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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.

In plants, cytosine DNA methylations (5mCs) can happen in three sequence contexts as CpG, CHG, and CHH (where H = A, C, or T), which play different roles in the regulation of biological processes. Although long Nanopore reads are advantageous in the detection of 5mCs comparing to short-read bisulfite sequencing, existing methods can only detect 5mCs in the CpG context, which limits their application in plants. Here, we develop DeepSignal-plant, a deep learning tool to detect genome-wide 5mCs of all three contexts in plants from Nanopore reads. We sequence Arabidopsis thaliana and Oryza sativa using both Nanopore and bisulfite sequencing. We develop a denoising process for training models, which enables DeepSignal-plant to achieve high correlations with bisulfite sequencing for 5mC detection in all three contexts. Furthermore, DeepSignal-plant can profile more 5mC sites, which will help to provide a more complete understanding of epigenetic mechanisms of different biological processes., Existing methods cannot profile genome-wide cytosine DNA methylations (5mCs) in all three contexts with acceptable accuracy. Here, the authors develop a deep learning tool to detect genome-wide 5mCs of all three contexts in plants with high accuracy from Nanopore reads.

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 nanopore reads are advantageous in de novo genome assembly. However, nanopore reads usually have broad error distribution and high-error-rate subsequences. Existing error correction tools cannot correct nanopore reads efficiently and effectively. Most methods trim high-error-rate subsequences during error correction, which reduces both the length of the reads and contiguity of the final assembly. Here, we develop an error correction, and de novo assembly tool designed to overcome complex errors in nanopore reads. We propose an adaptive read selection and two-step progressive method to quickly correct nanopore reads to high accuracy. We introduce a two-stage assembler to utilize the full length of nanopore reads. Our tool achieves superior performance in both error correction and de novo assembling nanopore reads. It requires only 8122 hours to assemble a 35X coverage human genome and achieves a 2.47-fold improvement in NG50. Furthermore, our assembly of the human WERI cell line shows an NG50 of 22 Mbp. The high-quality assembly of nanopore reads can significantly reduce false positives in structure variation detection., Nanopore reads have been advantageous for de novo genome assembly; however these reads have high error rates. Here, the authors develop an error correction and de novo assembly tool, NECAT, which produces efficient, high quality assemblies of nanopore reads.

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.

The single-molecular sequencing (SMS) is under rapid development and generating increasingly long and accurate sequences. De novo assembly of genomes from SMS sequences is a critical step for many genomic studies. To scale well with the developing trends of SMS, many de novo assemblers for SMS have been released. These assembly workflows can be categorized into two different kinds: the correction-and-assembly strategy and the assembly-and-correction strategy, both of which are gaining more and more attentions. In this article we make a discussion on the characteristics of errors in SMS sequences. We then review the currently widely applied de novo assemblers for SMS sequences. We also describe computational methods relevant to de novo assembly, including the alignment methods and the error correction methods. Benchmarks are provided to analyze their performance on different datasets and to provide use guides on applying the computation methods. Conclusion: We make a detailed review on the latest development of de novo assembly and some relevant algorithms for SMS, including their rationales, solutions and results. Besides, we provide use guides on the algorithms based on their benchmark results. Finally we conclude the review by giving some developing trends of third generation sequencing (TGS).

