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Sensitive detection of Mycobacterium tuberculosis (TB) in small percentages in metagenomic samples is essential for microbial classification and drug resistance prediction. However, traditional methods, such as bacterial culture and microscopy, are time-consuming and sometimes have limited TB detection sensitivity. Oxford nanopore technologies (ONT) MinION sequencing allows rapid and simple sample preparation for sequencing. Its recently developed adaptive sequencing selects reads from targets while allowing real-time base-calling to achieve sequence enrichment or depletion during sequencing. Another common enrichment method is PCR amplification of the target TB genes. In this study, we compared both methods using ONT MinION sequencing for TB detection and variant calling in metagenomic samples using both simulation runs and those with synthetic and patient samples. We found that both methods effectively enrich TB reads from a high percentage of human (95%) and other microbial DNA. Adaptive sequencing with readfish and UNCALLDE achieved a 3.9-fold and 2.2-fold enrichment compared to the control run. We provide a simple automatic analysis framework to support the detection of TB for clinical use, openly available at https://github.com/HKU-BAL/ONT-TB-NF. Depending on the patient's medical condition and sample type, we recommend users evaluate and optimize their workflow for different clinical specimens to improve the detection limit.

Background Very low-coverage (0.1 to 1×) whole genome sequencing (WGS) has become a promising and affordable approach to discover genomic variants of human populations for genome-wide association study (GWAS). To support genetic screening using preimplantation genetic testing (PGT) in a large population, the sequencing coverage goes below 0.1× to an ultra-low level. However, the feasibility and effectiveness of ultra-low-coverage WGS (ulcWGS) for GWAS remains undetermined. Methods We built a pipeline to carry out analysis of ulcWGS data for GWAS. To examine its effectiveness, we benchmarked the accuracy of genotype imputation at the combination of different coverages below 0.1× and sample sizes from 2000 to 16,000, using 17,844 embryo PGT samples with approximately 0.04× average coverage and the standard Chinese sample HG005 with known genotypes. We then applied the imputed genotypes of 1744 transferred embryos who have gestational ages and complete follow-up records to GWAS. Results The accuracy of genotype imputation under ultra-low coverage can be improved by increasing the sample size and applying a set of filters. From 1744 born embryos, we identified 11 genomic risk loci associated with gestational ages and 166 genes mapped to these loci according to positional, expression quantitative trait locus, and chromatin interaction strategies. Among these mapped genes, CRHBP, ICAM1, and OXTR were more frequently reported as preterm birth related. By joint analysis of gene expression data from previous studies, we constructed interrelationships of mainly CRHBP, ICAM1, PLAGL1, DNMT1, CNTLN, DKK1, and EGR2 with preterm birth, infant disease, and breast cancer. Conclusions This study not only demonstrates that ulcWGS could achieve relatively high accuracy of adequate genotype imputation and is capable of GWAS, but also provides insights into the associations between gestational age and genetic variations of the fetal embryos from Chinese population.

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

Sensitive detection ofMycobacterium Tuberculosis(TB) in small percentages in metagenomic samples is essential for microbial classification and drug resistance prediction, and assisting in diagnosis and treatment planning. However, traditional methods, such as bacterial culture and microscopy, are time-consuming and sometimes have limited TB detection sensitivity. Oxford Nanopore Technologies’ MinION sequencing allows rapid and simple sample preparation for whole genome and amplicon sequencing. Its recently developed adaptive sequencing selects reads from targets, while allowing real-time base-calling during sequencing to achieve sequence enrichment or depletion. Another common enrichment method is PCR amplification of the target TB genes. In this study, we compared both methods with MinION sequencing for TB detection and variant calling in metagenomic samples using both simulation runs and those with synthetic and patient samples. We found that both methods effectively enrich TB reads from a high percentage of human and other microbial DNA. We provide a simple automatic analysis framework, including quality filtering, taxonomic classification, variant calling, and antimicrobial resistance prediction, to support these detection workflows for clinical use, openly available athttps://github.com/HKU-BAL/ONT-TB-NF. Depending on the patient’s medical condition and sample type (commonly including bronchoalveolar lavage fluid, blood samples, sputum, and tissues), we discuss the findings and recommend that users optimize their workflow to improve the detection limit.

Background Whole genome sequencing using the long-read Oxford Nanopore Technologies (ONT) MinION sequencer provides a cost-effective option for structural variant (SV) detection in clinical applications. Despite the advantage of using long reads, however, accurate SV calling and phasing are still challenging. Results We introduce Duet, an SV detection tool optimized for SV calling and phasing using ONT data. The tool uses novel features integrated from both SV signatures and single-nucleotide polymorphism signatures, which can accurately distinguish SV haplotype from a false signal. Duet was benchmarked against state-of-the-art tools on multiple ONT sequencing datasets of sequencing coverage ranging from 8× to 40×. At low sequencing coverage of 8×, Duet performs better than all other tools in SV calling, SV genotyping and SV phasing. When the sequencing coverage is higher (20× to 40×), the F1-score for SV phasing is further improved in comparison to the performance of other tools, while its performance of SV genotyping and SV calling remains higher than other tools. Conclusion Duet can perform accurate SV calling, SV genotyping and SV phasing using low-coverage ONT data, making it very useful for low-coverage genomes. It has great performance when scaled to high-coverage genomes, which is adaptable to various clinical applications. Duet is open source and is available at https://github.com/yekaizhou/duet.

We reported the discovery of a novel Streptococcus species, propose to be named Streptococcus oriscaviae , from the pus collected from a guinea pig bite wound in a healthy young patient. The bacterium was initially misidentified as S. suis / S. parasuis by biochemical tests, mass spectrometry. and housekeeping genes sequencing.

Single-molecule localization microscopy is a powerful superresolution imaging technique to study biological questions by visualizing subcellular fine structures with nanometer-scale precision. However, its application in live-cell imaging studies has been impeded by the paucity of self-blinking organic fluorophores that enable high spatiotemporal resolution and labeling/localization density at a moderate laser intensity. Herein, we report a self-blinking Si-rhodamine dye6-HESiRwith a suitably increased “ON” fraction and a fluorogenic self-blinking dsDNA probe6-HoeHESiRas a powerful tool for 3D superresolution imaging of native chromatin in eukaryotes without the use of photoswitching buffer and high laser intensity. With the probe6-HoeHESiR, 3D superresolution imaging of in vitro reconstituted nucleosomal arrays and chromatin fibers yielded results consistent with EM analysis. Similar euchromatin and heterochromatin structures were visualized in fixed and live cells with high spatiotemporal resolution and labeling density, providing the first live-cell evidence for a hierarchical model of chromatin organization. 3D imaging results obtained in the presence of selective inhibitors of histone deacetylases also corroborate chromatin fiber decompaction upon hyperacetylation of histones.

