JTK: targeted diploid genome assembler.

MOTIVATION: Diploid assembly, or determining sequences of homologous chromosomes separately, is essential to elucidate genetic differences between haplotypes. One approach is to call and phase single nucleotide variants (SNVs) on a reference sequence. However, this approach becomes unstable on large segmental duplications (SDs) or structural variations (SVs) because the alignments of reads deriving from these regions tend to be unreliable. Another approach is to use highly accurate PacBio HiFi reads to output diploid assembly directly. Nonetheless, HiFi reads cannot phase homozygous regions longer than their length and require oxford nanopore technology (ONT) reads or Hi-C to produce a fully phased assembly. Is a single long-read sequencing technology sufficient to create an accurate diploid assembly? RESULTS: Here, we present JTK, a megabase-scale diploid genome assembler. It first randomly samples kilobase-scale sequences (called 'chunks') from the long reads, phases variants found on them, and produces two haplotypes. The novel idea of JTK is to utilize chunks to capture SNVs and SVs simultaneously. From 60-fold ONT reads on the HG002 and a Japanese sample, it fully assembled two haplotypes with approximately 99.9% accuracy on the histocompatibility complex (MHC) and the leukocyte receptor complex (LRC) regions, which was impossible by the reference-based approach. In addition, in the LRC region on a Japanese sample, JTK output an assembly of better contiguity than those built from high-coverage HiFi+Hi-C. In the coming age of pan-genomics, JTK would complement the reference-based phasing method to assemble the difficult-to-assemble but medically important regions. AVAILABILITY AND IMPLEMENTATION: JTK is available at https://github.com/ban-m/jtk, and the datasets are available at https://doi.org/10.5281/zenodo.7790310 or JGAS000580 in DDBJ.

Decomposing mosaic tandem repeats accurately from long reads.

MOTIVATION: Over the past 30 years, extended tandem repeats (TRs) have been correlated with ∼60 diseases with high odds ratios, and most known TRs consist of single repeat units. However, in the last few years, mosaic TRs composed of different units have been found to be associated with several brain disorders by long-read sequencing techniques. Mosaic TRs are difficult-to-characterize sequence configurations that are usually confirmed by manual inspection. Widely used tools are not designed to solve the mosaic TR problem and often fail to properly decompose mosaic TRs. RESULTS: We propose an efficient algorithm that can decompose mosaic TRs in the input string with high sensitivity. Using synthetic benchmark data, we demonstrate that our program named uTR outperforms TRF and RepeatMasker in terms of prediction accuracy, this is especially true when mosaic TRs are more complex, and uTR is faster than TRF and RepeatMasker in most cases. AVAILABILITY AND IMPLEMENTATION: The software program uTR that implements the proposed algorithm is available at https://github.com/morisUtokyo/uTR.

Investigating the mitochondrial genomic landscape of Arabidopsis thaliana by long-read sequencing.

Plant mitochondrial genomes have distinctive features compared to those of animals; namely, they are large and divergent, with sizes ranging from hundreds of thousands of to a few million bases. Recombination among repetitive regions is thought to produce similar structures that differ slightly, known as "multipartite structures," which contribute to different phenotypes. Although many reference plant mitochondrial genomes represent almost all the genes in mitochondria, the full spectrum of their structures remains largely unknown. The emergence of long-read sequencing technology is expected to yield this landscape; however, many studies aimed to assemble only one representative circular genome, because properly understanding multipartite structures using existing assemblers is not feasible. To elucidate multipartite structures, we leveraged the information in existing reference genomes and classified long reads according to their corresponding structures. We developed a method that exploits two classic algorithms, partial order alignment (POA) and the hidden Markov model (HMM) to construct a sensitive read classifier. This method enables us to represent a set of reads as a POA graph and analyze it using the HMM. We can then calculate the likelihood of a read occurring in a given cluster, resulting in an iterative clustering algorithm. For synthetic data, our proposed method reliably detected one variation site out of 9,000-bp synthetic long reads with a 15% sequencing-error rate and produced accurate clustering. It was also capable of clustering long reads from six very similar sequences containing only slight differences. For real data, we assembled putative multipartite structures of mitochondrial genomes of Arabidopsis thaliana from nine accessions sequenced using PacBio Sequel. The results indicated that there are recurrent and strain-specific structures in A. thaliana mitochondrial genomes.

A framework and an algorithm to detect low-abundance DNA by a handy sequencer and a palm-sized computer.

MOTIVATION: Detection of DNA at low abundance with respect to the entire sample is an important problem in areas such as epidemiology and field research, as these samples are highly contaminated with non-target DNA. To solve this problem, many methods have been developed to date, but all require additional time-consuming and costly procedures. Meanwhile, the MinION sequencer developed by Oxford Nanopore Technology (ONT) is considered a powerful tool for tackling this problem, as it allows selective sequencing of target DNA. The main technology employed involves rejection of an undesirable read from a specific pore by inverting the voltage of that pore, which is referred to as 'Read Until'. Despite its usefulness, several issues remain to be solved in real situations. First, limited computational resources are available in field research and epidemiological applications. In addition, a high-speed online classification algorithm is required to make a prompt decision. Lastly, the lack of a theoretical approach for modeling of selective sequencing makes it difficult to analyze and justify a given algorithm. RESULTS: In this paper, we introduced a statistical model of selective sequencing, proposed an efficient constant-time classifier for any background DNA profile, and validated its optimal precision. To confirm the feasibility of the proposed method in practice, for a pre-recorded mock sample, we demonstrate that the method can selectively sequence a 100 kb region, consisting of 0.1% of the entire read pool, and achieve approximately 500-fold amplification. Furthermore, the algorithm is shown to process 26 queries per second with a $500 palm-sized next unit of computing box using an Intel® CoreTMi7 CPU without extended computer resources such as a GPU or high-performance computing. Next, we prepared a mixed DNA pool composed of Saccharomyces cerevisiae and lambda phage, in which any 200 kb region of S.cerevisiae consists of 0.1% of the whole sample. From this sample, a 30-230 kb region of S.cerevisiae chromosome 1 was amplified approximately 30-fold. In addition, this method allowed on-the-fly changing of the amplified region according to the uncovered characteristics of a given DNA sample. AVAILABILITY AND IMPLEMENTATION: The source code is available at: https://bitbucket.org/ban-m/dyss.