COVID-19, caused by SARS-CoV-2, has recently affected over 1,200,000 people and killed more than 60,000. The key immune cell subsets change and their states during the course of COVID-19 remain unclear. We sought to comprehensively characterize the transcriptional changes in peripheral blood mononuclear cells during the recovery stage of COVID-19 by single-cell RNA sequencing technique. It was found that T cells decreased remarkably, whereas monocytes increased in patients in the early recovery stage (ERS) of COVID-19. There was an increased ratio of classical CD14++ monocytes with high inflammatory gene expression as well as a greater abundance of CD14++IL1β+ monocytes in the ERS. CD4+ T cells and CD8+ T cells decreased significantly and expressed high levels of inflammatory genes in the ERS. Among the B cells, the plasma cells increased remarkably, whereas the naïve B cells decreased. Several novel B cell-receptor (BCR) changes were identified, such as IGHV3-23 and IGHV3-7, and isotypes (IGHV3-15, IGHV3-30, and IGKV3-11) previously used for virus vaccine development were confirmed. The strongest pairing frequencies, IGHV3-23-IGHJ4, indicated a monoclonal state associated with SARS-CoV-2 specificity, which had not been reported yet. Furthermore, integrated analysis predicted that IL-1β and M-CSF may be novel candidate target genes for inflammatory storm and that TNFSF13, IL-18, IL-2, and IL-4 may be beneficial for the recovery of COVID-19 patients. Our study provides the first evidence of an inflammatory immune signature in the ERS, suggesting COVID-19 patients are still vulnerable after hospital discharge. Identification of novel BCR signaling may lead to the development of vaccines and antibodies for the treatment of COVID-19.

Eukaryotic DNA methylation has been receiving increasing attention for its crucial epigenetic regulatory function. The recently developed single-molecule real-time (SMRT) sequencing technology provides an efficient way to detect DNA N6-methyladenine (6mA) and N4-methylcytosine (4mC) modifications at a single-nucleotide resolution. The family Rosaceae contains horticultural plants with a wide range of economic importance. However, little is currently known regarding the genome-wide distribution patterns and functions of 6mA and 4mC modifications in the Rosaceae. In this study, we present an integrated DNA 6mA and 4mC modification database for the Rosaceae (MDR, http://mdr.xieslab.org). MDR, the first repository for displaying and storing DNA 6mA and 4mC methylomes from SMRT sequencing data sets for Rosaceae, includes meta and statistical information, methylation densities, Gene Ontology enrichment analyses, and genome search and browse for methylated sites in NCBI. MDR provides important information regarding DNA 6mA and 4mC methylation and may help users better understand epigenetic modifications in the family Rosaceae.

Motivation The Oxford Nanopore sequencing enables to directly detect methylation states of bases in DNA from reads without extra laboratory techniques. Novel computational methods are required to improve the accuracy and robustness of DNA methylation state prediction using Nanopore reads. Results In this study, we develop DeepSignal, a deep learning method to detect DNA methylation states from Nanopore sequencing reads. Testing on Nanopore reads of Homo sapiens (H. sapiens), Escherichia coli (E. coli) and pUC19 shows that DeepSignal can achieve higher performance at both read level and genome level on detecting 6 mA and 5mC methylation states comparing to previous hidden Markov model (HMM) based methods. DeepSignal achieves similar performance cross different DNA methylation bases, different DNA methylation motifs and both singleton and mixed DNA CpG. Moreover, DeepSignal requires much lower coverage than those required by HMM and statistics based methods. DeepSignal can achieve 90% above accuracy for detecting 5mC and 6 mA using only 2× coverage of reads. Furthermore, for DNA CpG methylation state prediction, DeepSignal achieves 90% correlation with bisulfite sequencing using just 20× coverage of reads, which is much better than HMM based methods. Especially, DeepSignal can predict methylation states of 5% more DNA CpGs that previously cannot be predicted by bisulfite sequencing. DeepSignal can be a robust and accurate method for detecting methylation states of DNA bases. Availability and implementation DeepSignal is publicly available at https://github.com/bioinfomaticsCSU/deepsignal. Supplementary information Supplementary data are available at bioinformatics online.