BackgroundVery low coverage (0.1 to 1x) whole genome sequencing (WGS) has become a promising and affordable approach to discover genomic variants of human populations for Genome-Wide Association Study (GWAS). To support genetic screening using Preimplantation Genetic Testing (PGT) in a large population, the sequencing coverage goes below 0.1x to an ultra-low level. However, its feasibility and effectiveness for GWAS remains undetermined.MethodsWe devised a pipeline to process ultra-low coverage WGS data and benchmarked the accuracy of genotype imputation at the combination of different coverages below 0.1x and sample sizes from 2,000 to 16,000, using 17,844 embryo PGT with approximately 0.04x average coverage and the standard Chinese sample HG005 with known genotypes. We then applied the imputed genotypes of 1,744 transferred embryos who have gestational ages and complete follow-up records to GWAS.ResultsThe accuracy of genotype imputation under ultra-low coverage can be improved by increasing the sample size and applying a set of filters. From 1,744 born embryos, we identified 11 genomic risk loci associated with gestational ages and 166 genes mapped to these loci according to positional, expression quantitative trait locus and chromatin interaction strategies. Among these mapped genes,CRHBP, ICAM1andOXTRwere more frequently reported as preterm birth related. By joint analysis of gene expression data from previous studies, we constructed interrelationships of mainlyCRHBP, ICAM1, PLAGL1, DNMT1, CNTLN, DKK1andEGR2with preterm birth, infant disease and breast cancer.ConclusionsThis study not only demonstrates that ultra-low coverage WGS could achieve relatively high accuracy of adequate genotype imputation and is capable of GWAS, but also provides insights into uncovering genetic associations of gestational age trait existed in the fetal embryo samples from Chinese or Eastern Asian populations.

Pet bite-related infections are commonly caused by the pet's oral flora transmitted to the animal handlers through the bite wounds. In this study, we isolated a streptococcus, HKU75

Accurate identification of genetic variants from family child-mother-father trio sequencing data is important in genomics. However, state-of-the-art approaches treat variant calling from trios as three independent tasks, which limits their calling accuracy for Nanopore long-read sequencing data. For better trio variant calling, we introduce Clair3-Trio, the first variant caller tailored for family trio data from Nanopore long-reads. Clair3-Trio employs a Trio-to-Trio deep neural network model, which allows it to input the trio sequencing information and output all of the trio’s predicted variants within a single model to improve variant calling. We also present MCVLoss, a novel loss function tailor-made for variant calling in trios, leveraging the explicit encoding of the Mendelian inheritance. Clair3-Trio showed comprehensive improvement in experiments. It predicted far fewer Mendelian inheritance violation variations than current state-of-the-art methods. We also demonstrated that our Trio-to-Trio model is more accurate than competing architectures. Clair3-Trio is accessible as a free, open-source project at https://github.com/HKU-BAL/Clair3-Trio.

Summary The global propagation of SARS‐CoV‐2 and the detection of a large number of variants, some of which have replaced the original clade to become dominant, underscores the fact that the virus is actively exploring its evolutionary space. The longer high levels of viral multiplication occur – permitted by high levels of transmission –, the more the virus can adapt to the human host and find ways to success. The third wave of the COVID‐19 pandemic is starting in different parts of the world, emphasizing that transmission containment measures that are being imposed are not adequate. Part of the consideration in determining containment measures is the rationale that vaccination will soon stop transmission and allow a return to normality. However, vaccines themselves represent a selection pressure for evolution of vaccine‐resistant variants, so the coupling of a policy of permitting high levels of transmission/virus multiplication during vaccine roll‐out with the expectation that vaccines will deal with the pandemic, is unrealistic. In the absence of effective antivirals, it is not improbable that SARS‐CoV‐2 infection prophylaxis will involve an annual vaccination campaign against ‘dominant’ viral variants, similar to influenza prophylaxis. Living with COVID‐19 will be an issue of SARS‐CoV‐2 variants and evolution. It is therefore crucial to understand how SARS‐CoV‐2 evolves and what constrains its evolution, in order to anticipate the variants that will emerge. Thus far, the focus has been on the receptor‐binding spike protein, but the virus is complex, encoding 26 proteins which interact with a large number of host factors, so the possibilities for evolution are manifold and not predictable a priori. However, if we are to mount the best defence against COVID‐19, we must mount it against the variants, and to do this, we must have knowledge about the evolutionary possibilities of the virus. In addition to the generic cellular interactions of the virus, there are extensive polymorphisms in humans (e.g. Lewis, HLA, etc.), some distributed within most or all populations, some restricted to specific ethnic populations and these variations pose additional opportunities for/constraints on viral evolution. We now have the wherewithal – viral genome sequencing, protein structure determination/modelling, protein interaction analysis – to functionally characterize viral variants, but access to comprehensive genome data is extremely uneven. Yet, to develop an understanding of the impacts of such evolution on transmission and disease, we must link it to transmission (viral epidemiology) and disease data (patient clinical data), and the population granularities of these. In this editorial, we explore key facets of viral biology and the influence of relevant aspects of human polymorphisms, human behaviour, geography and climate and, based on this, derive a series of recommendations to monitor viral evolution and predict the types of variants that are likely to arise.

Background Children and older adults with coronavirus disease 2019 (COVID-19) display a distinct spectrum of disease severity yet the risk factors aren’t well understood. We sought to examine the expression pattern of angiotensin-converting enzyme 2 (ACE2), the cell-entry receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the role of lung progenitor cells in children and older patients. Methods We retrospectively analyzed clinical features in a cohort of 299 patients with COVID-19. The expression and distribution of ACE2 and lung progenitor cells were systematically examined using a combination of public single-cell RNA-seq data sets, lung biopsies, and ex vivo infection of lung tissues with SARS-CoV-2 pseudovirus in children and older adults. We also followed up patients who had recovered from COVID-19. Results Compared with children, older patients (>50 years.) were more likely to develop into serious pneumonia with reduced lymphocytes and aberrant inflammatory response (P = .001). The expression level of ACE2 and lung progenitor cell markers were generally decreased in older patients. Notably, ACE2 positive cells were mainly distributed in the alveolar region, including SFTPC positive cells, but rarely in airway regions in the older adults (P < .01). The follow-up of discharged patients revealed a prolonged recovery from pneumonia in the older (P < .025). Conclusions Compared to children, ACE2 positive cells are generally decreased in older adults and mainly presented in the lower pulmonary tract. The lung progenitor cells are also decreased. These risk factors may impact disease severity and recovery from pneumonia caused by SARS-Cov-2 infection in older patients.

