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Irritable bowel syndrome (IBS) is one of the functional gastrointestinal disorders characterized by chronic and/or recurrent symptoms of abdominal pain and irregular defecation. Changed gut microbiota has been proposed to mediate IBS; however, contradictory results exist, and IBS-specific microbiota, metabolites, and their interactions remain poorly understood. To address this issue, we performed metabolomic and metagenomic profiling of stool and serum samples based on discovery (n = 330) and validation (n = 101) cohorts. Fecal metagenomic data showed moderate dysbiosis compared with other diseases, in contrast, serum metabolites showed significant differences with greater power to distinguish IBS patients from healthy controls. Specifically, 726 differentially abundant serum metabolites were identified, including a cluster of fatty acyl-CoAs enriched in IBS. We further identified 522 robust associations between differentially abundant gut bacteria and fecal metabolites, of which three species including Odoribacter splanchnicus, Escherichia coli, and Ruminococcus gnavus were strongly associated with the low abundance of dihydropteroic acid. Moreover, dysregulated tryptophan/serotonin metabolism was found to be correlated with the severity of IBS depression in both fecal and serum metabolomes, characterized by a shift in tryptophan metabolism towards kynurenine production. Collectively, our study revealed serum/fecal metabolome alterations and their relationship with gut microbiome, highlighted the massive alterations of serum metabolites, which empower to recognize IBS patients, suggested potential roles of metabolic dysregulation in IBS pathogenesis, and offered new clues to understand IBS depression comorbidity. Our study provided a valuable resource for future studies, and would facilitate potential clinical applications of IBS featured microbiota and/or metabolites.

WRKYs play important roles in plant growth, defense regulation, and the stress response. However, the mechanisms through which WRKYs are involved in drought tolerance have been rarely characterized in kale (Brassica oleracea var. acephala DC). In this study, we cloned the BoWRKY10 gene from kale and its expression was induced with PEG4000, NaCl, gibberellic acid, cold, H2O2, and abscisic acid (ABA). Bo-WRKY10 was localized in the nucleus. The protein had a WRKY domain and a C2H2 zinc finger structure and belonged to subgroup II. The analysis of yeast transcriptional activity showed that BoWRKY10 may have transcriptional activation activity, which was mainly determined by the carboxyl terminal sequence. BoW-RKY10-overexpressing tobacco (Nicotiana tabacum L.) showed enhanced drought tolerance. After the drought treatment, relative water content and proline contents as well as superoxide dismutase activity were higher in transgenic plants, while malondialdehyde and H2O2 contents were lower. In addition, several genes related to the ABA signaling pathway, sucrose, and the reactive oxygen species scavenging system, were significantly upregulated in the transgenic lines. These results demonstrate that BoWRKY10 confers drought tolerance in tobacco. These results provide clues regarding the mechanism by which BoWRKY10 contributes to the regulation of drought stress tolerance.

Motivation The microbial community plays an essential role in human diseases and physiological activities. The functions of microbes can differ due to strain-level differences in the genome sequences. Shotgun metagenomic sequencing allows us to profile the strains in microbial communities practically. However, current methods are underdeveloped due to the highly similar sequences among strains. We observe that strains genotypes at the same single nucleotide variant (SNV) locus can be speculated by the genotype frequencies. Also, the variants in different loci covered by the same reads can provide evidence that they reside on the same strain. Results These insights inspire us to design PStrain, an optimization method that utilizes genotype frequencies and the reads which cover multiple SNV loci to profile strains iteratively based on SNVs in a set of MetaPhlAn2 marker genes. Compared to the state-of-art methods, PStrain, on average, improved the performance of inferring strains abundances and genotypes by 87.75% and 59.45%, respectively. We have applied the PStrain package to the dataset with two cohorts of colorectal cancer (CRC) and found that the sequences of Bacteroides coprocola strains are significantly different between CRC and control samples, which is the first time to report the potential role of B.coprocola in the gut microbiota of CRC. Availabilityand implementation https://github.com/wshuai294/PStrain. Supplementary information Supplementary data are available at Bioinformatics online.