Graphical abstract, SPLiT-seq provides a low-cost platform to generate single-cell data by labeling the cellular origin of RNA through four rounds of combinatorial barcoding. However, an automatic and rapid method for preprocessing and classifying single-cell sequencing (SCS) data from SPLiT-seq, which directly identified and labeled combinatorial barcoding reads and distinguished special cell sequencing data, is currently lacking. Here, we develop a high-efficiency preprocessing tool for single-cell sequencing data from SPLiT-seq (SCSit), which can directly identify combinatorial barcodes and UMI of cell types and obtain more labeled reads, and remarkably enhance the retained data from SCS due to the exact alignment of insertion and deletion. Compared with the original method used in SPLiT-seq, the consistency of identified reads from SCSit increases to 97%, and mapped reads are twice than the original. Furthermore, the runtime of SCSit is less than 10% of the original. It can accurately and rapidly analyze SPLiT-seq raw data and obtain labeled reads, as well as effectively improve the single-cell data from SPLiT-seq platform. The data and source of SCSit are available on the GitHub website https://github.com/shang-qian/SCSit.

Methylation states of DNA bases can be detected from native Nanopore reads directly. At present, there are many computational methods that can detect 5mCs in CpG contexts accurately by Nanopore sequencing. However, there is currently a lack of methods to detect 5mCs in non-CpG contexts. In this study, we propose a computational pipeline which can detect 5mC sites in both CpG and non-CpG contexts of plant genomes by using Nanopore sequencing. And we sequenced two model plants Arabidopsis thaliana (A. thaliana) and Oryza sativa (O. sativa) by using Nanopore sequencing and bisulfite sequencing. The results of our proposed pipeline in the two plants achieved high correlations with bisulfite sequencing: above 0.98, 0.96, 0.85 for CpG, CHG, and CHH (H indicates A, C or T) motif, respectively. Our proposed pipeline also achieved high performance on Brassica nigra (B. nigra). Experiments also showed that our proposed pipeline can achieve high performance even with low coverage of reads. Moreover, by using Nanopore sequencing, our proposed pipeline is capable of profiling methylation of more cytosines than bisulfite sequencing.

Coronavirus disease 2019 (COVID-19), driven by SARS-CoV-2, is a severe infectious disease that has become a global health threat. Vaccines are among the most effective public health tools for combating COVID-19. Immune status is critical for evaluating the safety and response to the vaccine, however, the evolution of the immune response during immunization remains poorly understood. Single-cell RNA sequencing (scRNA-seq) represents a powerful tool for dissecting multicellular behavior and discovering therapeutic antibodies. Herein, by performing scRNA/V(D)J-seq on peripheral blood mononuclear cells from four COVID-19 vaccine trial participants longitudinally during immunization, we revealed enhanced cellular immunity with concerted and cell type-specific IFN responses as well as boosted humoral immunity with SARS-CoV-2-specific antibodies. Based on the CDR3 sequence and germline enrichment, we were able to identify several potential binding antibodies. We synthesized, expressed and tested 21 clones from the identified lineages. Among them, one monoclonal antibody (P3V6-1) exhibited relatively high affinity with the extracellular domain of Spike protein, which might be a promising therapeutic reagent for COVID-19. Overall, our findings provide insights for assessing vaccine through the novel scRNA/V(D)J-seq approach, which might facilitate the development of more potent, durable and safe prophylactic vaccines.

BackgroundRedundancy is a common feature of genomes, presumably to ensure robust growth under different and changing conditions. Genome compaction, removing sequences nonessential for given conditions, provides a novel way to understand the core principles of life. The synthetic chromosome rearrangement and modification by loxP-mediated evolution (SCRaMbLE) system is a unique feature implanted in the synthetic yeast genome (Sc2.0), which is proposed as an effective tool for genome minimization. As the Sc2.0 project is nearing its completion, we have begun to explore the application of the SCRaMbLE system in genome compaction.ResultsWe develop a method termed SCRaMbLE-based genome compaction (SGC) and demonstrate that a synthetic chromosome arm (synXIIL) can be efficiently reduced. The pre-introduced episomal essential gene array significantly enhances the compacting ability of SGC, not only by enabling the deletion of nonessential genes located in essential gene containing loxPsym units but also by allowing more chromosomal sequences to be removed in a single SGC process. Further compaction is achieved through iterative SGC, revealing that at least 39 out of 65 nonessential genes in synXIIL can be removed collectively without affecting cell viability at 30 °C in rich medium. Approximately 40% of the synthetic sequence, encoding 28 genes, is found to be dispensable for cell growth at 30 °C in rich medium and several genes whose functions are needed under specified conditions are identified.ConclusionsWe develop iterative SGC with the aid of eArray as a generic yet effective tool to compact the synthetic yeast genome.

