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1. Sequencing Illumina libraries at high accuracy on the ONT MinION using R2C2

   https://doi.org/10.1101/gr.277031.122  

   Zee, Alexander; Deng, Dori Z.Q.; Adams, Matthew; Schimke, Kayla D.; Corbett-Detig, Russell; Russell, Shelbi L.; Zhang, Xuan; Schmitz, Robert J.; Vollmers, Christopher (November 2022, Genome Research)

   High-throughput short-read sequencing has taken on a central role in research and diagnostics. Hundreds of different assays take advantage of Illumina short-read sequencers, the predominant short-read sequencing technology available today. Although other short-read sequencing technologies exist, the ubiquity of Illumina sequencers in sequencing core facilities and the high capital costs of these technologies have limited their adoption. Among a new generation of sequencing technologies, Oxford Nanopore Technologies (ONT) holds a unique position because the ONT MinION, an error-prone long-read sequencer, is associated with little to no capital cost. Here we show that we can make short-read Illumina libraries compatible with the ONT MinION by using the rolling circle to concatemeric consensus (R2C2) method to circularize and amplify the short library molecules. This results in longer DNA molecules containing tandem repeats of the original short library molecules. This longer DNA is ideally suited for the ONT MinION, and after sequencing, the tandem repeats in the resulting raw reads can be converted into high-accuracy consensus reads with similar error rates to that of the Illumina MiSeq. We highlight this capability by producing and benchmarking RNA-seq, ChIP-seq, and regular and target-enriched Tn5 libraries. We also explore the use of this approach for rapid evaluation of sequencing library metrics by implementing a real-time analysis workflow. 

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2. QAlign: aligning nanopore reads accurately using current-level modeling

   Dhaivat Joshi, Shunfu Mao (March 2021, Bioinformatics)

   null (Ed.)

   Efficient and accurate alignment of DNA/RNA sequence reads to each other or to a reference genome/transcriptome is an important problem in genomic analysis. Nanopore sequencing has emerged as a major sequencing technology and many long-read aligners have been designed for aligning nanopore reads. However, the high error rate makes accurate and efficient alignment difficult. Utilizing the noise and error characteristics inherent in the sequencing process properly can play a vital role in constructing a robust aligner. In this article, we design QAlign, a pre-processor that can be used with any long-read aligner for aligning long reads to a genome/transcriptome or to other long reads. The key idea in QAlign is to convert the nucleotide reads into discretized current levels that capture the error modes of the nanopore sequencer before running it through a sequence aligner.We show that QAlign is able to improve alignment rates from around 80\% up to 90\% with nanopore reads when aligning to the genome. We also show that QAlign improves the average overlap quality by 9.2, 2.5 and 10.8\% in three real datasets for read-to-read alignment. Read-to-transcriptome alignment rates are improved from 51.6\% to 75.4\% and 82.6\% to 90\% in two real datasets.https://github.com/joshidhaivat/QAlign.git.Supplementary data are available at Bioinformatics online. 

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3. Nanopores map the acid-base properties of a single site in a single DNA molecule

   https://doi.org/10.1093/nar/gkae518  

   Smith, Drew C.; Thomas, Christopher A.; Craig, Jonathan M.; Brinkerhoff, Henry; Abell, Sarah J.; Franzi, Michaela C.; Carrasco, Jessica D.; Hoshika, Shuichi; Benner, Steven A.; Gundlach, Jens H.; et al (June 2024, Nucleic Acids Research)

   Abstract Nanopores are increasingly powerful tools for single molecule sensing, in particular, for sequencing DNA, RNA and peptides. This success has spurred efforts to sequence non-canonical nucleic acid bases and amino acids. While canonical DNA and RNA bases have pKas far from neutral, certain non-canonical bases, natural RNA modifications, and amino acids are known to have pKas near neutral pHs at which nanopore sequencing is typically performed. Previous reports have suggested that the nanopore signal may be sensitive to the protonation state of an individual moiety. We sequenced ion currents with the MspA nanopore using a single stranded DNA containing a single non-canonical DNA base (Z) at various pH conditions. The Z-base has a near-neutral pKa ∼ 7.8. We find that the measured ion current is remarkably sensitive to the protonation state of the Z-base. We demonstrate how nanopores can be used to localize and determine the pKa of individual moieties along a polymer. More broadly, these experiments provide a path to mapping different protonation sites along polymers and give insight in how to optimize sequencing of polymers that contain moieties with near-neutral pKas. 