Genetics data visualization plays an important role in the sharing of knowledge from cancer genome research. Many types of visualization are widely used, most of which are static and require sufficient coding experience to create. Here, we present Oviz-Bio, a web-based platform that provides interactive and real-time visualizations of cancer genomics data. Researchers can interactively explore visual outputs and export high-quality diagrams. Oviz-Bio supports a diverse range of visualizations on common cancer mutation types, including annotation and signatures of small scale mutations, haplotype view and focal clusters of copy number variations, split-reads alignment and heatmap view of structural variations, transcript junction of fusion genes and genomic hotspot of oncovirus integrations. Furthermore, Oviz-Bio allows landscape view to investigate multi-layered data in samples cohort. All Oviz-Bio visual applications are freely available at https://bio.oviz.org/.

Background Horizontal Gene Transfer (HGT) refers to the transfer of genetic materials between organisms through mechanisms other than parent-offspring inheritance. HGTs may affect human health through a large number of microorganisms, especially the gut microbiomes which the human body harbors. The transferred segments may lead to complicated local genome structural variations. Details of the local genome structure can elucidate the effects of the HGTs. Results In this work, we propose a graph-based method to reconstruct the local strains from the gut metagenomics data at the HGT sites. The method is implemented in a package named LEMON. The simulated results indicate that the method can identify transferred segments accurately on reference sequences of the microbiome. Simulation results illustrate that LEMON could recover local strains with complicated structure variation. Furthermore, the gene fusion points detected in real data near HGT breakpoints validate the accuracy of LEMON. Some strains reconstructed by LEMON have a replication time profile with lower standard error, which demonstrates HGT events recovered by LEMON is reliable. Conclusions Through LEMON we could reconstruct the sequence structure of bacteria, which harbors HGT events. This helps us to study gene flow among different microbial species.

Background Copy number variation is crucial in deciphering the mechanism and cure of complex disorders and cancers. The recent advancement of scDNA sequencing technology sheds light upon addressing intratumor heterogeneity, detecting rare subclones, and reconstructing tumor evolution lineages at single-cell resolution. Nevertheless, the current circular binary segmentation based approach proves to fail to efficiently and effectively identify copy number shifts on some exceptional trails. Results Here, we propose SCYN, a CNV segmentation method powered with dynamic programming. SCYN resolves the precise segmentation on in silico dataset. Then we verified SCYN manifested accurate copy number inferring on triple negative breast cancer scDNA data, with array comparative genomic hybridization results of purified bulk samples as ground truth validation. We tested SCYN on two datasets of the newly emerged 10x Genomics CNV solution. SCYN successfully recognizes gastric cancer cells from 1% and 10% spike-ins 10x datasets. Moreover, SCYN is about 150 times faster than state of the art tool when dealing with the datasets of approximately 2000 cells. Conclusions SCYN robustly and efficiently detects segmentations and infers copy number profiles on single cell DNA sequencing data. It serves to reveal the tumor intra-heterogeneity. The source code of SCYN can be accessed in https://github.com/xikanfeng2/SCYN.

