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1. 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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2. Ghost in the Machine: Evidence for Non-Random Errors During Direct RNA Nanopore Sequencing Due to Post-Translocated RNA Folding

   https://doi.org/10.64898/2025.12.02.691860  

   Needham, Jason M; Johnson, Philip Z; Simon, Anne E (December 2025, bioRxiv)

   ABSTRACT Direct RNA nanopore sequencing allows for the identification of full-length RNAs with a ∼10% error rate consisting of mismatches and small deletions. These errors are thought to be randomly distributed and structure-independent since RNA/cDNA duplexes are generated to prevent RNA structure formation prior to sequencing. When analyzing citrus yellow vein associated virus (CY1) reads during infection ofNicotiana benthamiana,viral (+/-)foldback RNAs (i.e., viral plus [+]-strands joined to [-]-strands) showed significantly higher error rates (mismatches and deletions) in the 5ʹ (+)RNA portion with errors that were relatively evenly distributed, while errors in the attached (-)RNA portion were less frequent and unevenly distributed. Non-foldback CY1 (+)RNAs from infected plants also showed an uneven distribution of errors, which correlated with errors inin vitrotranscribed CY1 (+)RNA reads in both position and frequency. Hotspot errors in non-foldback CY1 (+)RNA and (-)RNA reads only weakly correlated, and hotspots were frequently located 5ʹ of known structural elements. Since nanopore sequencing is also used to identify RNA modifications, which depend on base-specific sequencing errors, algorithms for RNA modification detection were also examined for bias. We found that multiple programs predicted RNA modifications inin vitrotranscribed CY1 RNA at the same positions and with similar confidence levels as within plantaCY1 RNA. These data suggest that direct RNA sequencing contains inherent error biases that may be associated with post-translocation RNA folding and low sequence complexity, and therefore extrapolations based on sequencing error require special consideration. 

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3. 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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4. Understanding the natural language of DNA using encoder–decoder foundation models with byte-level precision

   https://doi.org/10.1093/bioadv/vbae117  

   Malusare, Aditya; Kothandaraman, Harish; Tamboli, Dipesh; Lanman, Nadia A; Aggarwal, Vaneet (January 2024, Bioinformatics Advances)

   Lengauer, Thomas (Ed.)

   Abstract SummaryThis article presents the Ensemble Nucleotide Byte-level Encoder-Decoder (ENBED) foundation model, analyzing DNA sequences at byte-level precision with an encoder–decoder Transformer architecture. ENBED uses a subquadratic implementation of attention to develop an efficient model capable of sequence-to-sequence transformations, generalizing previous genomic models with encoder-only or decoder-only architectures. We use Masked Language Modeling to pretrain the foundation model using reference genome sequences and apply it in the following downstream tasks: (i) identification of enhancers, promotors, and splice sites, (ii) recognition of sequences containing base call mismatches and insertion/deletion errors, an advantage over tokenization schemes involving multiple base pairs, which lose the ability to analyze with byte-level precision, (iii) identification of biological function annotations of genomic sequences, and (iv) generating mutations of the Influenza virus using the encoder–decoder architecture and validating them against real-world observations. In each of these tasks, we demonstrate significant improvement as compared to the existing state-of-the-art results. Availability and implementationThe source code used to develop and fine-tune the foundation model has been released on Github (https://github.itap.purdue.edu/Clan-labs/ENBED). 

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5. Towards quantum belief propagation for LDPC decoding in wireless networks

   https://doi.org/10.1145/3372224.3419207  

   Kasi, Srikar; Jamieson, Kyle (September 2020, Proceedings of the 26th Annual International Conference on Mobile Computing and Networking (ACM MobiCom))

   We present Quantum Belief Propagation (QBP), a Quantum Annealing (QA) based decoder design for Low Density Parity Check (LDPC) error control codes, which have found many useful applications in Wi-Fi, satellite communications, mobile cellular systems, and data storage systems. QBP reduces the LDPC decoding to a discrete optimization problem, then embeds that reduced design onto quantum annealing hardware. QBP's embedding design can support LDPC codes of block length up to 420 bits on real state-of-the-art QA hardware with 2,048 qubits. We evaluate performance on real quantum annealer hardware, performing sensitivity analyses on a variety of parameter settings. Our design achieves a bit error rate of 10--8 in 20 μs and a 1,500 byte frame error rate of 10--6 in 50 μs at SNR 9 dB over a Gaussian noise wireless channel. Further experiments measure performance over real-world wireless channels, requiring 30 μs to achieve a 1,500 byte 99.99% frame delivery rate at SNR 15-20 dB. QBP achieves a performance improvement over an FPGA based soft belief propagation LDPC decoder, by reaching a bit error rate of 10--8 and a frame error rate of 10--6 at an SNR 2.5--3.5 dB lower. In terms of limitations, QBP currently cannot realize practical protocol-sized (e.g., Wi-Fi, WiMax) LDPC codes on current QA processors. Our further studies in this work present future cost, throughput, and QA hardware trend considerations. 

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