Age-associated changes in immune cells have been linked to an increased risk for infection. However, a global and detailed characterization of the changes that human circulating immune cells undergo with age is lacking. Here, we combined scRNA-seq, mass cytometry and scATAC-seq to compare immune cell types in peripheral blood collected from young and old subjects and patients with COVID-19. We found that the immune cell landscape was reprogrammed with age and was characterized by T cell polarization from naive and memory cells to effector, cytotoxic, exhausted and regulatory cells, along with increased late natural killer cells, age-associated B cells, inflammatory monocytes and age-associated dendritic cells. In addition, the expression of genes, which were implicated in coronavirus susceptibility, was upregulated in a cell subtype-specific manner with age. Notably, COVID-19 promoted age-induced immune cell polarization and gene expression related to inflammation and cellular senescence. Therefore, these findings suggest that a dysregulated immune system and increased gene expression associated with SARS-CoV-2 susceptibility may at least partially account for COVID-19 vulnerability in the elderly. Electronic supplementary material The online version of this article (10.1007/s13238-020-00762-2) contains supplementary material, which is available to authorized users.

Single-cell isoform sequencing can reveal transcriptomic dynamics in individual cells invisible to bulk- and single-cell RNA analysis based on short-read sequencing. However, current long-read single-cell sequencing technologies have been limited by low throughput and high error rate. Here we introduce HIT-scISOseq for high-throughput single-cell isoform sequencing. This method was made possible by full-length cDNA capture using biotinylated PCR primers, and by our novel library preparation procedure that combines head-to-tail concatemeric full-length cDNAs into a long SMRTbell insert for high-accuracy PacBio sequencing. HIT-scISOseq yields > 10 million high-accuracy full-length isoforms in a single PacBio Sequel II 8M SMRT Cell, providing > 8 times more data output than the standard single-cell isoform PacBio sequencing protocol. We exemplified HIT-scISOseq by first studying transcriptome profiles of 4,000 normal and 8,000 injured corneal epitheliums from cynomolgus monkeys. We constructed dynamic transcriptome landscapes of known and rare cell types, revealed novel isoforms, and identified injury-related splicing and switching events that are previously not accessible with low throughput isoform sequencing. HIT-scISOseq represents a high-throughput, cost-effective, and technically simple method to accelerate the burgeoning field of long-read single-cell transcriptomics.

In the middle of March, the World Health Organization declared the outbreak of COVID-19 caused by SARS-CoV-2 infection a global pandemic. While China experienced a dramatic decline in daily growth rate of COVID-19, multiple importations of new cases from other countries and their related local infections caused a rapid rise. Between March 12 and April 15, we collected nasopharyngeal samples from 109 imported cases from 25 countries and 69 local cases in Guangzhou, China. In order to characterize the transmission patterns and genetic evolution of this virus among different populations, we sequenced the genome of SARS-CoV-2. The imported viral strains were assigned to lineages distributed in Europe (33.0%), America (17.4%), Africa (25.7%), or Southeast/West Asia (23.9%). Importantly, 10 imported cases from Africa formed two novel sub-lineages not identified in global tree previously. A detailed analysis showed that the imported viral strains from Philippines and Pakistan were closely related and within the same sub-lineage, whereas Ethiopia had varied lineages in the African phylogenetic tree. In spite of the diversity of imported SARS-CoV-2, 60 of 69 local infections could be traced back to two specific small lineages imported from Africa. A combined genetic and epidemiological analysis revealed a high-resolution transmission network of the imported SARS-CoV-2 in local communities, which might help inform the public health response and genomic surveillance in other cities and regions. Finally, we observed in-frame deletions on seven loci of SARS-CoV-2 genome, some of which were intra-host mutations, and they exhibited no enrichment on the S protein. Our findings provide new insight into the viral phylodynamics of SARS-CoV-2 and beta coronavirus.