Natural and artificial directional selections have resulted in significantly genetic and phenotypic differences across breeds in domestic animals. However, the molecular regulation of skeletal muscle diversity remains largely unknown. Here, we conducted transcriptome profiling of skeletal muscle across 27 time points, and performed whole-genome re-sequencing in Landrace (lean-type) and Tongcheng (obese-type) pigs. The transcription activity decreased with development, and the high-resolution transcriptome precisely captured the characterizations of skeletal muscle with distinct biological events in four developmental phases: Embryonic, Fetal, Neonatal, and Adult. A divergence in the developmental timing and asynchronous development between the two breeds was observed; Landrace showed a developmental lag and stronger abilities of myoblast proliferation and cell migration, whereas Tongcheng had higher ATP synthase activity in postnatal periods. The miR-24-3p driven network targeting insulin signaling pathway regulated glucose metabolism. Notably, integrated analysis suggested SATB2 and XLOC_036765 contributed to skeletal muscle diversity via regulating the myoblast migration and proliferation, respectively. Overall, our results provide insights into the molecular regulation of skeletal muscle development and diversity in mammals., Author summary Compared with the commercial breeds, Chinese local pig breeds have lower growth rates, higher fat content and better meat quality, because they have adapted to local environment and have not been strongly selected. These differences make them as exceptional resources to identify candidate markers for the improvement of meat production traits in pig breeding. In this study, we compared the genome and skeletal muscle transcriptome differences between Chinese local Tongcheng and commercial Landrace pigs, and uncovered the genetic regulation of coding and non-coding RNAs, such as SATB2 and XLOC_036765, in skeletal muscle development and diversity. This study enhances our understanding of the genetic basis of skeletal muscle development and diversity, and provide useful molecular markers for the genetic improvement of meat production traits.

SARS-CoV-2 is a single-stranded RNA betacoronavirus with a high mutation rate. The rapidly emerged SARS-CoV-2 variants could increase the transmissibility, aggravate the severity, and even fade the vaccine protection. Although the coinfections of SARS-CoV-2 with other respiratory pathogens have been reported, whether multiple SARS-CoV-2 variants coinfection exists remains controversial. This study collected 12,986 and 4,113 SARS-CoV-2 genomes from the GISAID database on May 11, 2020 (GISAID20May11) and April 1, 2021 (GISAID21Apr1), respectively. With the single-nucleotide variants (SNV) and network clique analysis, we constructed the single-nucleotide polymorphism (SNP) coexistence networks and noted the SNP number of the maximal clique as the coinfection index. The coinfection indices of GISAID20May11 and GISAID21Apr1 datasets were 16 and 34, respectively. Simulating the transmission routes and the mutation accumulations, we discovered the linear relationship between the coinfection index and the coinfected variant number. Based on the linear relationship, we deduced that the COVID-19 cases in the GISAID20May11 and GISAID21Apr1 datasets were coinfected with 2.20 and 3.42 SARS-CoV-2 variants on average. Additionally, we performed Nanopore sequencing on 42 COVID-19 patients to explore the virus mutational characteristics. We found the heterozygous SNPs in 41 COVID-19 cases, which support the coinfection of SARS-CoV-2 variants and challenge the accuracy of phylogenetic analysis. In conclusion, our findings reported the coinfection of SARS-CoV-2 variants in COVID-19 patients, demonstrated the increased coinfected variants number in the epidemic, and provided clues for the prolonged viral shedding and severe symptoms in some cases.

Oncovirus integrations cause copy number variations (CNVs) and complex structural variations (SVs) on host genomes. However, the understanding of how inserted viral DNA impacts the local genome remains limited. The linear structure of the oncovirus integrated local genomic map (LGM) will lay the foundations to understand how oncovirus integrations emerge and compromise the host genome’ s functioning. We propose a conjugate graph model to reconstruct the rearranged local genomic map at integrated loci. Simulation tests prove the reliability and credibility of the algorithm. Applications of the algorithm to whole-genome sequencing data of Human papillomavirus (HPV) and hepatitis B virus (HBV)-infected cancer samples gained biological insights on oncovirus integrations. We observed five affection patterns of oncovirus integrations from the HPV and HBV-integrated cancer samples, including the exon loss, promoter gain, hyper-amplification of tumor gene, the viral cis-regulation inserted at the single intron and at the intergenic region. We found that the focal duplicates and host SVs are frequent in the HPV-integrated LGMs, while the focal deletions and complex virus SVs are prevalent in HBV-integrated LGMs. Furthermore, with the results yields from our method, we found the enhanced microhomology-mediated end joining (MMEJ) might lead to both HPV and HBV integrations, and conjectured that the HPV integrations might mainly occur during the DNA replication process. The conjugate graph algorithm code and LGM construction pipeline, available at https://github.com/deepomicslab/FuseSV.Key pointsConjugate graph model resolves complex local genomic strcuture at oncovirus integration loci.Local genome maps reveal five affection patterns of oncovirus integrations.Microhomology bases and small insertions are enriched at the junctions of structural variations and virus integrations.HPV and HBV integrations may be induced by the enhanced microhomology-meditated mechanism.