Introduction: An increasing number of children with severe coronavirus disease 2019 (COVID-19) is being reported, yet the spectrum of disease severity and expression patterns of angiotensin-converting enzyme 2 (ACE2) in children at different developmental stages are largely unknow. Methods: We analysed clinical features in a cohort of 173 children with COVID-19 (0-15 yrs.-old) between January 22, 2020 and March 15, 2020. We systematically examined the expression and distribution of ACE2 in different developmental stages of children by using a combination of children's lung biopsies, pluripotent stem cell-derived lung cells, RNA-sequencing profiles, and ex vivo SARS-CoV-2 pseudoviral infections. Results: It revealed that infants (< 1yrs.-old), with a weaker potency of immune response, are more vulnerable to develop pneumonia whereas older children (> 1 yrs.-old) are more resistant to lung injury. The expression levels of ACE2 however do not vary by age in children's lung. ACE2 is notably expressed not only in Alveolar Type II (AT II) cells, but also in SOX9 positive lung progenitor cells detected in both pluripotent stem cell derivatives and infants' lungs. The ACE2+SOX9+ cells are readily infected by SARS-CoV-2 pseudovirus and the numbers of the double positive cells are significantly decreased in older children. Conclusions: Infants (< 1 yrs.-old) with SARS-CoV-2 infection are more vulnerable to lung injuries. ACE2 expression in multiple types of lung cells including SOX9 positive progenitor cells, in cooperation with an unestablished immune system, could be risk factors contributing to vulnerability of infants with COVID-19. There is a need to continue monitoring lung development in young children who have recovered from SARS-CoV-2 infection.

Background Next-generation sequencing (NGS) enables unbiased detection of pathogens by mapping the sequencing reads of a patient sample to the known reference sequence of bacteria and viruses. However, for a new pathogen without a reference sequence of a close relative, or with a high load of mutations compared to its predecessors, read mapping fails due to a low similarity between the pathogen and reference sequence, which in turn leads to insensitive and inaccurate pathogen detection outcomes. Results We developed MegaPath, which runs fast and provides high sensitivity in detecting new pathogens. In MegaPath, we have implemented and tested a combination of polishing techniques to remove non-informative human reads and spurious alignments. MegaPath applies a global optimization to the read alignments and reassigns the reads incorrectly aligned to multiple species to a unique species. The reassignment not only significantly increased the number of reads aligned to distant pathogens, but also significantly reduced incorrect alignments. MegaPath implements an enhanced maximum-exact-match prefix seeding strategy and a SIMD-accelerated Smith-Waterman algorithm to run fast. Conclusions In our benchmarks, MegaPath demonstrated superior sensitivity by detecting eight times more reads from a low-similarity pathogen than other tools. Meanwhile, MegaPath ran much faster than the other state-of-the-art alignment-based pathogen detection tools (and compariable with the less sensitivity profile-based pathogen detection tools). The running time of MegaPath is about 20 min on a typical 1 Gb dataset.

Structural variation (SV) is a major cause of genetic disorders. In this paper, we show that low-depth (specifically, 4×) whole-genome sequencing using a single Oxford Nanopore MinION flow cell suffices to support sensitive detection of SV, particularly pathogenic SV for supporting clinical diagnosis. When using 4× ONT WGS data, existing SV calling software often fails to detect pathogenic SV, especially in the form of long deletion, terminal deletion, duplication, and unbalanced translocation. Our new SV calling software SENSV can achieve high sensitivity for all types of SV and a breakpoint precision typically ± 100 bp; both features are important for clinical concerns. The improvement achieved by SENSV stems from several new algorithms. We evaluated SENSV and other software using both real and simulated data. The former was based on 24 patient samples, each diagnosed with a genetic disorder. SENSV found the pathogenic SV in 22 out of 24 cases (all heterozygous, size from hundreds of kbp to a few Mbp), reporting breakpoints within 100 bp of the true answers. On the other hand, no existing software can detect the pathogenic SV in more than 10 out of 24 cases, even when the breakpoint requirement is relaxed to ± 2000 bp.

PURPOSE Mitogen-activated protein kinase pathway–activating mutations occur in the majority of colorectal cancer (CRC) cases and show mutual exclusivity. We identified 47 epidermal growth factor receptor/BRAF inhibitor-naive CRC patients with dual RAS hotspot/ BRAF V600E mutations (CRC-DD) from a cohort of 4,561 CRC patients with clinical next-generation sequencing results. We aimed to define the molecular phenotypes of the CRC-DD and to test if the dual RAS hotspot/BRAF V600E mutations coexist within the same cell. MATERIALS AND METHODS We developed a single-cell genotyping method with a mutation detection rate of 96.3% and a genotype prediction accuracy of 92.1%. Mutations in the CRC-DD cohort were analyzed for clonality, allelic imbalance, copy number, and overall survival. RESULTS Application of single-cell genotyping to four CRC-DD revealed the co-occurrence of both mutations in the following percentages of cells per case: NRAS G13D/ KRAS G12C, 95%; KRAS G12D/ NRAS G12V, 48%; BRAF V600E/ KRAS G12D, 44%; and KRAS G12D/ NRAS G13V, 14%, respectively. Allelic imbalance favoring the oncogenic allele was less frequent in CRC-DD (24 of 76, 31.5%, somatic mutations) compared with a curated cohort of CRC with a single-driver mutation (CRC-SD; 119 of 232 mutations, 51.3%; P = .013). Microsatellite instability–high status was enriched in CRC-DD compared with CRC-SD (23% v 11.4%, P = .028). Of the seven CRC-DD cases with multiregional sequencing, five retained both driver mutations throughout all sequenced tumor sites. Both CRC-DD cases with discordant multiregional sequencing were microsatellite instability–high. CONCLUSION Our findings indicate that dual-driver mutations occur in a rare subset of CRC, often within the same tumor cells and across multiple tumor sites. Their presence and a lower rate of allelic imbalance may be related to dose-dependent signaling within the mitogen-activated protein kinase pathway.

Single-molecule sequencing technologies have emerged in recent years and revolutionized structural variant calling, complex genome assembly and epigenetic mark detection. However, the lack of a highly accurate small variant caller has limited these technologies from being more widely used. Here, we present Clair, the successor to Clairvoyante, a program for fast and accurate germline small variant calling, using single-molecule sequencing data. For Oxford Nanopore Technology data, Clair achieves better precision, recall and speed than several competing programs, including Clairvoyante, Longshot and Medaka. Through studying the missed variants and benchmarking intentionally overfitted models, we found that Clair may be approaching the limit of possible accuracy for germline small variant calling using pileup data and deep neural networks. Clair requires only a conventional central processing unit (CPU) for variant calling and is an open-source project available at https://github.com/HKU-BAL/Clair. A lack of accurate and efficient variant calling methods has held back single-molecule sequencing technologies from clinical applications. The authors present a deep-learning method for fast and accurate germline small variant calling, using single-molecule sequencing data.

HKG is the first fully accessible variant database for Hong Kong Cantonese, constructed from 205 novel whole-exome sequencing data. There has long been a research gap in the understanding of the genetic architecture of southern Chinese subgroups, including Hong Kong Cantonese. HKG detected 196 325 high-quality variants with 5.93% being novel, and 25 472 variants were found to be unique in HKG compared to three Chinese populations sampled from 1000 Genomes (CHN). PCA illustrates the uniqueness of HKG in CHN, and the admixture study estimated the ancestral composition of HKG and CHN, with a gradient change from north to south, consistent with their geological distribution. ClinVar, CIViC and PharmGKB annotated 599 clinically significant variants and 360 putative loss-of-function variants, substantiating our understanding of population characteristics for future medical development. Among the novel variants, 96.57% were singleton and 6.85% were of high impact. With a good representation of Hong Kong Cantonese, we demonstrated better variant imputation using reference with the addition of HKG data, thus successfully filling the data gap in southern Chinese to facilitate the regional and global development of population genetics.