COVID-19, caused by SARS-CoV-2, has recently affected over 1,200,000 people and killed more than 60,000. The key immune cell subsets change and their states during the course of COVID-19 remain unclear. We sought to comprehensively characterize the transcriptional changes in peripheral blood mononuclear cells during the recovery stage of COVID-19 by single-cell RNA sequencing technique. It was found that T cells decreased remarkably, whereas monocytes increased in patients in the early recovery stage (ERS) of COVID-19. There was an increased ratio of classical CD14

Background Microorganisms in a shrimp cultural pond ecosystem (SCPE) play important roles for animal and environmental health. While the microbial diversity and assembly mechanism, and how communities of multiple habitats contribute to the SCPE metacommunity are poorly understood. Here, we analyzed phylogenetic structure of 792 samples of three habitats (water, shrimp intestine and sediment) from 88 shrimp cultural ponds across six regional sites in China. Results We show that the microbial communities of three habitats in the SCPE contained 15,197 operational taxonomic units (OTUs), and the microbial community of sediment had the highest OTUs (14,857). Despite the high microbial diversity, the SCPE has small core taxa (13 OTUs) among three habitats, and 30, 15, 53 core OTUs in water, shrimp intestine, and sediment, respectively. Especially, some opportunistic pathogens or functional microbes were core taxa in shrimp intestine or water and sediment. Of the SCPE metacommunity, the microbial diversity of the SCPE was mainly contributed by‾αLocalCommunities (66.0%), and followed by βInterHabitats (29.0%). The sediment microbial communities had a larger contribution (56.8%) to shrimp intestine microbiota compared to water communities (18.1%), which was also corroborated by the Sloan neutral community model analysis. Further, microbial assembly of three habitats appeared to be largely driven by stochastic processes (over 78%) in the SCPE. Conclusions Our results demonstrate that those core microbial taxa of three habitats were distinct with each other, that sediment communities mainly contributed to shrimp intestine communities, and that the microbial assembly was largely driven by stochastic processes. Our study enhances the mechanistic understanding of the microbial diversity and assembly in the SCPE for further analyzing metacommunities, and has important implications for microbial ecology and shrimp culture strategies.

Although long Nanopore reads are advantageous in de novo genome assembly, applying Nanopore reads in genomic studies is still hindered by their complex errors. Here, we developed NECAT, an error correction and de novo assembly tool designed to overcome complex errors in Nanopore reads. We proposed an adaptive read selection and two-step progressive method to quickly correct Nanopore reads to high accuracy. We introduced a two-stage assembler to utilize the full length of Nanopore reads. NECAT achieves superior performance in both error correction and de novo assembly of Nanopore reads. NECAT requires only 7,225 CPU hours to assemble a 35X coverage human genome and achieves a 2.28-fold improvement in NG50. Furthermore, our assembly of the human WERI cell line showed an NG50 of 29 Mbp. The high-quality assembly of Nanopore reads can significantly reduce false positives in structure variation detection.

N6-methyladenine (6mA) DNA modification has been detected in several eukaryotic organisms, where it plays important roles in gene regulation and epigenetic memory maintenance. However, the genome-wide distribution patterns and potential functions of 6mA DNA modification in woodland strawberry (Fragaria vesca) remain largely unknown. Here, we examined the 6mA landscape in the F. vesca genome by adopting single-molecule real-time sequencing technology and found that 6mA modification sites were broadly distributed across the woodland strawberry genome. The pattern of 6mA distribution in the long non-coding RNA was significantly different from that in protein-coding genes. The 6mA modification influenced the gene transcription and was positively associated with gene expression, which was validated by computational and experimental analyses. Our study provides new insights into the DNA methylation in F. vesca.

