More Like this

1. Nanopore decoding with speed and versatility for data storage

   https://doi.org/10.1093/bioinformatics/btaf006  

   Volkel, Kevin D; Hook, Paul W; Keung, Albert; Timp, Winston; Tuck, James M (December 2024, Bioinformatics)

   Mathelier, Anthony (Ed.)

   Abstract MotivationAs nanopore technology reaches ever higher throughput and accuracy, it becomes an increasingly viable candidate for reading out DNA data storage. Nanopore sequencing offers considerable flexibility by allowing long reads, real-time signal analysis, and the ability to read both DNA and RNA. We need flexible and efficient designs that match nanopore’s capabilities, but relatively few designs have been explored and many have significant inefficiency in read density, error rate, or compute time. To address these problems, we designed a new single-read per-strand decoder that achieves low byte error rates, offers high throughput, scales to long reads, and works well for both DNA and RNA molecules. We achieve these results through a novel soft decoding algorithm that can be effectively parallelized on a GPU. Our faster decoder allows us to study a wider range of system designs. ResultsWe demonstrate our approach on HEDGES, a state-of-the-art DNA-constrained convolutional code. We implement one hard decoder that runs serially and two soft decoders that run on GPUs. Our evaluation for each decoder is applied to the same population of nanopore reads collected from a synthesized library of strands. These same strands are synthesized with a T7 promoter to enable RNA transcription and decoding. Our results show that the hard decoder has a byte error rate over 25%, while the prior state of the art soft decoder can achieve error rates of 2.25%. However, that design also suffers a low throughput of 183 s/read. Our new Alignment Matrix Trellis soft decoder improves throughput by 257× with the trade-off of a higher byte error rate of 3.52% compared to the state of the art. Furthermore, we use the faster speed of our algorithm to explore more design options. We show that read densities of 0.33 bits/base can be achieved, which is 4× larger than prior MSA-based decoders. We also compare RNA to DNA, and find that RNA has 85% as many error-free reads when compared to DNA. Availability and implementationSource code for our soft decoder and data used to generate figures is available publicly in the Github repository https://github.com/dna-storage/hedges-soft-decoder (10.5281/zenodo.11454877). All raw FAST5/FASTQ data are available at 10.5281/zenodo.11985454 and 10.5281/zenodo.12014515. 

   more » « less
2. Genome-wide alignment-free phylogenetic distance estimation under a no strand-bias model

   https://doi.org/10.1093/bioadv/vbac055  

   Balaban, Metin; Bristy, Nishat Anjum; Faisal, Ahnaf; Bayzid, Md Shamsuzzoha; Mirarab, Siavash; Lengauer, ed., Thomas (August 2022, Bioinformatics Advances)

   Abstract  Summary: While alignment has been the dominant approach for determining homology prior to phylogenetic inference, alignment-free methods can simplify the analysis, especially when analyzing genome-wide data. Furthermore, alignment-free methods present the only option for emerging forms of data, such as genome skims, which do not permit assembly. Despite the appeal, alignment-free methods have not been competitive with alignment-based methods in terms of accuracy. One limitation of alignment-free methods is their reliance on simplified models of sequence evolution such as Jukes–Cantor. If we can estimate frequencies of base substitutions in an alignment-free setting, we can compute pairwise distances under more complex models. However, since the strand of DNA sequences is unknown for many forms of genome-wide data, which arguably present the best use case for alignment-free methods, the most complex models that one can use are the so-called no strand-bias models. We show how to calculate distances under a four-parameter no strand-bias model called TK4 without relying on alignments or assemblies. The main idea is to replace letters in the input sequences and recompute Jaccard indices between k-mer sets. However, on larger genomes, we also need to compute the number of k-mer mismatches after replacement due to random chance as opposed to homology. We show in simulation that alignment-free distances can be highly accurate when genomes evolve under the assumed models and study the accuracy on assembled and unassembled biological data. Availability and implementationOur software is available open source at https://github.com/nishatbristy007/NSB. Supplementary informationSupplementary data are available at Bioinformatics Advances online. 

   more » « less
3. 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. 

   more » « less
4. EMMA: a new method for computing multiple sequence alignments given a constraint subset alignment

   https://doi.org/10.1186/s13015-023-00247-x  

   Shen, Chengze; Liu, Baqiao; Williams, Kelly P.; Warnow, Tandy (December 2023, Algorithms for Molecular Biology)

   Abstract BackgroundAdding sequences into an existing (possibly user-provided) alignment has multiple applications, including updating a large alignment with new data, adding sequences into a constraint alignment constructed using biological knowledge, or computing alignments in the presence of sequence length heterogeneity. Although this is a natural problem, only a few tools have been developed to use this information with high fidelity. ResultsWe present EMMA (Extending Multiple alignments using MAFFT--add) for the problem of adding a set of unaligned sequences into a multiple sequence alignment (i.e., a constraint alignment). EMMA builds on MAFFT--add, which is also designed to add sequences into a given constraint alignment. EMMA improves on MAFFT--add methods by using a divide-and-conquer framework to scale its most accurate version, MAFFT-linsi--add, to constraint alignments with many sequences. We show that EMMA has an accuracy advantage over other techniques for adding sequences into alignments under many realistic conditions and can scale to large datasets with high accuracy (hundreds of thousands of sequences). EMMA is available athttps://github.com/c5shen/EMMA. ConclusionsEMMA is a new tool that provides high accuracy and scalability for adding sequences into an existing alignment. 

   more » « less
5. AlignS: A Processing-In-Memory Accelerator for DNA Short Read Alignment Leveraging SOT-MRAM

   Angizi, Shaahin; Sun, Jiao; Zhang, Wei; Fan, Deliang (June 2019, 56th Annual Design Automation Conference)

   Classified as a complex big data analytics problem, DNA short read alignment serves as a major sequential bottleneck to massive amounts of data generated by next-generation sequencing platforms. With Von-Neumann computing architectures struggling to address such computationally-expensive and memory-intensive task today, Processing-in-Memory (PIM) platforms are gaining growing interests. In this paper, an energy-efficient and parallel PIM accelerator (AlignS) is proposed to execute DNA short read alignment based on an optimized and hardware-friendly alignment algorithm. We first develop AlignS platform that harnesses SOT-MRAM as computational memory and transforms it to a fundamental processing unit for short read alignment. Accordingly, we present a novel, customized, highly parallel read alignment algorithm that only seeks the proposed simple and parallel in-memory operations (i.e. comparisons and additions). AlignS is then optimized through a new correlated data partitioning and mapping methodology that allows local storage and processing of DNA sequence to fully exploit the algorithm-level's parallelism, and to accelerate both exact and inexact matches. The device-to-architecture co-simulation results show that AlignS improves the short read alignment throughput per Watt per mm^2 by ~12X compared to the ASIC accelerator. Compared to recent FM-index-based ReRAM platform, AlignS achieves 1.6X higher throughput per Watt. 

   more » « less