The gut-brain axis provides a pathway for the interaction between gut microbiota and methamphetamine (METH) addiction. However, the gut microbial signatures during different phases of METH use remain unclear. In the present study, we established models of acquisition, extinction, and reinstatement of METH-induced conditioned place preference (CPP) in male mice and detected the gut microbiome profiles of the fecal samples at the three phases by 16S rRNA gene sequencing. Our results revealed that the richness of the gut microbiome increased following repeated METH administration, and it decreased after 4 weeks of abstinence. The microbial richness remained at a low level after one METH challenge at the reinstatement phase. The abundance of several genera including Prevotella, Bacteroides, and Lactobacillus differentially altered among phases of METH-induced CPP. The co-occurrence networks of the gut microbiome became weaker and more unstable during the development of METH-induced CPP at the extinction and reinstatement phases. Notably, the predicted gene functions of short-chain fatty acid metabolism, which were correlated with the abundance of Prevotella, Bacteroides, and Lactobacillus, were found differentially enriched among phases of METH-induced CPP. Our findings highlight a potential association between perturbations of the gut microbiome and different phases of METH use.

Background: Periodontitis and peri-implantitis are common biofilm-mediated infectious diseases affecting teeth and dental implants, and have been considered to be initiated with the adjacent microbial dysbiosis. Study Aim: To further understand the essence of oral microbiota dysbiosis in terms of bacterial interactions, community structure and microbial stability.Methods: We analyzed 64 plaque samples from 34 participants with teeth or implants under different health conditions using metagenomic sequencing. After taxonomical annotation, we computed the core microbiome, analyzed the bacterial co-occurrence networks, and calculated the microbial stability in supra- and sub-gingival plaques from hosts with different health conditions. Result: When inflammation arises, the subgingival communities become less connective and competitive with fewer hub species. In contrast, the supragingival communities tend to be more connective and competitive with a increased number of hub species. Notably, periodontitis and peri-implantitis are associated with significantly increased microbial stability in subgingival plaques. In addition, we also observe similar core bacterial components yet distinct co-occurrence networks and community structures between the healthy and diseased hosts.Conclusion: The findings indicated that the aberrant changes of the bacterial co-occurrence networks and community structures are the essence of dysbiosis in periodontal and peri-implant patients, while breaking the diseased equilibrium and reestablishing healthy equilibrium is crucial for the treatment of periodontitis and peri-implantitis.

SUMMARYNeural stem cells (NSCs) divide asymmetrically to balance their self-renewal and differentiation, an imbalance in which can lead to NSC overgrowth and tumour formation. The functions of Parafibromin, a conserved tumour suppressor, in the nervous system are not established. Here, we demonstrate that Drosophila Parafibromin/Hyrax (Hyx) inhibits NSC overgrowth by governing cell polarity. Hyx is essential for the apicobasal polarization of NSCs, through its role in the asymmetric distribution of polarity proteins. hyx depletion results in the symmetric division of NSCs, leading to the formation of supernumerary NSCs in the larval brain. Importantly, we show that human Parafibromin can fully rescue NSC overgrowth and cell polarity defects in Drosophila hyx mutant brains. We have also discovered a novel role for Hyx in regulating the formation of interphase microtubule-organizing center and mitotic spindles in NSCs. Moreover, Hyx is required for the proper localization of a key centrosomal protein, Polo, and the microtubule-binding proteins Msps and D-TACC in dividing NSCs. Furthermore, Hyx directly regulates the polo expression in vitro. Altogether, our study provides the first evidence that the brain tumour suppressor-like role and polarity establishing functions of Hyx are mediated by its role in regulating microtubule growth and centrosomal assembly. The new paradigm that Parafibromin orchestrates cell polarization by regulating centrosomal assembly may be relevant to future studies on Parafibromin/HRPT2-associated cancers.

