machine learning approaches, combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.

This study discusses the genetic mutations that have a significant association with economically important traits that would benefit tea breeders. The purpose of this study was to analyze the leaf quality and SNPs in quality-related genes in the tea plant collection of 20 mutant genotypes growing without nitrogen fertilizers. Leaf N-content, catechins, L-theanine, and caffeine contents were analyzed in dry leaves via HPLC. Additionally, the photochemical yield, electron transport efficiency, and non-photochemical quenching were analyzed using PAM-fluorimetry. The next generation pooled amplicon-sequencing approach was used for SNPs-calling in 30 key genes related to N metabolism and leaf quality. The leaf N content varied significantly among genotypes (p ≤ 0.05) from 2.3 to 3.7% of dry mass. The caffeine content varied from 0.7 to 11.7 mg g-1, and the L-theanine content varied from 0.2 to 5.8 mg g-1 dry leaf mass. Significant positive correlations were detected between the nitrogen content and biochemical parameters such as theanine, caffeine, and most of the catechins. However, significant negative correlations were observed between the photosynthetic parameters (Y, ETR, Fv/Fm) and several biochemical compounds, including rutin, Quercetin-3-O-glucoside, Kaempferol-3-O-rutinoside, Kaempferol-3-O-glucoside, Theaflavin-3\'-gallate, gallic acid. From our SNP-analysis, three SNPs in WRKY57 were detected in all genotypes with a low N content. Moreover, 29 SNPs with a high or moderate effect were specific for #316 (high N-content, high quality) or #507 (low N-content, low quality). The use of a linear regression model revealed 16 significant associations; theaflavin, L-theanine, and ECG were associated with several SNPs of the following genes: ANSa, DFRa, GDH2, 4CL, AlaAT1, MYB4, LHT1, F3\'5\'Hb, UFGTa. Among them, seven SNPs of moderate effect led to changes in the amino acid contents in the final proteins of the following genes: ANSa, GDH2, 4Cl, F3\'5\'Hb, UFGTa. These results will be useful for further evaluations of the important SNPs and will help to provide a better understanding of the mechanisms of nitrogen uptake efficiency in tree crops.

BACKGROUND: With the continuous advances in third-generation sequencing technology and the increasing affordability of next-generation sequencing technology, sequencing data from different sequencing technology platforms is becoming more common. While numerous benchmarking studies have been conducted to compare variant-calling performance across different platforms and approaches, little attention has been paid to the potential of leveraging the strengths of different platforms to optimize overall performance, especially integrating Oxford Nanopore and Illumina sequencing data.
RESULTS: We investigated the impact of multi-platform data on the performance of variant calling through carefully designed experiments with a deep learning-based variant caller named Clair3-MP (Multi-Platform). Through our research, we not only demonstrated the capability of ONT-Illumina data for improved variant calling, but also identified the optimal scenarios for utilizing ONT-Illumina data. In addition, we revealed that the improvement in variant calling using ONT-Illumina data comes from an improvement in difficult genomic regions, such as the large low-complexity regions and segmental and collapse duplication regions. Moreover, Clair3-MP can incorporate reference genome stratification information to achieve a small but measurable improvement in variant calling. Clair3-MP is accessible as an open-source project at: https://github.com/HKU-BAL/Clair3-MP .
CONCLUSIONS: These insights have important implications for researchers and practitioners alike, providing valuable guidance for improving the reliability and efficiency of genomic analysis in diverse applications.

Delins, as known as complex indel, is a combined genomic structural variation formed by deleting and inserting DNA fragments at a common genomic location. Recent studies emphasized the importance of delins in cancer diagnosis and treatment. Although the long reads from PacBio CLR sequencing significantly facilitate delins calling, the existing approaches still encounter computational challenges from the high level of sequencing errors, and often introduce errors in genotyping and phasing delins. In this paper, we propose an efficient algorithmic pipeline, named delInsCaller, to identify delins on haplotype resolution from the PacBio CLR sequencing data. delInsCaller design a fault-tolerant method by calculating a variation density score, which helps to locate the candidate mutational regions under a high-level of sequencing errors. It adopts a base association-based contig splicing method, which facilitates contig splicing in the presence of false-positive interference. We conducted a series of experiments on simulated datasets, and the results showed that delInsCaller outperformed several state-of-the-art approaches, e.g., SVseq3, across a wide range of parameter settings, such as read depth, sequencing error rates, etc. delInsCaller often obtained higher f-measures than other approaches; specifically, it was able to maintain advantages at ~15% sequencing errors. delInsCaller was able to significantly improve the N50 values with almost no loss of haplotype accuracy compared with the existing approach as well.

Accurate identification of genetic variants from family child-mother-father trio sequencing data is important in genomics. However, state-of-the-art approaches treat variant calling from trios as three independent tasks, which limits their calling accuracy for Nanopore long-read sequencing data. For better trio variant calling, we introduce Clair3-Trio, the first variant caller tailored for family trio data from Nanopore long-reads. Clair3-Trio employs a Trio-to-Trio deep neural network model, which allows it to input the trio sequencing information and output all of the trio\'s predicted variants within a single model to improve variant calling. We also present MCVLoss, a novel loss function tailor-made for variant calling in trios, leveraging the explicit encoding of the Mendelian inheritance. Clair3-Trio showed comprehensive improvement in experiments. It predicted far fewer Mendelian inheritance violation variations than current state-of-the-art methods. We also demonstrated that our Trio-to-Trio model is more accurate than competing architectures. Clair3-Trio is accessible as a free, open-source project at https://github.com/HKU-BAL/Clair3-Trio.

The landraces preserved by indigenous worldwide exhibited larger variation in the phenotypes and adaption to different environments, which suggests that they comprise rich resources and can be served as a gene pool for rice improvement. Despite extensive studies on cultivated rice, the variations and relationships between landraces and modern cultivated rice remain unclear. In this study, a total of 20 varieties that include 10 Oryza javanica collected from different countries worldwide and 10 Oryza indica from China were genotyped and yielded a sum of 99.9-Gb resequencing raw data. With the genomic sequence of the japonica cultivar Nipponbare as a reference, the following genetic features of single-nucleotide polymorphism (SNP) ranged from 861,177 to 1,044,617, insertion-deletion polymorphisms (InDels) ranged from 164,018 to 211,135, and structural variation (SV) ranged from 3,313 to 4,959 were identified in Oryza javanica. Variation between the two subspecies was also determined that 584,104 SNPs, 75,351 InDels, 104,606 SNPs, and 19,872 InDels specific to Oryza indica and Oryza javanica, respectively. Furthermore, Gene Ontology (GO) and KEGG of Oryza javanica-specific SNP-related genes revealed that they participated in DNA metabolic process, DNA replication, and DNA integration. The sequence variation and candidate grain shape-related gene TGW2 were identified through Fst and sweep selective analysis. Hap4 of TGW2 is performed better than others. The whole genome sequence data and genetic variation information illustrated in this study will serve as an important gene pool for molecular breeding and facilitate genetic analysis of Oryza javanica varieties.