A comprehensive evaluation of the potential of three next-generation short-read-based plant pan-genome construction strategies for the identification of novel non-reference sequence.

Pan-genome studies are important for understanding plant evolution and guiding the breeding of crops by containing all genomic diversity of a certain species. Three short-read-based strategies for plant pan-genome construction include iterative individual, iteration pooling, and map-to-pan. Their performance is very different under various conditions, while comprehensive evaluations have yet to be conducted nowadays. Here, we evaluate the performance of these three pan-genome construction strategies for plants under different sequencing depths and sample sizes. Also, we indicate the influence of length and repeat content percentage of novel sequences on three pan-genome construction strategies. Besides, we compare the computational resource consumption among the three strategies. Our findings indicate that map-to-pan has the greatest recall but the lowest precision. In contrast, both two iterative strategies have superior precision but lower recall. Factors of sample numbers, novel sequence length, and the percentage of novel sequences' repeat content adversely affect the performance of all three strategies. Increased sequencing depth improves map-to-pan's performance, while not affecting the other two iterative strategies. For computational resource consumption, map-to-pan demands considerably more than the other two iterative strategies. Overall, the iterative strategy, especially the iterative pooling strategy, is optimal when the sequencing depth is less than 20X. Map-to-pan is preferable when the sequencing depth exceeds 20X despite its higher computational resource consumption.

TCOD: an integrated resource for tropical crops.

Tropical crops are vital for tropical agriculture, with resource scarcity, functional diversity and extensive market demand, providing considerable economic benefits for the world's tropical agriculture-producing countries. The rapid development of sequencing technology has promoted a milestone in tropical crop research, resulting in the generation of massive amount of data, which urgently needs an effective platform for data integration and sharing. However, the existing databases cannot fully satisfy researchers' requirements due to the relatively limited integration level and untimely update. Here, we present the Tropical Crop Omics Database (TCOD, https://ngdc.cncb.ac.cn/tcod), a comprehensive multi-omics data platform for tropical crops. TCOD integrates diverse omics data from 15 species, encompassing 34 chromosome-level de novo assemblies, 1 255 004 genes with functional annotations, 282 436 992 unique variants from 2048 WGS samples, 88 transcriptomic profiles from 1997 RNA-Seq samples and 13 381 germplasm items. Additionally, TCOD not only employs genes as a bridge to interconnect multi-omics data, enabling cross-species comparisons based on homology relationships, but also offers user-friendly online tools for efficient data mining and visualization. In short, TCOD integrates multi-species, multi-omics data and online tools, which will facilitate the research on genomic selective breeding and trait biology of tropical crops.

T2T-YAO: A Telomere-to-telomere Assembled Diploid Reference Genome for Han Chinese.

Since its initial release in 2001, the human reference genome has undergone continuous improvement in quality, and the recently released telomere-to-telomere (T2T) version - T2T-CHM13 - reaches its highest level of continuity and accuracy after 20 years of effort by working on a simplified, nearly homozygous genome of a hydatidiform mole cell line. Here, to provide an authentic complete diploid human genome reference for the Han Chinese, the largest population in the world, we assembled the genome of a male Han Chinese individual, T2T-YAO, which includes T2T assemblies of all the 22 + X + M and 22 + Y chromosomes in both haploid. The quality of T2T-YAO is much better than all currently available diploid assemblies, and its haploid version, T2T-YAO-hp, generated by selecting the better assembly for each autosome, reaches the top quality of fewer than one error per 29.5 Mb, even higher than that of T2T-CHM13. Derived from an individual living in the aboriginal region of the Han population, T2T-YAO shows clear ancestry and potential genetic continuity from the ancient ancestors. Each haplotype of T2T-YAO possesses ∼ 330-Mb exclusive sequences, ∼ 3100 unique genes, and tens of thousands of nucleotide and structural variations as compared with CHM13, highlighting the necessity of a population-stratified reference genome. The construction of T2T-YAO, a truly accurate and authentic representative of the Chinese population, would enable precise delineation of genomic variations and advance our understandings in the hereditability of diseases and phenotypes, especially within the context of the unique variations of the Chinese population.

Genomic Analysis of the Suspicious SARS-CoV-2 Sequences in the Public Sequencing Database.