Deep learning-based variant callers are becoming the standard and have achieved superior SNP calling performance using long reads. In this paper, we present Clair3, which makes the best of two major method categories: pile-up calling handles most variant candidates with speed, and full-alignment tackles complicated candidates to maximize precision and recall. Clair3 ran faster than any of the other state-of-the-art variant callers and performed the best, especially at lower coverage.

Pre-training lays the foundation for recent successes in radiograph analysis supported by deep learning. It learns transferable image representations by conducting large-scale fully-supervised or self-supervised learning on a source domain. However, supervised pre-training requires a complex and labor intensive two-stage human-assisted annotation process while self-supervised learning cannot compete with the supervised paradigm. To tackle these issues, we propose a cross-supervised methodology named REviewing FreE-text Reports for Supervision (REFERS), which acquires free supervision signals from original radiology reports accompanying the radiographs. The proposed approach employs a vision transformer and is designed to learn joint representations from multiple views within every patient study. REFERS outperforms its transfer learning and self-supervised learning counterparts on 4 well-known X-ray datasets under extremely limited supervision. Moreover, REFERS even surpasses methods based on a source domain of radiographs with human-assisted structured labels. Thus REFERS has the potential to replace canonical pre-training methodologies., Accepted by Nature Machine Intelligence. The official version is at https://www.nature.com/articles/s42256-021-00425-9. Codes are available at https://github.com/funnyzhou/REFERS

Pan-genome sequence analysis of human population ancestry is critical for expanding and better defining human genome sequence diversity. However, the amount of genetic variation still missing from current human reference sequences is still unknown. Here, we used 486 deep-sequenced Han Chinese genomes to identify 276 Mbp of DNA sequences that, to our knowledge, are absent in the current human reference. We classified these sequences into individual-specific and common sequences, and propose that the common sequence size is uncapped with a growing population. The 46.646 Mbp common sequences obtained from the 486 individuals improved the accuracy of variant calling and mapping rate when added to the reference genome. We also analyzed the genomic positions of these common sequences and found that they came from genomic regions characterized by high mutation rate and low pathogenicity. Our study authenticates the Chinese pan-genome as representative of DNA sequences specific to the Han Chinese population missing from the GRCh38 reference genome and establishes the newly defined common sequences as candidates to supplement the current human reference., Qiuhui Li et al. report a pan-genome sequence study in Han Chinese genomes. They find 276 Mbp of DNA sequences (absent in the current human reference), and report that 46.646 Mbp common sequences improved the accuracy of variant calling and mapping rate when added to the reference genome.

Biomedical question answering (QA) is playing an increasingly significant role in medical knowledge translation. However, current biomedical QA datasets and methods have limited capacity, as they commonly neglect the role of numerical facts in biomedical QA. In this paper, we constructed BioNumQA, a novel biomedical QA dataset that answers research questions using relevant numerical facts for biomedical QA model training and testing. To leverage the new dataset, we designed a new method called BioNumQA-BERT by introducing a novel numerical encoding scheme into the popular biomedical language model BioBERT to represent the numerical values in the input text. Our experiments show that BioNumQA-BERT significantly outperformed other state-of-art models, including DrQA, BERT and BioBERT (39.0% vs 29.5%, 31.3% and 33.2%, respectively, in strict accuracy). To improve the generalization ability of BioNumQA-BERT, we further pretrained it on a large biomedical text corpus and achieved 41.5% strict accuracy. BioNumQA and BioNumQA-BERT establish a new baseline for biomedical QA. The dataset, source codes and pretrained model of BioNumQA-BERT are available at https://github.com/LeaveYeah/BioNumQA-BERT.

Summary The Human Genome Project opened an era of (epi)genomic research, and also provided a platform for the development of new sequencing technologies. During and after the project, several sequencing technologies continue to dominate nucleic acid sequencing markets. Currently, Illumina (short-read), PacBio (long-read), and Oxford Nanopore (long-read) are the most popular sequencing technologies. Unlike PacBio or the popular short-read sequencers before it, which, as examples of the second or so-called Next-Generation Sequencing platforms, need to synthesize when sequencing, nanopore technology directly sequences native DNA and RNA molecules. Nanopore sequencing, therefore, avoids converting mRNA into cDNA molecules, which not only allows for the sequencing of extremely long native DNA and full-length RNA molecules but also document modifications that have been made to those native DNA or RNA bases. In this review on direct DNA sequencing and direct RNA sequencing using Oxford Nanopore technology, we focus on their development and application achievements, discussing their challenges and future perspective. We also address the problems researchers may encounter applying these approaches in their research topics, and how to resolve them., Graphical abstract, Public summary • Nanopore-seq can dissect native DNA/RNA molecules from any organisms at unlimited length • A wide variety of algorithms greatly increase the accuracy of signal decoding in Nanopore-Seq • Nanopore-Seq significantly facilitates genome assembly and structural variant calling, and can simultaneously detect base modifications • These advantages ensure its great potentials in future medical and agricultural practices

BackgroundRelation extraction is a fundamental task for extracting gene-disease associations from biomedical text. Existing tools have limited capacity, as they can extract gene-disease associations only from single sentences or abstract texts.ResultsIn this work, we propose RENET2, a deep learning-based relation extraction method, which implements section filtering and ambiguous relations modeling to extract gene-disease associations from full-text articles. We designed a novel iterative training data expansion strategy to build an annotated full-text dataset to resolve the scarcity of labels on full-text articles. In our experiments, RENET2 achieved an F1-score of 72.13% for extracting gene-disease associations from an annotated full-text dataset, which was 27.22%, 30.30% and 29.24% higher than the best existing tools BeFree, DTMiner and BioBERT, respectively. We applied RENET2 to (1) ~1.89M full-text articles from PMC and found ~3.72M gene-disease associations; and (2) the LitCovid articles set and ranked the top 15 proteins associated with COVID-19, supported by recent articles.ConclusionRENET2 is an efficient and accurate method for full-text gene-disease association extraction. The source-code, manually curated abstract/full-text training data, and results of RENET2 are available at https://github.com/sujunhao/RENET2.

HKG is the first fully accessible variant database for Hong Kong Cantonese, constructed from 205 novel whole-exome sequencing data. There has long been a research gap in the understanding of the genetic architecture of southern Chinese subgroups, including Hong Kong Cantonese. HKG detected 196,325 high-quality variants with 5.93% being novel, and 25,472 variants were found to be unique in HKG compared to other Chinese populations (CHN). PCA illustrates the uniqueness of HKG in CHN, and IBD analysis revealed that it is related mostly to southern Chinese with a similar effective population size. An admixture study estimated the ancestral composition of HKG and CHN, with a gradient change from north to south, consistent with their geological distribution. ClinVar, CIViC and PharmGKB annotated 599 clinically significant variants and 360 putative loss-of-function variants, substantiating our understanding of population characteristics for future medical development. Among the novel variants, 96.57% were singleton and 6.85% were of high impact. With a good representation of Hong Kong Cantonese, we demonstrated better variant imputation using reference with the addition of HKG data, thus successfully filling the data gap in southern Chinese to facilitate the regional and global development of population genetics.