LncRNAs comprise a vast majority of the RNAs transcribed from non-coding regions of mammalian genomes. Recently, lncRNAs have emerged as key regulators of a wide range of biological processes. In this study, we identified lncRNAs expressed in swine ovary tissue from Yorkshire and Meishan pigs. We obtained 3827 lncRNAs expressed from 4973 transcripts in swine ovary tissue, of which 510 were ovary specific, 192 were differentially expressed between Yorkshire and Meishan pigs and 38 were both ovary specific and differentially expressed. Furthermore, we randomly selected several differentially expressed and ovary-specific lncRNAs for verification by qRT-PCR and RT-PCR respectively. We also predicted the functions of the lncRNAs by analysing their nearest neighbouring protein-coding genes. Together, our results identify a series of ovary-associated lncRNAs for further experimental investigation of the functions of these genes in ovary development or for selection of candidate genes for breeding.

Background Recent advances in genome analysis have established that chromatin has preferred 3D conformations, which bring distant loci into contact. Identifying these contacts is important for us to understand possible interactions between these loci. This has motivated the creation of the Hi-C technology, which detects long-range chromosomal interactions. Distance geometry-based algorithms, such as ChromSDE and ShRec3D, have been able to utilize Hi-C data to infer 3D chromosomal structures. However, these algorithms, being matrix-based, are space- and time-consuming on very large datasets. A human genome of 100 kilobase resolution would involve ∼30,000 loci, requiring gigabytes just in storing the matrices. Results We propose a succinct representation of the distance matrices which tremendously reduces the space requirement. We give a complete solution, called SuperRec, for the inference of chromosomal structures from Hi-C data, through iterative solving the large-scale weighted multidimensional scaling problem. Conclusions SuperRec runs faster than earlier systems without compromising on result accuracy. The SuperRec package can be obtained from http://www.cs.cityu.edu.hk/~shuaicli/SuperRec. Electronic supplementary material The online version of this article (10.1186/s12864-019-5470-2) contains supplementary material, which is available to authorized users.

Background Finding peptides with high binding affinity to Class I major histocompatibility complex (MHC-I) attracts intensive research, and it serves a crucial part of developing a better vaccine for precision medicine. Traditional methods cost highly for designing such peptides. The advancement of computational approaches reduces the cost of new drug discovery dramatically. Compared with flourishing computational drug discovery area, the immunology area lacks tools focused on in silico design for the peptides with high binding affinity. Attributed to the ever-expanding amount of MHC-peptides binding data, it enables the tremendous influx of deep learning techniques for modeling MHC-peptides binding. To leverage the availability of these data, it is of great significance to find MHC-peptides binding specificities. The binding motifs are one of the key components to decide the MHC-peptides combination, which generally refer to a combination of some certain amino acids at certain sites which highly contribute to the binding affinity. Result In this work, we propose the Motif Activation Mapping (MAM) network for MHC-I and peptides binding to extract motifs from peptides. Then, we substitute amino acid randomly according to the motifs for generating peptides with high affinity. We demonstrated the MAM network could extract motifs which are the features of peptides of highly binding affinities, as well as generate peptides with high-affinities; that is, 0.859 for HLA-A*0201, 0.75 for HLA-A*0206, 0.92 for HLA-B*2702, 0.9 for HLA-A*6802 and 0.839 for Mamu-A1*001:01. Besides, its binding prediction result reaches the state of the art. The experiment also reveals the network is appropriate for most MHC-I with transfer learning. Conclusions We design the MAM network to extract the motifs from MHC-peptides binding through prediction, which are proved to generate the peptides with high binding affinity successfully. The new peptides preserve the motifs but vary in sequences.

