Benchmarking long-read aligners and SV callers for structural variation detection in Oxford nanopore sequencing data.

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.

Metagenomic nanopore sequencing versus conventional diagnosis for identification of the dieback pathogens of mango trees.

Dieback is one of the most dangerous fungal diseases affecting mango trees. In this study, nanopore metagenome sequencing of the root-soil samples and infected plant tissues was conducted to identify the fungal pathogens present. Soil analysis of the infected mango trees showed the abundance of the Dikarya subkingdom (59%) including Lasiodiplodia theobromae (15%), Alternaria alternata (6%), Ceratocystis huliohia and Colletotrichum gloeosporioides. Analysis of the infected plant tissues revealed the presence of A. alternata (34%). The data were deposited in the National Center of Biotechnology Information (PRJNA767267). In conclusion, nanopore metagenome sequencing analysis was a valuable tool to rapidly identify dieback-associated fungal pathogens.

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.