Inefficient differentiation and insufficient maturation are barriers to the application of human pluripotent stem cell (hPSC)-derived cardiomyocytes (CMs) for research and therapy. Great strides have been made to the former, and multiple groups have reported cardiac differentiation protocol that can generate hPSC-CMs at high efficiency. Although many such protocols are based on the modulation of the WNT signaling pathway, they differ in their timing and in the WNT inhibitors used. Little is currently known about whether and how conditions of differentiation affect cardiac maturation. Here we adapted multiple cardiac differentiation protocols to improve cost-effectiveness and consistency, and compared the properties of the hPSC-CMs generated. Our results showed that the schedule of differentiation, but not the choice of WNT inhibitors, was a critical determinant not only of differentiation efficiency, which was expected, but also CM maturation. Among cultures with comparable purity, hPSC-CMs generated with different differentiation schedules vary in the expression of genes which are important for developmental maturation, and in their structural, metabolic, calcium transient and proliferative properties. In summary, we demonstrated that simple changes in the schedule of cardiac differentiation could promote maturation. To this end, we have optimized a cardiac differentiation protocol that can simultaneously achieve high differentiation efficiency and enhanced developmental maturation. Our findings would advance the production of hPSC-CMs for research and therapy.

The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecule sequencing, which has a per-nucleotide error rate of ~5–15%. Meeting this demand, we developed Clairvoyante, a multi-task five-layer convolutional neural network model for predicting variant type (SNP or indel), zygosity, alternative allele and indel length from aligned reads. For the well-characterized NA12878 human sample, Clairvoyante achieves 99.67, 95.78, 90.53% F1-score on 1KP common variants, and 98.65, 92.57, 87.26% F1-score for whole-genome analysis, using Illumina, PacBio, and Oxford Nanopore data, respectively. Training on a second human sample shows Clairvoyante is sample agnostic and finds variants in less than 2 h on a standard server. Furthermore, we present 3,135 variants that are missed using Illumina but supported independently by both PacBio and Oxford Nanopore reads. Clairvoyante is available open-source (https://github.com/aquaskyline/Clairvoyante), with modules to train, utilize and visualize the model., Single Molecule Sequencing (SMS) technologies generate long but noisy reads data. Here, the authors develop Clairvoyante, a deep neural network-based method for variant calling with SMS reads such as PacBio and ONT data.

BackgroundDNA methylation may regulate pre-mRNA transcriptional initiation and processing, thus affecting gene expression. Unlike animal cells, plants, especially Arabidopsis thaliana, have relatively low DNA methylation levels, limiting our ability to observe any correlation between DNA methylation and pre-mRNA processing using typical short-read sequencing. However, with newly developed long-read sequencing technologies, such as Oxford Nanopore Technology Direct RNA sequencing (ONT DRS), combined with whole-genome bisulfite sequencing, we were able to precisely analyze the relationship between DNA methylation and pre-mRNA transcriptional initiation and processing using DNA methylation-related mutants.ResultsUsing ONT DRS, we generated more than 2 million high-quality full-length long reads of native mRNA for each of the wild type Col-0 and mutants defective in DNA methylation, identifying a total of 117,474 isoforms. We found that low DNA methylation levels around splicing sites tended to prevent splicing events from occurring. The lengths of the poly(A) tail of mRNAs were positively correlated with DNA methylation. DNA methylation before transcription start sites or around transcription termination sites tended to result in gene-silencing or read-through events.Furthermore, using ONT DRS, we identified novel transcripts that we could not have otherwise, since transcripts with intron retention and fusion transcripts containing the uncut intergenic sequence tend not to be exported to the cytoplasm. Using the met1-3 mutant with activated constitutive heterochromatin regions, we confirmed the effects of DNA methylation on pre-mRNA processing.ConclusionThe combination of ONT DRS with whole-genome bisulfite sequencing was a powerful tool for studying the effects of DNA methylation on splicing site selection and pre-mRNA processing, and therefore regulation of gene expression.

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of personal epigenomes, for ∼25 tissues and >10 assays in four donors (>1500 open-access functional genomic and proteomic datasets, in total). Each dataset is mapped to a matched, diploid personal genome, which has long-read phasing and structural variants. The mappings enable us to identify >1 million loci with allele-specific behavior. These loci exhibit coordinated epigenetic activity along haplotypes and less conservation than matched, non-allele-specific loci, in a fashion broadly paralleling tissue-specificity. Surprisingly, they can be accurately modelled just based on local nucleotide-sequence context. Combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci and enables models for transferring known eQTLs to difficult-to-profile tissues. Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

Structural variation (SV) is a major cause of genetic disorders. In this paper, we show that low-depth (specifically, 4x) whole-genome sequencing using a single Oxford Nanopore MinION flow cell suffices to support sensitive detection of SV, in particular, pathogenic SV for supporting clinical diagnosis. Existing SV calling software, when using 4x ONT WGS data, often fails to detect pathogenic SV especially in the form of long deletion, terminal deletion, duplication, and unbalanced translocation. Our new SV calling software SENSV is able to achieve high sensitivity for all types of SV and a breakpoint precision typically ±100 bp, both features are important for clinical concerns. The improvement achieved by SENSV stems from several new algorithms. We evaluated SENSV and other software using both real and simulated data. The former was based on 24 patient samples, each diagnosed with a genetic disorder. SENSV found the pathogenic SV in 22 out of 24 cases (all heterozygous, size from hundreds of Kbp to a few Mbp), reporting breakpoints within 100 bp of the true answers. No existing software can detect the pathogenic SV in more than 10 out of 24 cases, even when the breakpoint requirement is relaxed to ±2,000 bp.

BackgroundThe application of long-read sequencing using the Oxford Nanopore Technologies (ONT) MinION sequencer is getting more diverse in the medical field. Having a high sequencing error of ONT and limited throughput from a single MinION flowcell, however, limits its applicability for accurate variant detection. Medical exome sequencing (MES) targets clinically significant exon regions, allowing rapid and comprehensive screening of pathogenic variants. By applying MES with MinION sequencing, the technology can achieve a more uniform capture of the target regions, shorter turnaround time, and lower sequencing cost per sample.MethodWe introduced a cost-effective optimized workflow, ECNano, comprising a wet-lab protocol and bioinformatics analysis, for accurate variant detection at 4,800 clinically important genes and regions using a single MinION flowcell. The ECNano wet-lab protocol was optimized to perform long-read target enrichment and ONT library preparation to stably generate high-quality MES data with adequate coverage. The subsequent variant-calling workflow, Clair-ensemble, adopted a fast RNN-based variant caller, Clair, and was optimized for target enrichment data. To evaluate its performance and practicality, ECNano was tested on both reference DNA samples and patient samples.ResultsECNano achieved deep on-target depth of coverage (DoC) at average >100x and >98% uniformity using one MinION flowcell. For accurate ONT variant calling, the generated reads sufficiently covered 98.9% of pathogenic positions listed in ClinVar, with 98.96% having at least 30x DoC. ECNano obtained an average read length of 1,000 bp. The long reads of ECNano also covered the adjacent splice sites well, with 98.5% of positions having ≥ 30x DoC. Clair-ensemble achieved >99% recall and accuracy for SNV calling. The whole workflow from wet-lab protocol to variant detection was completed within three days.ConclusionWe presented ECNano, an out-of-the-box workflow comprising (1) a wet-lab protocol for ONT target enrichment sequencing and (2) a downstream variant detection workflow, Clair-ensemble. The workflow is cost-effective, with a short turnaround time for high accuracy variant calling in 4,800 clinically significant genes and regions using a single MinION flowcell. The long-read exon captured data has potential for further development, promoting the application of long-read sequencing in personalized disease treatment and risk prediction.

