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Long read sequencing data, particularly those derived from the Oxford Nanopore (ONT) sequencing platform, tend to exhibit a high error rate. Here, we present NextDenovo, a highly efficient error correction and assembly tool for noisy long reads, which achieves a high level of accuracy in genome assembly. NextDenovo can rapidly correct reads; these corrected reads contain fewer errors than other comparable tools and are characterized by fewer chimeric alignments. We applied NextDenovo to the assembly of high quality reference genomes of 35 diverse humans from across the world using ONT Nanopore long read sequencing data. Based on thesede novogenome assemblies, we were able to identify the landscape of segmental duplications and gene copy number variation in the modern human population. The use of the NextDenovo program should pave the way for population-scale long-read assembly, thereby facilitating the construction of human pan-genomes, using Nanopore long read sequencing data.

Fragmented long noisy reads (FLNRs), such as Pore-C, contain multiple fragments of varied length separated by restriction enzyme sites. Existing alignment tools have a low mapping rate for short fragments and find incorrect fragment boundaries, which affects the utilization of FLNRs for downstream studies. Here, we develop Falign, a sequence alignment method that is adapted to the nature of FLNRs. Falign adopts a two-phase approach to efficiently align both long and short fragments. Falign uses the restriction enzyme sites on the reference genome as boundaries, which avoids the problem of destroyed fragment boundaries on FLNRs. Falign employs a multiple-stage searching mechanism to effectively recover the alignments of FLNRs with multiple fragments and interchromosomal fragments. Experiments on simulated and experimental fragmented long noisy 3C datasets show that Falign can effectively recover the constructs of reads and the sampled loci of the fragments. Falign allows significantly higher data utilization for FLNRs.

Although long-read single-cell isoform sequencing (scISO-Seq) can reveal transcriptomic dynamics in individual cells invisible to NGS-based single-cell RNA analysis, scISO-Seq has been limited by low throughput, high error rates, and long running time.Here, we introduce HIT-scISOseq, the first method that concatenates multiple full-length cDNAs for PacBio circular consensus sequencing (CCS) sequencing to achievehigh-throughput, and high-accuracy single-cell isoform sequencing. HIT-scISOseq can yield >10 million high-accuracy full-length isoforms in a single PacBio Sequel II SMRT Cell 8M. We have developed scISA-Tools that demultiplex HIT-scISOseq concatenated reads into single-cell full-length isoforms with >99.99% accuracy and specificity. We have applied HIT-scISOseq to characterize the transcriptome of thousands of corneal limbus cells, and reveal cell-type-specific isoform expression changes that are previously not identified by NGS-based scRNAseq. HIT-scISOseq is a high-throughput, high-accuracy, and technically accessible method that can be used by most laboratories to accelerate the burgeoning field of long-read single-cell transcriptomics.

Isoform expression in preimplantation embryos has been extensively investigated, and many novel isoforms have been identified. However, the regulation patterns of different types of transcripts along the developing stages remainsunexplored. We quantified the expression of full-length isoforms in over one hundred single blastomeres from the mouse oocyte to blastocyst stage and found that the 3’ prime partial transcripts that lack stop codonswere highly accumulated in oocytes and zygotes. A typical 3’ prime partial isoform,Ncl-S-350, was demonstratedto be not a transcription by-productbutto function in the ZGA process. SRSF4 was identified to be responsible for generating these 3’ prime partial transcripts in mouse embryonic stem cells (mESCs), and both Ncl-S-350 and Srsf4 overexpression could convert mESCs to a 2-cell (2C)-like state. Our work reveals the important role of isoform switch regulation in early embryonic development and lays the foundation for an alternative way of acquiring totipotent cells.

Canonical three-dimensional (3D) genome structures represent the ensemble average of pairwise chromatin interactions but not the single-allele topologies in populations of cells. Recently developed Pore-C can capture multiway chromatin contacts that reflect regional topologies of single chromosomes. By carrying out high-throughput Pore-C, we reveal extensive but regionally restricted clusters of single-allele topologies that aggregate into canonical 3D genome structures in two human cell types. We show that fragments in multicontact reads generally coexist in the same TAD, while a concurrent significant proportion of multicontact reads span multiple compartments of the same chromatin type over megabase distances. Synergistic chromatin looping between multiple sites in multicontact reads is extremely rare compared to pairwise interactions. Interestingly, the single-allele topology clusters are cell type-specific even inside highly conserved TADs in different types of cells. In summary, HiPore-C enables global characterization of single-allele topologies at an unprecedented depth to reveal elusive genome folding principles.

Motivation Oxford Nanopore sequencing has great potential and advantages in population-scale studies. Due to the cost of sequencing, the depth of whole-genome sequencing for per individual sample must be small. However, the existing single nucleotide polymorphism (SNP) callers are aimed at high-coverage Nanopore sequencing reads. Detecting the SNP variants on low-coverage Nanopore sequencing data is still a challenging problem. Results We developed a novel deep learning-based SNP calling method, NanoSNP, to identify the SNP sites (excluding short indels) based on low-coverage Nanopore sequencing reads. In this method, we design a multi-step, multi-scale and haplotype-aware SNP detection pipeline. First, the pileup model in NanoSNP utilizes the naive pileup feature to predict a subset of SNP sites with a Bi-long short-term memory (LSTM) network. These SNP sites are phased and used to divide the low-coverage Nanopore reads into different haplotypes. Finally, the long-range haplotype feature and short-range pileup feature are extracted from each haplotype. The haplotype model combines two features and predicts the genotype for the candidate site using a Bi-LSTM network. To evaluate the performance of NanoSNP, we compared NanoSNP with Clair, Clair3, Pepper-DeepVariant and NanoCaller on the low-coverage (∼16×) Nanopore sequencing reads. We also performed cross-genome testing on six human genomes HG002–HG007, respectively. Comprehensive experiments demonstrate that NanoSNP outperforms Clair, Pepper-DeepVariant and NanoCaller in identifying SNPs on low-coverage Nanopore sequencing data, including the difficult-to-map regions and major histocompatibility complex regions in the human genome. NanoSNP is comparable to Clair3 when the coverage exceeds 16×. Availability and implementation https://github.com/huangnengCSU/NanoSNP.git. Supplementary information Supplementary data are available at Bioinformatics online.

Long single-molecular sequencing, such as PacBio circular consensus sequencing (CCS) and nanopore sequencing, is advantageous in detecting DNA 5-methylcytosine (5mC) in CpGs, especially in repetitive genomic regions. However, existing methods for detecting 5mCpGs using PacBio CCS are less accurate and robust. Here, we present ccsmeth, a deep-learning method to detect DNA 5mCpGs using CCS reads. We sequence PCR-treated and M.SssI-treated DNA of one human sample using PacBio CCS for training ccsmeth. Using long (≥10Kb) CCS reads, ccsmeth achieves 0.90 accuracy and 0.97 AUC on 5mCpG detection at single-molecule resolution. At the genome-wide site level, ccsmeth achieves >0.90 correlations with bisulfite sequencing and nanopore sequencing using only 10× reads. Furthermore, we develop a Nextflow pipeline, ccsmethphase, to detect haplotype-aware methylation using CCS reads, and then sequence a Chinese family trio to validate it. ccsmeth and ccsmethphase can be robust and accurate tools for detecting DNA 5mCs using PacBio CCS.