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1. Sketching and sampling approaches for fast and accurate long read classification

   https://doi.org/10.1186/s12859-022-05014-0  

   Das, Arun; Schatz, Michael C. (December 2022, BMC Bioinformatics)

   Abstract Background In modern sequencing experiments, quickly and accurately identifying the sources of the reads is a crucial need. In metagenomics, where each read comes from one of potentially many members of a community, it can be important to identify the exact species the read is from. In other settings, it is important to distinguish which reads are from the targeted sample and which are from potential contaminants. In both cases, identification of the correct source of a read enables further investigation of relevant reads, while minimizing wasted work. This task is particularly challenging for long reads, which can have a substantial error rate that obscures the origins of each read. Results Existing tools for the read classification problem are often alignment or index-based, but such methods can have large time and/or space overheads. In this work, we investigate the effectiveness of several sampling and sketching-based approaches for read classification. In these approaches, a chosen sampling or sketching algorithm is used to generate a reduced representation (a “screen”) of potential source genomes for a query readset before reads are streamed in and compared against this screen. Using a query read’s similarity to the elements of the screen, the methods predict the source of the read. Such an approach requires limited pre-processing, stores and works with only a subset of the input data, and is able to perform classification with a high degree of accuracy. Conclusions The sampling and sketching approaches investigated include uniform sampling, methods based on MinHash and its weighted and order variants, a minimizer-based technique, and a novel clustering-based sketching approach. We demonstrate the effectiveness of these techniques both in identifying the source microbial genomes for reads from a metagenomic long read sequencing experiment, and in distinguishing between long reads from organisms of interest and potential contaminant reads. We then compare these approaches to existing alignment, index and sketching-based tools for read classification, and demonstrate how such a method is a viable alternative for determining the source of query reads. Finally, we present a reference implementation of these approaches at https://github.com/arun96/sketching . 

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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. Accurate detection of complex structural variations using single molecule sequencing

   https://doi.org/doi:10.1038/s41592-018-0001-7  

   Sedlazeck, FJ; Reschender, P; Smolka, M; Fang, H; Nattestad, M; von Haeseler, A; Schatz, MC (January 2018, Nature methods)

   Structural variations are the greatest source of genetic variation, but they remain poorly understood because of technological limitations. Single-molecule long-read sequencing has the potential to dramatically advance the field, although high error rates are a challenge with existing methods. Addressing this need, we introduce open-source methods for long-read alignment (NGMLR; https://github.com/philres/ngmlr ) and structural variant identification (Sniffles; https://github.com/fritzsedlazeck/Sniffles ) that provide unprecedented sensitivity and precision for variant detection, even in repeat-rich regions and for complex nested events that can have substantial effects on human health. In several long-read datasets, including healthy and cancerous human genomes, we discovered thousands of novel variants and categorized systematic errors in short-read approaches. NGMLR and Sniffles can automatically filter false events and operate on low-coverage data, thereby reducing the high costs that have hindered the application of long reads in clinical and research settings 

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4. 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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5. Semi-Fast Byzantine-tolerant Shared Register without Reliable Broadcast

   Konwar, Kishori Kumar (December 2020, 40th IEEE International Conference on Distributed Computing Systems)

   Shared register emulations on top of message- passing systems provide an illusion of a simpler shared memory system which can make the task of a system designer easier. Numerous shared register applications have a considerably high read to write ratio. Thus having algorithms that make reads more efficient than writes is a fair trade-off. Typically such algorithms for reads and writes are asymmetric and sacrifice the stringent consistency condition atomicity as it is impossible to have fast reads for multi-writer atomicity. Safety is a consistency condition has has gathered interest from both the systems and theory community as it is weaker than atomicity yet provides strong enough guarantees like “strong consistency” or read-my-write consistency. One requirement that is assumed by many researchers is that of the reliable broadcast (RB) primitive, which ensures the all or none property during a broadcast. One drawback is that such a primitive takes 1.5 rounds to complete. This paper implements an efficient multi-writer multi-reader safe register without using a reliable broadcast primitive. More- over, we provide fast reads or one-shot reads – our read operation can be completed in one round of client-to-server communication. Of course, this comes with the price of requiring more servers when compared to prior solutions assuming reliable broadcast. However, we show that this increased number of servers is indeed necessary as we prove a tight bound on the number of servers required to implement Byzantine-fault tolerant safe registers in a system without reliable broadcast. We extend our results to data stored using erasure coding as well. We present an emulation of single-writer multi-reader safe register based on MDS code. The usage of MDS code reduces storage cost and communication cost. On the negative side, we also show that to use MDS code and achieve one-shot read at the same time, we need even more servers. 

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