In this study, we reported the prevalence and mechanism associated with the extended-spectrum beta-lactamase (ESBL)-positive phenotype in Laribacter hongkongensis isolated from patients and fish. Using the inhibition zone enhancement test, 20 (95.2%) of the 21 patient strains and 8 (57.1%) of the 14 fish strains were tested ESBL-positive. However, ESBL genes, including SHV, TEM, CTX-M, GES, and PER, were not detected in all of these 28 L. hongkongensis isolates. No ESBL gene could be detected in either the complete genome of L. hongkongensis HLHK9 or the draft genome of PW3643. PCR and DNA sequencing revealed that all the 35 L. hongkongensis isolates (showing both ESBL-positive and ESBL-negative phenotypes) were positive for the ampC gene. When the AmpC deletion mutant, HLHK9ΔampC, was subject to the zone enhancement test, the difference of zone size between ceftazidime/clavulanate and ceftazidime was less than 5 mm. When boronic acid was added to the antibiotic disks, none of the 28 “ESBL-positive” isolates showed a ≥ 5 mm enhancement of inhibition zone size diameter between ceftazidime/clavulanate and ceftazidime and between cefotaxime/clavulanate and cefotaxime. A high prevalence (80%) of ESBL-positive phenotype is present in L. hongkongensis. Overall, our results suggested that the ESBL-positive phenotype in L. hongkongensis results from the expression of the intrinsic AmpC beta-lactamase. Confirmatory tests should be performed before issuing laboratory reports for L. hongkongensis isolates that are tested ESBL-positive by disk diffusion clavulanate inhibition test.

Karyotyping is the most commonly used genetic tool for diagnosing diseases associated with chromosomal abnormalities. It generates images of the chromosomes of a patient in which quantity or shape discrepancies against normal chromosomes might suggest chromosomal abnormalities. However, the current methods are cumbersome and require manual or half-automatic separation of overlapping chromosomes, significantly limiting the productivity of clinical geneticists and cytologists. In this project, we implemented a fully automatic method, called ChromSeg, which efficiently separates crossing-overlap chromosomes. It uses a new neural network architecture called “region-guided UNet++” to accurately detect crossing-overlap chromosomes from metaphase cell images. A new heuristic algorithm, called “crossing-partition”, is then applied to splice and reconstruct the crossing-overlap chromosomes into single chromosomes. While there are a very limited number of publicly accessible annotations on overlapping chromosomes, we manually annotated 345 images for our model training and performance testing. Benchmarking results showed that our method achieved 99.1% overlap detection on crossing-overlap chromosomes and outperformed the second best method by 3.1%. Notably, this is the first tool to provide an image of the reconstructed chromosomes; other tools provide only segmentation suggestions, which are of less value to end-users. The source code of ChromSeg is available at https://github.com/HKU-BAL/ChromSeg, and the 345 annotated images are available at http://www.bio8.cs.hku.hk/bibm/.

Accurate and sensitive taxonomic profiling is essential for any metagenomic analysis to reveal microbial community structure and for potential functional prediction. Antimicrobial resistance (AMR) detection is also a critical task in the clinical diagnosis of infection and antimicrobial therapy. By incorporating Oxford Nanopore Technologies (ONT) sequencing, users benefit from the high-confidence alignment of long reads for taxonomic classification, even among bacteria with similar genomes. Portable ONT devices, such as VolTRAX with MinION, allow short turnaround time for detection and can be used in a lightweight laboratory setting. However, error-prone ONT sequencing reads are still challenging for existing software for accurate taxonomic classification of microbes and detection of AMR down to the drug level. In this paper, we present MegaPath-Nano, the successor to NGS-based MegaPath. It is a high-precision compositional analysis software with drug-level AMR detection for ONT metagenomic sequencing data. MegaPath-Nano performs 1) thorough multi-level filtering against decoy and human reads while removing noisy alignments, 2) alignment-based taxonomic classification with RefSeq down to strain-level, with an alignment-reassignment algorithm to tackle the challenge of non-unique alignments, based on global alignment distribution, and 3) comprehensive downstream drug-level AMR detection, integrating five AMR databases. In our benchmarks using the Zymo metagenomic dataset, MegaPath-Nano performed better than other existing software for taxonomic classification. We also sequenced five real patient isolates using MinION to benchmark its performance of AMR detection. MegaPath-Nano was the most accurate and provided the most comprehensive output at both the drug and class level of AMR prediction against other state-of-the-art software. MegaPath-Nano is open-source and available at https://github.com/HKU-BAL/MegaPath-Nano.

Background Genome-centric approaches are widely used to investigate microbial compositions, dynamics, ecology, and interactions within various environmental systems. Hundreds or even thousands of genomes could be retrieved in a single study contributed by the cost-effective short-read sequencing and developed assembly/binning pipelines. However, conventional binning methods usually yield highly fragmented draft genomes that limit our ability to comprehensively understand these microbial communities. Thus, to leverage advantage of both the long and short reads to retrieve more complete genomes from environmental samples is a must-do task to move this direction forward. Results Here, we used an iterative hybrid assembly (IHA) approach to reconstruct 49 metagenome-assembled genomes (MAGs), including 27 high-quality (HQ) and high-contiguity (HC) genomes with contig number ≤ 5, eight of which were circular finished genomes from a partial-nitritation anammox (PNA) reactor. These 49 recovered MAGs (43 MAGs encoding full-length rRNA, average N50 of 2.2 Mbp), represented the majority (92.3%) of the bacterial community. Moreover, the workflow retrieved HQ and HC MAGs even with an extremely low coverage (relative abundance < 0.1%). Among them, 34 MAGs could not be assigned to the genus level, indicating the novelty of the genomes retrieved using the IHA method proposed in this study. Comparative analysis of HQ MAG pairs reconstructed using two methods, i.e., hybrid and short reads only, revealed that identical genes in the MAG pairs represented 87.5% and 95.5% of the total gene inventory of hybrid and short reads only assembled MAGs, respectively. In addition, the first finished anammox genome of the genus Ca. Brocadia reconstructed revealed that there were two identical hydrazine synthase (hzs) genes, providing the exact gene copy number of this crucial phylomarker of anammox at the genome level. Conclusions Our results showcased the high-quality and high-contiguity genome retrieval performance and demonstrated the feasibility of complete genome reconstruction using the IHA workflow from the enrichment system. These (near-) complete genomes provided a high resolution of the microbial community, which might help to understand the bacterial repertoire of anammox-associated systems. Combined with other validation experiments, the workflow can enable a detailed view of the anammox or other similar enrichment systems.