SARS-CoV-2 has infected more than 600 million people. However, the origin of the virus is still unclear; knowing where the virus came from could help us prevent future zoonotic epidemics. Sequencing data, particularly metagenomic data, can profile the genomes of all species in the sample, including those not recognized at the time, thus allowing for the identification of the progenitor of SARS-CoV-2 in samples collected before the pandemic. We analyzed the data from 5,196 SARS-CoV-2-positive sequencing runs in the NCBI's SRA database with collection dates prior to 2020 or unknown. We found that the mutation patterns obtained from these suspicious SARS-CoV-2 reads did not match the genome characteristics of an unknown progenitor of the virus, suggesting that they may derive from circulating SARS-CoV-2 variants or other coronaviruses. Despite a negative result for tracking the progenitor of SARS-CoV-2, the methods developed in the study could assist in pinpointing the origin of various pathogens in the future. IMPORTANCE Sequences that are homologous to the SARS-CoV-2 genome were found in numerous sequencing runs that were not associated with the SARS-CoV-2 studies in the public database. It is unclear whether they are derived from the possible progenitor of SARS-CoV-2 or contamination of more recent SARS-CoV-2 variants circulated in the population due to the lack of information on the collection, library preparation, and sequencing processes. We have developed a computational framework to infer the evolutionary relationship between sequences based on the comparison of mutations, which enabled us to rule out the possibility that these suspicious sequences originate from unknown progenitors of SARS-CoV-2.

HGD: an integrated homologous gene database across multiple species.

Homology is fundamental to infer genes' evolutionary processes and relationships with shared ancestry. Existing homolog gene resources vary in terms of inferring methods, homologous relationship and identifiers, posing inevitable difficulties for choosing and mapping homology results from one to another. Here, we present HGD (Homologous Gene Database, https://ngdc.cncb.ac.cn/hgd), a comprehensive homologs resource integrating multi-species, multi-resources and multi-omics, as a complement to existing resources providing public and one-stop data service. Currently, HGD houses a total of 112 383 644 homologous pairs for 37 species, including 19 animals, 16 plants and 2 microorganisms. Meanwhile, HGD integrates various annotations from public resources, including 16 909 homologs with traits, 276 670 homologs with variants, 398 573 homologs with expression and 536 852 homologs with gene ontology (GO) annotations. HGD provides a wide range of omics gene function annotations to help users gain a deeper understanding of gene function.

TSomVar: a tumor-only somatic and germline variant identification method with random forest.

Somatic variants act as critical players during cancer occurrence and development. Thus, an accurate and robust method to identify them is the foundation of cutting-edge cancer genome research. However, due to low accessibility and high individual-/sample-specificity of the somatic variants in tumor samples, the detection is, to date, still crammed with challenges, particularly when lacking paired normal samples as control. To solve this burning issue, we developed a tumor-only somatic and germline variant identification method (TSomVar) using the random forest algorithm established on sample-specific variant datasets derived from genotype imputation, reads-mapping level annotation and functional annotation. We trained TSomVar by using genomic variant datasets of three major cancer types: colorectal cancer, hepatocellular carcinoma and skin cutaneous melanoma. Compared with existing tumor-only somatic variant identification tools, TSomVar shows excellent performances in somatic variant detection with higher accuracy and better capability of recalling for test datasets from colorectal cancer and skin cutaneous melanoma. In addition, TSomVar is equipped with the competence of accurately identifying germline variants in tumor samples. Taken together, TSomVar will undoubtedly facilitate and revolutionize somatic variant explorations in cancer research.

Whole-genome sequencing reveals the genetic mechanisms of domestication in classical inbred mice.

BACKGROUND: The laboratory mouse was domesticated from the wild house mouse. Understanding the genetics underlying domestication in laboratory mice, especially in the widely used classical inbred mice, is vital for studies using mouse models. However, the genetic mechanism of laboratory mouse domestication remains unknown due to lack of adequate genomic sequences of wild mice. RESULTS: We analyze the genetic relationships by whole-genome resequencing of 36 wild mice and 36 inbred strains. All classical inbred mice cluster together distinctly from wild and wild-derived inbred mice. Using nucleotide diversity analysis, Fst, and XP-CLR, we identify 339 positively selected genes that are closely associated with nervous system function. Approximately one third of these positively selected genes are highly expressed in brain tissues, and genetic mouse models of 125 genes in the positively selected genes exhibit abnormal behavioral or nervous system phenotypes. These positively selected genes show a higher ratio of differential expression between wild and classical inbred mice compared with all genes, especially in the hippocampus and frontal lobe. Using a mutant mouse model, we find that the SNP rs27900929 (T>C) in gene Astn2 significantly reduces the tameness of mice and modifies the ratio of the two Astn2 (a/b) isoforms. CONCLUSION: Our study indicates that classical inbred mice experienced high selection pressure during domestication under laboratory conditions. The analysis shows the positively selected genes are closely associated with behavior and the nervous system in mice. Tameness may be related to the Astn2 mutation and regulated by the ratio of the two Astn2 (a/b) isoforms.