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4. Transformer‐based DNA methylation detection on ionic signals from Oxford Nanopore sequencing data

   https://doi.org/10.15302/J-QB-022-0323  

   Wang, Xiuquan; Ahsan, Mian Umair; Zhou, Yunyun; Wang, Kai (September 2023, Quantitative Biology)

   Transformer is an algorithm that adopts self‐attention architecture in the neural networks and has been widely used in natural language processing. In the current study, we apply Transformer architecture to detect DNA methylation on ionic signals from Oxford Nanopore sequencing data. We evaluated this idea using real data sets (Escherichia colidata and the human genome NA12878 sequenced by Simpsonet al.) and demonstrated the ability of Transformers to detect methylation on ionic signal data. BackgroundOxford Nanopore long‐read sequencing technology addresses current limitations for DNA methylation detection that are inherent in short‐read bisulfite sequencing or methylation microarrays. A number of analytical tools, such as Nanopolish, Guppy/Tombo and DeepMod, have been developed to detect DNA methylation on Nanopore data. However, additional improvements can be made in computational efficiency, prediction accuracy, and contextual interpretation on complex genomics regions (such as repetitive regions, low GC density regions). MethodIn the current study, we apply Transformer architecture to detect DNA methylation on ionic signals from Oxford Nanopore sequencing data. Transformer is an algorithm that adopts self‐attention architecture in the neural networks and has been widely used in natural language processing. ResultsCompared to traditional deep‐learning method such as convolutional neural network (CNN) and recurrent neural network (RNN), Transformer may have specific advantages in DNA methylation detection, because the self‐attention mechanism can assist the relationship detection between bases that are far from each other and pay more attention to important bases that carry characteristic methylation‐specific signals within a specific sequence context. ConclusionWe demonstrated the ability of Transformers to detect methylation on ionic signal data. 

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5. HQAlign: aligning nanopore reads for SV detection using current-level modeling

   https://doi.org/10.1093/bioinformatics/btad580  

   Joshi, Dhaivat; Diggavi, Suhas; Chaisson, Mark J; Kannan, Sreeram (October 2023, Bioinformatics)

   Alkan, Can (Ed.)

   Abstract MotivationDetection of structural variants (SVs) from the alignment of sample DNA reads to the reference genome is an important problem in understanding human diseases. Long reads that can span repeat regions, along with an accurate alignment of these long reads play an important role in identifying novel SVs. Long-read sequencers, such as nanopore sequencing, can address this problem by providing very long reads but with high error rates, making accurate alignment challenging. Many errors induced by nanopore sequencing have a bias because of the physics of the sequencing process and proper utilization of these error characteristics can play an important role in designing a robust aligner for SV detection problems. In this article, we design and evaluate HQAlign, an aligner for SV detection using nanopore sequenced reads. The key ideas of HQAlign include (i) using base-called nanopore reads along with the nanopore physics to improve alignments for SVs, (ii) incorporating SV-specific changes to the alignment pipeline, and (iii) adapting these into existing state-of-the-art long-read aligner pipeline, minimap2 (v2.24), for efficient alignments. ResultsWe show that HQAlign captures about 4%–6% complementary SVs across different datasets, which are missed by minimap2 alignments while having a standalone performance at par with minimap2 for real nanopore reads data. For the common SV calls between HQAlign and minimap2, HQAlign improves the start and the end breakpoint accuracy by about 10%–50% for SVs across different datasets. Moreover, HQAlign improves the alignment rate to 89.35% from minimap2 85.64% for nanopore reads alignment to recent telomere-to-telomere CHM13 assembly, and it improves to 86.65% from 83.48% for nanopore reads alignment to GRCh37 human genome. Availability and implementationhttps://github.com/joshidhaivat/HQAlign.git. 

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