MotivationDanchin et al. have pointed out that cytosine drives the evolution of SARS-CoV-2. A depletion of cytosine might lead to the attenuation of SARS-CoV-2.ResultsWe built a website to track the composition change of mono-, di-, and tri-nucleotide of SARS-CoV-2 over time. The website downloads new strains available from GISAID and updates its results daily. Our analysis suggests that the composition of cytosine in coronaviruses is related to their reported mortality. Using 137,315 SARS-CoV-2 strains collected in ten months, we observed cytosine depletion at a rate of about one cytosine loss per month from the whole genome.AvailabilityThe website is available at http://www.bio8.cs.hku.hk/sarscov2/.Contactrbluo@cs.hku.hkSupplementary informationSupplementary data are available at Bioinformatics online.

Translocation is an important class of structural variants known to be associated with cancer formation and treatment. The recent development in single-molecule sequencing technologies that produce long reads has promised an advance in detecting translocations accurately. However, existing tools struggled with the high base error-rate of the long reads. Figuring out the correct translocation breakpoints is especially challenging due to suboptimally aligned reads. To address the problem, we developed Translocator, a robust and accurate translocation detection method that implements an effective realignment algorithm to recover the correct alignments. For benchmarking, we analyzed using NA12878 long reads against a modified GRCh38 reference genome embedded with translocations at known locations. Our results show that Translocator significantly outperformed other state-of-the-art methods, including Sniffles and PBSV. On Oxford Nanopore data, the recall improved from 48.2% to 87.5% and the precision from 88.7% to 92.7%. Translocator is available open-source at https://github.com/HKU-BAL/Translocator.

The pathogenesis of diabetic nephropathy (DN) is accompanied by alterations in biological function and signaling pathways regulated through complex molecular mechanisms. A number of regulatory factors, including transcription factors (TFs) and non-coding RNAs (ncRNAs, including lncRNAs and miRNAs), have been implicated in DN; however, it is unclear how the interactions among these regulatory factors contribute to the development of DN pathogenesis. In this study, we developed a network-based analysis to decipher interplays between TFs and ncRNAs regulating progression of DN by combining omics data with regulatory factor-target information. To accomplish this, we identified differential expression programs of mRNAs and miRNAs during early DN (EDN) and established DN. We then uncovered putative interactive connections among miRNA-mRNA, lncRNA-miRNA, and lncRNA-mRNA implicated in transcriptional control. This led to the identification of two lncRNAs (MALAT1 and NEAT1) and the three TFs (NF-κB, NFE2L2, and PPARG) that likely cooperate with a set of miRNAs to modulate EDN and DN target genes. The results highlight how crosstalk among TFs, lncRNAs, and miRNAs regulate the expression of genes both transcriptionally and post-transcriptionally, and our findings provide new insights into the molecular basis and pathogenesis of progressive DN.

Summary Single-molecule sequencing technologies produce much longer reads compared with next-generation sequencing, greatly improving the contiguity of de novo assembly of genomes. However, the relatively high error rates in long reads make it challenging to obtain high-quality assemblies. A computationally intensive consensus step is needed to resolve the discrepancies in the reads. Efficient consensus tools have emerged in the recent past, based on partial-order alignment. In this study, we discovered that the spatial relationship of alignment pileup is crucial to high-quality consensus and developed a deep learning-based consensus tool, CONNET, which outperforms the fastest tools in terms of both accuracy and speed. We tested CONNET using a 90× dataset of E. coli and a 37× human dataset. In addition to achieving high-quality consensus results, CONNET is capable of delivering phased diploid genome consensus. Diploid consensus on the above-mentioned human assembly further reduced 12% of the consensus errors made in the haploid results., Graphical Abstract, Highlights • Deep learning methods outperform existing approaches in assembly consensus • Spatial relationships in alignment pileup are crucial to high-quality consensus • Diploid consensus can further reduce errors made in haploid consensus • CONNET can be used for both consensus and polishing, Genomics; Bioinformatics; Sequence Analysis

Large networks with labeled nodes are prevalent in various applications, such as biological graphs, social networks, and e-commerce graphs. To extract insight from this rich information source, we propose MC-Explorer, which is an advanced analysis and visualization system. A highlight of MC-Explorer is its ability to discover motif-cliques from a graph with labeled nodes. A motif, such as a 3-node triangle, is a fundamental building block of a graph. A motif-clique is a "complete" subgraph in a network with respect to a desired higher-order connection pattern. For example, on a large biological graph, we found out some motif-cliques, which disclose new side effects of a drug, and potential drugs for healing diseases. MC-Explorer includes online and interactive facilities for exploring a large labeled network through the use of motif-cliques. We will demonstrate how MC-Explorer can facilitate the analysis and visualization of a labeled biological network.An online demo video of MC-Explorer can be accessed from https://www.dropbox.com/s/vkalumc28wqp8yl/demo.mov

Additional file 1: Figure S1. Length distribution of long reads (LRs) generated on Oxford Nanopore PromethION platform. Figure S2. Initial binning results of PNA dataset using various short reads and long reads combination strategies. The short reads (SRs) and long reads (LRs) were hybrid assembled by Unicycler with parameter --min_fasta_length 1000 and then was binned by MetaWRAP (‘initial binning’ and ‘bin refinement’ modules with parameter -x 70 -c 10). Figure S3. Mismatches and indels rates of four assemblers, evaluated using PNA-with-Spiked-Mock dataset. (A), mismatches rate (B), indels rate. Orange triangle and red dashed line stand for the average and median value of the MAGs misassembly, respectively. The circle with red asterisk stands for a genome with mismatch rate more than 1000/Mbp. Figure S4. Assembler performance evaluation using Mock dataset, in which only contained a pool of SRs and LRs from mock community. Recovered MAGs number of different assemblers: megahit (6), metaspades (5), OPERA-MS (8), Unicycler (7). (A), Genomes recovery (B), Genome fraction vs genome purity (C), Genome continuity (D), Misassembly event occurred in assembled MAGs (E), Mismatches rates (F), Indels rates (G), gene recovery ratio. Figure S5. Assembler performance evaluation using the HMP dataset. Three cycles were performed using Unicyler-based IHA method. Only MAGs meeting the criteria (completeness > 70% and contamination < 10%) have been kept for downstream evaluation. (A-I) different evaluation indicators. Numbers in the legend indicate the MAG number retrieved using different assemblers. For B, the NGA50 improvement in each MAG pair was calculated by using NGA50 of the MAG obtained from IHA approach compared with megahit and metaSPAdes. Figure S6. Assembler performance evaluation using the GIS20 dataset. Three cycles were performed using Unicyler-based IHA method. Only MAGs meeting the criteria (completeness > 70% and contamination < 10%) have been kept for downstream evaluation. (A-I) different evaluation indicators. Numbers in the legend indicate the MAG number retrieved using different assemblers. For B, the NGA50 improvement in each MAG pair was calculated by using NGA50 of the MAG obtained from IHA approach compared with megahit and metaSPAdes. Figure S7. Initial binning result of PNA dataset of using 6 Gbp short reads (SRs) and 22 Gbp long reds (LRs). The figure was visualized by Bandage (v0.8.1). The number indicates the contig name, the blank circle stands for the single-contig MAG and the circle with colors means the circular sing-contig MAG. Figure S8. Weighted functional gene sets distribution in 22 MAG pairs. Red asterisk stands for the genes in Set II and Set III were accounted more than 3% of total functional genes of the representative genome.