CCAS: One-stop and comprehensive annotation system for individual cancer genome at multi-omics level.

Due to the explosion of cancer genome data and the urgent needs for cancer treatment, it is becoming increasingly important and necessary to easily and timely analyze and annotate cancer genomes. However, tumor heterogeneity is recognized as a serious barrier to annotate cancer genomes at the individual patient level. In addition, the interpretation and analysis of cancer multi-omics data rely heavily on existing database resources that are often located in different data centers or research institutions, which poses a huge challenge for data parsing. Here we present CCAS (Cancer genome Consensus Annotation System, https://ngdc.cncb.ac.cn/ccas/#/home), a one-stop and comprehensive annotation system for the individual patient at multi-omics level. CCAS integrates 20 widely recognized resources in the field to support data annotation of 10 categories of cancers covering 395 subtypes. Data from each resource are manually curated and standardized by using ontology frameworks. CCAS accepts data on single nucleotide variant/insertion or deletion, expression, copy number variation, and methylation level as input files to build a consensus annotation. Outputs are arranged in the forms of tables or figures and can be searched, sorted, and downloaded. Expanded panels with additional information are used for conciseness, and most figures are interactive to show additional information. Moreover, CCAS offers multidimensional annotation information, including mutation signature pattern, gene set enrichment analysis, pathways and clinical trial related information. These are helpful for intuitively understanding the molecular mechanisms of tumors and discovering key functional genes.

Docking rings in a solid: reversible assembling of pseudorotaxanes inside a zirconium metal-organic framework.

An unprecedented zirconium metal-organic framework featuring a T-shaped benzimidazole strut was constructed and employed as a sponge-like material for selective absorption of macrocyclic guests. The neutral benzimidazole domain of the as-synthesized framework can be readily protonated and fully converted to benzimidazolium. Mechanical threading of [24]crown-8 ether wheels onto recognition sites to form pseudorotaxanes was evidenced by solution nuclear magnetic resonance, solid-state fluorescence, and infrared spectroscopy. Selective absorption of [24]crown-8 ether rather than its dibenzo counterpart was also observed. Further study reveals that this binding process is reversible and acid-base switchable. The success of docking macrocyclic guests in crystals via host-guest interactions provides an alternative route to complex functional materials with interpenetrated structures.

Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases.

Endometrial carcinoma (EC), a common female reproductive system malignant tumor, affects thousands of people with high morbidity and mortality worldwide. This study was aimed at developing a prediction model for the diagnosis of EC in the general population. First, we obtained datasets GSE63678, GSE106191, and GSE115810 from the Gene Expression Omnibus (GEO) database, dataset GSE17025 from the GEO database, and the RNA sequence of EC from The Cancer Genome Atlas (TCGA) database to constitute the training, test, and validation groups, respectively. Subsequently, the 96 most significantly differentially expressed genes (DEGs) were identified and analyzed for function and pathway enrichment in the training group. Next, we acquired the disease-specific genes by random forest and established an artificial neural network for the diagnosis. Receiver operating characteristic (ROC) curves were utilized to identify the signature across the three groups. Finally, immune infiltration was analyzed to reveal tumor-immune microenvironment (TIME) alterations in EC. The top 96 DEGs (77 down-regulated and 19 up-regulated genes) were primarily enriched in the interleukin-17 signaling pathway, protein digestion and absorption, and transcriptional misregulation in cancer. Subsequently, 14 characterizing genes of EC were identified by random forest. In the training, test, and validation groups, the artificial neural network was constructed with high diagnostic accuracies of 0.882, 0.864, and 0.839, respectively, and areas under the ROC curve (AUCs) of 0.928, 0.921, and 0.782, respectively. Finally, resting and activated mast cells were found to have increased in TIME. We constructed an artificial diagnostic model with excellent reliability for EC and uncovered variations in the immunological ecosystem of EC through integrated bioinformatics approaches, which might be potential diagnostic targets for EC.

2D and 3D metal-organic frameworks constructed with a mechanically rigidified [3]rotaxane ligand.