COVID-19 is caused by a newly identified coronavirus, SARS-CoV-2, and has become a pandemic around the world. The illustration of the immune responses against SARS-CoV-2 is urgently needed for understanding the pathogenesis of the disease and its vaccine development. CD8+ T cells are critical for virus clearance and induce long lasting protection in the host. Here we identified specific HLA-A2 restricted T cell epitopes in the spike protein of SARS-CoV-2. Seven epitope peptides were confirmed to bind with HLA-A2 and potentially be presented by antigen presenting cells to induce host immune responses. Tetramers containing these peptides could interact with specific CD8+ T cells from convalescent COVID-19 patients, and three dominant epitopes were defined. Furthermore, those epitopes could activate and generate epitope-specific T cells in vitro . All these epitopes exhibited high frequency of variations. The epitope variations not only significantly reduced their binding to the HLA-A2 but also decrease the proportion of specific T cell activation, which might contribute to the immune escape of SAR-CoV-2. Funding: This work was supported by grants from the National Key Research and Development Program of China (2018YFC2002003), the Natural Science Foundation of China (U1801285, 81971301), Guangzhou Planned Project of Science and Technology (201904010111, 202002020039), Zhuhai Planned Project of Science and Technology (ZH22036302200067PWC) and the Initial Supporting Foundation of Jinan University. Conflict of Interest: The epitopes and tetramers from this study are the subject of a patent application. No other conflicts exist. Ethical Approval: The Institutional Review Board of the Affiliated Huaqiao Hospital of Jinan University approved this study.

Background DNA methylation is an important epigenetic modification. Recently the developed single-molecule real-time (SMRT) sequencing technology provided an efficient way to detect DNA N6-methyladenine (6mA) modification that played an important role in epigenetic and positively regulated gene expression. In addition, the gene expression was also regulated by genetic variation. However, the relationship between DNA 6mA modification and variation is still unknown. Results We collected the SMRT long-reads DNA, Illumina short reads DNA and RNA datasets from the young leaves of Herrania umbratica, and used them to detect 35,654 6mA modification sites, 829,894 DNA variations and 60,672 RNA variations respectively, among which, there are 303 DNA variations and 19 RNA variations with 6mA modification, and 57,468 transmitted genetic variations from DNA to RNA. The results illustrated that the genes with 6mA modification were significant disadvantage to mutate than those genes without modification (p-value

DNA base modifications, such as C5-methylcytosine (5mC) and N6-methyldeoxyadenosine (6mA), are important types of epigenetic regulations. Short-read bisulfite sequencing and long-read PacBio sequencing have inherent limitations to detect DNA modifications. Here, using raw electric signals of Oxford Nanopore long-read sequencing data, we design DeepMod, a bidirectional recurrent neural network (RNN) with long short-term memory (LSTM) to detect DNA modifications. We sequence a human genome HX1 and a Chlamydomonas reinhardtii genome using Nanopore sequencing, and then evaluate DeepMod on three types of genomes (Escherichia coli, Chlamydomonas reinhardtii and human genomes). For 5mC detection, DeepMod achieves average precision up to 0.99 for both synthetically introduced and naturally occurring modifications. For 6mA detection, DeepMod achieves ~0.9 average precision on Escherichia coli data, and have improved performance than existing methods on Chlamydomonas reinhardtii data. In conclusion, DeepMod performs well for genome-scale detection of DNA modifications and will facilitate epigenetic analysis on diverse species., DNA modification generates unique electric signals in Oxford Nanopore sequencing data but the signals can be complicated to decipher. Here, the authors develop a deep learning framework, DeepMod, to detect DNA base modifications including 5mC and 6mA using Nanopore sequencing data

Figure S1. The 6mA density of genes with and without variation. Figure S2. The ratio of transmitted variants in intergenic regions with 6mA and without 6mA modification. Figure S3. The gene expression in different tissues of H. umbratica. (DOCX 369 kb)

We present a tool that combines fast mapping, error correction, and de novo assembly (MECAT; accessible at https://github.com/xiaochuanle/MECAT) for processing single-molecule sequencing (SMS) reads. MECAT's computing efficiency is superior to that of current tools, while the results MECAT produces are comparable or improved. MECAT enables reference mapping or de novo assembly of large genomes using SMS reads on a single computer.