Haplotype phasing is indispensable to study human genetics. The pervasiveness of large copy number variant segments in solid tumors brings possibilities to resolve long germline phasing blocks utilizing allele imbalance in tumor data. Although there exist such studies, none of them provide easy-use software based on availability and usability. Herein, we present a novel tool, CNAHap, to determine the allele-specific copy number in tumor and then phase germline variants according to the imbalanced alleles in tumor genomes. We also provide interactive web interfaces to visualize the copy number and phase landscape from CNAHap. On in silico datasets, CNAHap demonstrates higher allele-specific copy number calling accuracy than the benchmark tool and generates long phasing blocks. As a case study on Hepatocellular carcinoma, CNAHap successfully generates huge phase blocks with the averages of N50 and N90 as 25M and 7M, respectively, and finds the Olfactory receptor family is recurrent amplified. Our results illustrate the efficacy of CNAHap in determining tumor allele-specific copy numbers and their long germline haplotypes. CNAHap is available at https://github.com/bowentan/CNAHap and the CNAHap visualization web interfaces are hosted at bio.oviz.org.

Donkeys (Equus asinus) are important livestock with great economic value in meat, skin, and milk production. However, a lack of knowledge of the transcriptome landscape across a wide range of donkey tissues limits genetic selective breeding and conservation. Here we used transcriptomics to describe the transcriptome landscape, classify the tissue-specific gene expression across all primary donkey tissues, and present supplementary analyses on the protein level of additional donkey milk samples. Overall, 16,013 protein-coding genes and 21,983 transcripts were mapped to the reference genome, including 6,778 ubiquitously expressed genes and 2,601 tissue-enriched genes. Functional analysis revealed that the function of the tissue-enriched genes was highly tissue specific. Tissue-elevated genes that could be associated with unique phenotypes in donkey were analyzed. The results showed that, compared with those in human and other livestock, the lysozyme gene in donkey breast was specifically and highly expressed. The calcium-binding lysozyme, encoded by the lysozyme gene, was also detected in high amounts in donkey milk. Given those intact lysozyme genes that predict potentially functional calcium-binding lysozyme found in only a few species (e.g., donkey and horse), the high expression of the lysozyme gene in donkey breast may contribute to the high lysozyme content in donkey milk. Furthermore, 71% of the proteins in donkey milk overlapped with human milk protein, higher than the overlapping rates of bovine, sheep, and swine with humans. The donkey transcriptomic resource contributes to the available genomic resources to interpret the molecular mechanisms underlying phenotype traits.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Although genome-wide association studies (GWAS) identify the risk ADHD-associated variants and genes with significant P-values, they may neglect the combined effect of multiple variants with insignificant P-values. Here, we proposed a convolutional neural network (CNN) to classify 1033 individuals diagnosed with ADHD from 950 healthy controls according to their genomic data. The model takes the single nucleotide polymorphism (SNP) loci of P-values \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\le{1\times 10^{-3}}$\end{document}, i.e. 764 loci, as inputs, and achieved an accuracy of 0.9018, AUC of 0.9570, sensitivity of 0.8980 and specificity of 0.9055. By incorporating the saliency analysis for the deep learning network, a total of 96 candidate genes were found, of which 14 genes have been reported in previous ADHD-related studies. Furthermore, joint Gene Ontology enrichment and expression Quantitative Trait Loci analysis identified a potential risk gene for ADHD, EPHA5 with a variant of rs4860671. Overall, our CNN deep learning model exhibited a high accuracy for ADHD classification and demonstrated that the deep learning model could capture variants’ combining effect with insignificant P-value, while GWAS fails. To our best knowledge, our model is the first deep learning method for the classification of ADHD with SNPs data.