A mechanically interlocked [3]rotaxane was newly designed, synthesized, and employed as a ligand for constructing metal-organic frameworks (MOFs). The nano-confinement by macrocycles forces the soft bis-isophthalate axle into a pseudo-rigid conformation and coordinates to zinc(II) ions, affording a two- or three-dimensional MOF under controlled conditions. The 2D MOF exhibits a neutral framework with a periodic puckering sheet structure, while an anionic framework with a pts topology was observed for the 3D MOF. The phase purity of both bulk materials was confirmed by powder X-ray diffraction. Thermogravimetric analysis reveals that both materials are stable up to 250 °C. The success of applying mechanical bonds to rigidify flexible ligands provides new insights for the design of metal-organic frameworks.

CancerSCEM: a database of single-cell expression map across various human cancers.

With the proliferating studies of human cancers by single-cell RNA sequencing technique (scRNA-seq), cellular heterogeneity, immune landscape and pathogenesis within diverse cancers have been uncovered successively. The exponential explosion of massive cancer scRNA-seq datasets in the past decade are calling for a burning demand to be integrated and processed for essential investigations in tumor microenvironment of various cancer types. To fill this gap, we developed a database of Cancer Single-cell Expression Map (CancerSCEM, https://ngdc.cncb.ac.cn/cancerscem), particularly focusing on a variety of human cancers. To date, CancerSCE version 1.0 consists of 208 cancer samples across 28 studies and 20 human cancer types. A series of uniformly and multiscale analyses for each sample were performed, including accurate cell type annotation, functional gene expressions, cell interaction network, survival analysis and etc. Plus, we visualized CancerSCEM as a user-friendly web interface for users to browse, search, online analyze and download all the metadata as well as analytical results. More importantly and unprecedentedly, the newly-constructed comprehensive online analyzing platform in CancerSCEM integrates seven analyze functions, where investigators can interactively perform cancer scRNA-seq analyses. In all, CancerSCEM paves an informative and practical way to facilitate human cancer studies, and also provides insights into clinical therapy assessments.

Metagenomic evidence for the co-existence of SARS and H1N1 in patients from 2007-2012 flu seasons in France.

By re-analzying public metagenomic data from 101 patients infected with influenza A virus during the 2007-2012 H1N1 flu seasons in France, we identified 22 samples with SARS-CoV sequences. In 3 of them, the SARS genome sequences could be fully assembled out of each. These sequences are highly similar (99.99% and 99.7%) to the artificially constructed recombinant SARS-CoV (SARSr-CoV) strains generated by the J. Craig Venter Institute in the USA. Moreover, samples from different flu seasons have different SARS-CoV strains, and the divergence between these strains cannot be explained by natural evolution. Our study also shows that retrospective studies using public metagenomic data from past major epidemic outbreaks serve as a genomic strategy for researching the origins or spread of infectious diseases.

GSA-Human: Genome Sequence Archive for Human.

The Genome Sequence Archive for Human (GSA-Human) is a data repository specialized for human genetic related data derived from biomedical researches, and also supports the data collection and management of National Key Research and Development Projects. GSA-Human has a data security management strategy according to the national regulations of human genetic resources. It provides two different models of data access: Open-access and Controlled-access. Open-access data are universally and freely accessible for global researchers, while Controlled-access ensures that data are accessed only by authorized users with the permission of the Data Access Committee (DAC). Till July 2021, GSA-Human has housed more than 5.27 PB of data from 750 datasets.

RefRGim: an intelligent reference panel reconstruction method for genotype imputation with convolutional neural networks.

Genotype imputation is a statistical method for estimating missing genotypes from a denser haplotype reference panel. Existing methods usually performed well on common variants, but they may not be ideal for low-frequency and rare variants. Previous studies showed that the population similarity between study and reference panels is one of the key factors influencing the imputation accuracy. Here, we developed an imputation reference panel reconstruction method (RefRGim) using convolutional neural networks (CNNs), which can generate a study-specified reference panel for each input data based on the genetic similarity of individuals from current study and references. The CNNs were pretrained with single nucleotide polymorphism data from the 1000 Genomes Project. Our evaluations showed that genotype imputation with RefRGim can achieve higher accuracies than original reference panel, especially for low-frequency and rare variants. RefRGim will serve as an efficient reference panel reconstruction method for genotype imputation. RefRGim is freely available via GitHub: https://github.com/shishuo16/RefRGim.

Author Correction: Whole-genome and time-course dual RNA-Seq analyses reveal chronic pathogenicity-related gene dynamics in the ginseng rusty root rot pathogen Ilyonectria robusta.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An online coronavirus analysis platform from the National Genomics Data Center.