DNA N6-methyladenine (6mA) modifications expand the information capacity of DNA and have long been known to exist in bacterial genomes. Xanthomonas oryzae pv. Oryzicola (Xoc) is the causative agent of bacterial leaf streak, an emerging and destructive disease in rice worldwide. However, the genome-wide distribution patterns and potential functions of 6mA in Xoc are largely unknown. In this study, we analyzed the levels and global distribution patterns of 6mA modification in genomic DNA of seven Xoc strains (BLS256, BLS279, CFBP2286, CFBP7331, CFBP7341, L8 and RS105). The 6mA modification was found to be widely distributed across the seven Xoc genomes, accounting for percent of 3.80, 3.10, 3.70, 4.20, 3.40, 2.10, and 3.10 of the total adenines in BLS256, BLS279, CFBP2286, CFBP7331, CFBP7341, L8, and RS105, respectively. Notably, more than 82% of 6mA sites were located within gene bodies in all seven strains. Two specific motifs for 6 mA modification, ARGT and AVCG, were prevalent in all seven strains. Comparison of putative DNA methylation motifs from the seven strains reveals that Xoc have a specific DNA methylation system. Furthermore, the 6 mA modification of rpfC dramatically decreased during Xoc infection indicates the important role for Xoc adaption to environment.

The accurate landscape of transcript isoforms plays an important role in the understanding of gene function and gene regulation. However, building complete transcripts is very challenging for short reads generated using next-generation sequencing. Fortunately, isoform sequencing (Iso-Seq) using single-molecule sequencing technologies, such as PacBio SMRT, provides long reads spanning entire transcript isoforms which do not require assembly. Therefore, we have developed ISOdb, a comprehensive resource database for hosting and carrying out an in-depth analysis of Iso-Seq datasets and visualising the full-length transcript isoforms. The current version of ISOdb has collected 93 publicly available Iso-Seq samples from eight species and presents the samples in two levels: (1) sample level, including metainformation, long read distribution, isoform numbers, and alternative splicing (AS) events of each sample; (2) gene level, including the total isoforms, novel isoform number, novel AS number, and isoform visualisation of each gene. In addition, ISOdb provides a user interface in the website for uploading sample information to facilitate the collection and analysis of researchers’ datasets. Currently, ISOdb is the first repository that offers comprehensive resources and convenient public access for hosting, analysing, and visualising Iso-Seq data, which is freely available.

DNA N

In human cells, DNA 5-methylcytosine (5mC) modification plays an important role as an epigenetic mark. However, DNA N6-methyladenine modification (6mA), which is predominantly present in prokaryotes and a limited number of eukaryotes, is considered to be absent in human genomic DNA. Here, we show that 6mA is present in human genome, and we identified 881,240 6mA sites whose density is about 0.051% of the total adenines in the human genome DNA, but more than 0.18% in the mitochondrion genome. [G/C]AGG[C/T] was the most significant motif associated with 6mA modification. 6mA sites are enriched in the exon coding regions (P=0.02) and associated with transcriptional activation (P

Low-input RNA-seq is powerful to represent the gene expression profiles with limited number of cells, especially when single-cell variations are not the aim. However, pre-amplification-based and molecule index-based library construction methods boost bias or require higher throughput. Here we demonstrate a simple, low-cost, low-bias and low-input RNA-seq with ion torrent semiconductor sequencing (LIEA RNA-seq). We also developed highly accurate and error-tolerant spliced mapping algorithm FANSe2splice to accurately map the single-ended reads to the reference genome with better experimental verifiability than the previous spliced mappers. Combining the experimental and computational advancements, our solution is comparable with the bulk mRNA-seq in quantification, reliably detects splice junctions and minimizes the bias with much less mappable reads.