AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Highly accurate sequencing approaches have substantially improved the detection of low-frequency variants by reducing technical artifacts and enhancing signal-to-noise ratios.
This work compared the performance of long-read sequencing against short-read sequencing and microarrays, identified two novel non-synonymous variants, including a unique InDel detected exclusively by long-read sequencing, and demonstrated superior detection power, particularly for InDels.
The results indicate that pangenome-based workflows aid improved detection of large variants from targeted sequencing data in the clinical context and suggest that they may contribute to more unified variant detection frameworks for all-size genetic variants in the future.
Progress has been made in identifying G-quadruplexes (G4s) and other non-canonical (non-B) DNA structures in live cells. However, these experiments have been limited by methodological constraints, including low resolution and specificity, and GC-sequencing bias inherent to the short-read sequencing technologies. Direct, single-molecule, and long-read technologies have the potential to address these shortcomings. Here, we investigated the use of long-read DNA sequencing with Oxford Nanopore Technologies (ONT) to detect G4 and other non-B DNA structures. We applied ONT sequencing to oligos known to form G4s in vitro, as well as to DNA from live cells. Both experiments were probed with potassium permanganate, a chemical that preferentially oxidizes single-stranded DNA (ssDNA). We determined that G4 structures can be resolved by ONT sequencing, and a G4 motif’s sequencing error profile depends on the original structural state of the G4 (as determined in vitro). We next applied machine learning algorithms (logistic regression, random forest, XGBoost, and 1D convolutional neural networks) to the raw ONT sequencing current data. Using these approaches, we could determine whether a sequencing read originated from a G4 motif in the G4 vs. B-form conformation with ∼90% accuracy. Further, we demonstrated that it is possible to sequence permanganate-modified DNA directly using ONT, with minimal effects on the read length, yield, and alignment accuracy. We conclude that at high ONT sequencing depth, this approach can identify B-folded vs. single-stranded DNA regions in cells, with ssDNA frequently present across several non-B DNA structures.
A single genomic assay that delivers complete information across variant classes remains an aspirational goal. Currently, researchers and clinicians rely on an inefficient, expensive combination of short-read sequencing for single-nucleotide variants (SNVs) and small indels, comparative genomic hybridization (CGH) arrays for copy number variants (CNVs), and optical mapping and long-read sequencing for complex rearrangements, limiting the full potential of genomic discovery. To address these issues, TruPath Genome provides a one-test-for-all solution. By combining PCR-free whole-genome sequencing (WGS) with proximity-mapped read technology, it achieves high-resolution detection of SNVs and indels alongside long-range phasing for CNVs and structural variant (SV) refinement. We applied TruPath Genome on six clinical samples that were previously resolved by conventional methods. Across the cohort, TruPath Genome delivered coverage and variant-calling performance comparable to conventional WGS while achieving superior long-range phasing and enabling precise breakpoint resolution for clinically relevant structural events. This highlights TruPath Genome’s potential to consolidate genomic testing pipelines, accelerate diagnosis, and expand access to advanced genomic insights. Furthermore, its ultra-long-range data facilitates telomere-to-telomere assemblies and pangenome development, advancing our understanding of genome biology at an unprecedented scale.
This review compares SNP detection programs such as GATK, BCFtools, FreeBayes, SAMtools, SAMtools, and DeepVariant and their algorithmic structures, namely pileup- based, haplotype-based, and machine-learning approaches and suggests that no single tool is the best.
Information about segregating haplotypes and structural variation (SV) can be extremely rich for a variety of applications in population genomics but remains largely inaccessible for many non-model species. Of the available methods, linked-read sequencing is especially promising for its low cost and scalability, but its adoption remains limited. One existing linked-read method is Haplotagging, which barcodes sequencing reads to reconstruct long molecules that encode haplotype information, with the potential to generate phased whole-genome data and detect structural variants. In this study, we present BLink-seq, a novel Haplotagging method that is compatible with standard short-read next-generation sequencing platforms, is locally reproducible with low-cost reagents, and is scalable for high-throughput sample processing. We optimized library preparation parameters, explored their relationship to linked-read library metrics, and validated phasing performance and structural variant detection in two evolutionary extremes: an experimental Drosophila melanogaster cross of inbred lines carrying known inversions, and four Atlantic silverside (Menidia menidia) parent-offspring trios sourced from highly outbred, wild-caught populations. We then applied our protocol to a cohort of 376 silversides to demonstrate its scalability and potential for SV detection and genotype imputation. Using BLink-seq, we generated chromosome-scale phased blocks and identified known inversions in both validation datasets. We discovered previously uncharacterized structural complexity within a known adaptive inversion on silverside chromosome 11, demonstrating that linked-read data can refine our understanding of SV architecture beyond what short reads alone can resolve. Finally, we provide a user guide for researchers interested in using BLink-seq.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.