Since the first reported severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in December 2019, coronavirus disease 2019 (COVID-19) has become a global pandemic, spreading to more than 200 countries and regions worldwide. With continued research progress and virus detection, SARS-CoV-2 genomes and sequencing data have been reported and accumulated at an unprecedented rate. To meet the need for fast analysis of these genome sequences, the National Genomics Data Center (NGDC) of the China National Center for Bioinformation (CNCB) has established an online coronavirus analysis platform, which includes de novoassembly, BLAST alignment, genome annotation, variant identification, and variant annotation modules. The online analysis platform can be freely accessed at the 2019 Novel Coronavirus Resource (2019nCoVR) (https://bigd.big.ac.cn/ncov/online/tools).

From asymmetrical to balanced genomic diversification during rediploidization: Subgenomic evolution in allotetraploid fish.

A persistent enigma is the rarity of polyploidy in animals, compared to its prevalence in plants. Although animal polyploids are thought to experience deleterious genomic chaos during initial polyploidization and subsequent rediploidization processes, this hypothesis has not been tested. We provide an improved reference-quality de novo genome for allotetraploid goldfish whose origin dates to ~15 million years ago. Comprehensive analyses identify changes in subgenomic evolution from asymmetrical oscillation in goldfish and common carp to diverse stabilization and balanced gene expression during continuous rediploidization. The homoeologs are coexpressed in most pathways, and their expression dominance shifts temporally during embryogenesis. Homoeolog expression correlates negatively with alternation of DNA methylation. The results show that allotetraploid cyprinids have a unique strategy for balancing subgenomic stabilization and diversification. Rediploidization process in these fishes provides intriguing insights into genome evolution and function in allopolyploid vertebrates.

Exome sequencing identifies new somatic alterations and mutation patterns of tongue squamous cell carcinoma in a Chinese population.

Tongue squamous cell carcinoma (TSCC) is an aggressive group of tumors characterized by high rates of regional lymph node metastasis and local recurrence. Emerging evidence has revealed genetic variations of TSCC across different geographical regions due to the impact of multiple risk factors such as chewing betel-quid. However, we know little of the mutational processes of TSCC in the Chinese population without the history of chewing betel-quid/tobacco. To explore the mutational spectrum of this disease, we performed whole-exome sequencing of sample pairs, comprising tumors and normal tissue, from 82 TSCC patients. In addition to identifying seven previously known TSCC-associated genes (TP53, CDKN2A, PIK3CA, NOTCH1, ASXL1, USH2A, and CSMD3), the analysis revealed six new genes (GNAQ, PRG4, RP1, ZNF16, NBEA, and PTPRC) that had not been reported previously in TSCC. Our in vitro experiments identified ZNF16 for the first time as a solid tumor associated gene to promote malignancy of TSCC cells. We also identified a microRNA (miR-585-5p) encoded by the 5q35.1 region and characterized it as a tumor suppressor by targeting SOX9. At least one non-silent mutation of genes involved in the 10 canonical oncogenic pathways (Notch, RTK-RAS, PI3K, Wnt, Cell cycle, p53, Myc, Hippo, TGFß, and Nrf2) was found in 82.9% of cases. Collectively, our data extend the spectrum of TSCC mutations and define novel diagnosis markers and potential clinical targets for TSCC. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Whole-genome and time-course dual RNA-Seq analyses reveal chronic pathogenicity-related gene dynamics in the ginseng rusty root rot pathogen Ilyonectria robusta.

Ilyonectria robusta causes rusty root rot, the most devastating chronic disease of ginseng. Here, we for the first time report the high-quality genome of the I. robusta strain CD-56. Time-course (36 h, 72 h, and 144 h) dual RNA-Seq analysis of the infection process was performed, and many genes, including candidate effectors, were found to be associated with the progression and success of infection. The gene expression profile of CD-56 showed a trend of initial inhibition and then gradually returned to a profile similar to that of the control. Analyses of the gene expression patterns and functions of pathogenicity-related genes, especially candidate effector genes, indicated that the stress response changed to an adaptive response during the infection process. For ginseng, gene expression patterns were highly related to physiological conditions. Specifically, the results showed that ginseng defenses were activated by CD-56 infection and persisted for at least 144 h thereafter but that the mechanisms invoked were not effective in preventing CD-56 growth. Moreover, CD-56 did not appear to fully suppress plant defenses, even in late stages after infection. Our results provide new insight into the chronic pathogenesis of CD-56 and the comprehensive and complex inducible defense responses of ginseng root to I. robusta infection.