Background: An increasing number of infected children are being encountered with the rapid spread of COVID-19, yet the vulnerability of children with COVID-19 i

Single-molecule sequencing technologies have emerged in recent years and revolutionized structural variant calling, complex genome assembly, and epigenetic mark detection. However, the lack of a highly accurate small variant caller has limited the new technologies from being more widely used. In this study, we present Clair, the successor to Clairvoyante, a program for fast and accurate germline small variant calling, using single molecule sequencing data. For ONT data, Clair achieves the best precision, recall and speed as compared to several competing programs, including Clairvoyante, Longshot and Medaka. Through studying the missed variants and benchmarking intentionally overfitted models, we found that Clair may be approaching the limit of possible accuracy for germline small variant calling using pileup data and deep neural networks. Clair requires only a conventional CPU for variant calling and is an open source project available at https://github.com/HKU-BAL/Clair.

Identifying effective drug treatments for COVID‐19 is essential to reduce morbidity and mortality. Although a number of existing drugs have been proposed as potential COVID‐19 treatments, effective data platforms and algorithms to prioritize drug candidates for evaluation and application of knowledge graph for drug repurposing have not been adequately explored. A COVID‐19 knowledge graph by integrating 14 public bioinformatic databases containing information on drugs, genes, proteins, viruses, diseases, symptoms and their linkages is developed. An algorithm is developed to extract hidden linkages connecting drugs and COVID‐19 from the knowledge graph, to generate and rank proposed drug candidates for repurposing as treatments for COVID‐19 by integrating three scores for each drug: motif scores, knowledge graph PageRank scores, and knowledge graph embedding scores. The knowledge graph contains over 48 000 nodes and 13 37 000 edges, including 13 563 molecules in the DrugBank database. From the 5624 molecules identified by the motif‐discovery algorithms, ranking results show that 112 drug molecules had the top 2% scores, of which 50 existing drugs with other indications approved by health administrations reported. The proposed drug candidates serve to generate hypotheses for future evaluation in clinical trials and observational studies., Knowledge graph can be used to identify potential drug candidates for repurposing as treatment of COVID‐19 by using motif discovery algorithms to identify frequent high‐order patterns of interest. The findings serve to generate hypotheses and prioritize drug candidates for further evaluation in clinical trials and be further adapted for drug repurposing in other disease areas.

Linked-read sequencing, using highly-multiplexed genome partitioning and barcoding, can span hundreds of kilobases to improve de novo assembly, haplotype phasing, and other applications. Based on our analysis of 14 datasets, we introduce LRSim that simulates linked-reads by emulating the library preparation and sequencing process with fine control over variants, linked-read characteristics, and the short-read profile. We conclude from the phasing and assembly of multiple datasets, recommendations on coverage, fragment length, and partitioning when sequencing genomes of different sizes and complexities. These optimizations improve results by orders of magnitude, and enable the development of novel methods. LRSim is available at https://github.com/aquaskyline/LRSIM. Keywords: Linked-read, Molecular barcoding, Reads partitioning, Phasing, Reads simulation, Genome assembly, 10X Genomics

Over one million new biomedical articles are published every year. Efficient and accurate text-mining tools are urgently needed to automatically extract knowledge from these articles to support research and genetic testing. In particular, the extraction of gene-disease associations is mostly studied. However, existing text-mining tools for extracting gene-disease associations have limited capacity, as each sentence is considered separately. Our experiments show that the best existing tools, such as BeFree and DTMiner, achieve a precision of 48% and recall rate of 78% at most. In this study, we designed and implemented a deep learning approach, named RENET, which considers the correlation between the sentences in an article to extract gene-disease associations. Our method has significantly improved the precision and recall rate to 85.2% and 81.8%, respectively. The source code of RENET is available at https://bitbucket.org/alexwuhkucs/gda-extraction/src/master/.

MotivationMany rare diseases and cancers are fundamentally diseases of the genome. In the past several years, genome sequencing has become one of the most important tools in clinical practice for rare disease diagnosis and targeted cancer therapy. However, variant interpretation remains the bottleneck as is not yet automated and may take a specialist several hours of work per patient. On average, one-fifth of this time is spent on visually confirming the authenticity of the candidate variants.ResultsWe developed Skyhawk, an artificial neural network-based discriminator that mimics the process of expert review on clinically significant genomics variants. Skyhawk runs in less than one minute to review ten thousand variants, and about 30 minutes to review all variants in a typical whole-genome sequencing sample. Among the false positive singletons identified by GATK HaplotypeCaller, UnifiedGenotyper and 16GT in the HG005 GIAB sample, 79.7% were rejected by Skyhawk. Worked on the Variants with Unknown Significance (VUS), Skyhawk marked most of the false positive variants for manual review and most of the true positive variants no need for review.AvailabilitySkyhawk is easy to use and freely available at https://github.com/aquaskyline/Skyhawk

Background: Liver transplantation remains the treatment of choice for a selected group of hepatocellular carcinoma (HCC) patients. However, the long-term benefit is greatly hampered by post-transplant HCC recurrence. Our previous studies have identified liver graft injury as an acute phase event leading to post-transplant tumor recurrence. Methods: To re-examine this acute phase event at the molecular level and in an unbiased way, RNA sequencing (RNA-Seq) was performed on liver graft biopsies obtained from the transplant recipients two hours after portal vein reperfusion with an aim to capture frequently altered pathways that account for post-transplant tumor recurrence. Liver grafts from recurrent recipients (n = 6) were sequenced and compared with those from recipients without recurrence (n = 5). Results: RNA expression profiles comparison pointed to several frequently altered pathways, among which pathways related to cell adhesion molecules were the most involved. Subsequent validation using quantitative polymerase chain reaction confirmed the differential involvement of two cell adhesion molecules HFE (hemochromatosis) and CD274 and their related molecules in the acute phase event. Conclusion: This whole transcriptome strategy unravels the molecular landscape of liver graft gene expression alterations, which can identify key pathways and genes that are involved in acute phase liver graft injury that may lead to post-transplant tumor recurrence.