Oncogenic KRAS mutations are considered to be a key driver for initiation and progression in non-small-cell lung cancer (NSCLC). However, how post-translational modifications (PTMs) of KRAS, especially methylation, modify KRAS activity and downstream signals remain largely unclear. Here, we showed that SET domain containing histone lysine methyltransferase 7 (SETD7) interacts with KRAS and methylates KRAS at lysine 182 and 184. SETD7-mediated methylation of KRAS led to degradation of KRAS and attenuation of the RAS/MEK/ERK signaling cascade, endowing SETD7 with a potent tumor-suppressive role in NSCLC, both in vitro and in vivo. Mechanistically, RABGEF1, a ubiquitin E3 ligase of KRAS, was recruited and promoted KRAS degradation in a K182/K184 methylation-dependent manner. Notably, low SETD7 or RABGEF1 expression was associated with poor prognosis in lung adenocarcinoma patients. Taken together, our results establish a novel connection between lysine methylation and KRAS protein stability, in addition to elucidating a tumor-suppressive function of SETD7 that operates via modulation of oncogenic RAS signaling.

Topologically associating domains (TADs) are the organizational units of chromosome structures. TADs can contain TADs, thus forming a hierarchy. TAD hierarchies can be inferred from Hi-C data through coding trees. However, the current method for computing coding trees is not optimal. In this paper, we propose optimal algorithms for this computation. In comparison with seven state-of-art methods using two public datasets, from GM12878 and IMR90 cells, SuperTAD shows a significant enrichment of structural proteins around detected boundaries and histone modifications within TADs and displays a high consistency between various resolutions of identical Hi-C matrices. Supplementary Information The online version contains supplementary material available at (10.1186/s13059-020-02234-6).

Background Horizontal Gene Transfer (HGT) refers to the sharing of genetic materials between distant species that are not in a parent-offspring relationship. The HGT insertion sites are important to understand the HGT mechanisms. Recent studies in main agents of HGT, such as transposon and plasmid, demonstrate that insertion sites usually hold specific sequence features. This motivates us to find a method to infer HGT insertion sites according to sequence features. Results In this paper, we propose a deep residual network, DeepHGT, to recognize HGT insertion sites. To train DeepHGT, we extracted about 1.55 million sequence segments as training instances from 262 metagenomic samples, where the ratio between positive instances and negative instances is about 1:1. These segments are randomly partitioned into three subsets: 80% of them as the training set, 10% as the validation set, and the remaining 10% as the test set. The training loss of DeepHGT is 0.4163 and the validation loss is 0.423. On the test set, DeepHGT has achieved the area under curve (AUC) value of 0.8782. Furthermore, in order to further evaluate the generalization of DeepHGT, we constructed an independent test set containing 689,312 sequence segments from another 147 gut metagenomic samples. DeepHGT has achieved the AUC value of 0.8428, which approaches the previous test AUC value. As a comparison, the gradient boosting classifier model implemented in PyFeat achieve an AUC value of 0.694 and 0.686 on the above two test sets, respectively. Furthermore, DeepHGT could learn discriminant sequence features; for example, DeepHGT has learned a sequence pattern of palindromic subsequences as a significantly (P-value=0.0182) local feature. Hence, DeepHGT is a reliable model to recognize the HGT insertion site. Conclusion DeepHGT is the first deep learning model that can accurately recognize HGT insertion sites on genomes according to the sequence pattern.

Background The initialization of the neonatal gut microbiota (GM) is affected by diverse factors and is associated with infant development and health outcomes. Methods In this study, we collected 207 faecal samples from 41 infants at 6 time points (1, 3, and 7 days and 1, 3, and 6 months after birth). The infants were assigned to four groups according to delivery mode (caesarean section (CS) or vaginal delivery (VD)) and feeding pattern (breastfeeding or formula milk). Results The meconium bacterial diversity was slightly higher in CS than in VD. Three GM patterns were identified, including Escherichia/Shigella-Streptococcus-dominated, Bifidobacterium-Escherichia/Shigella-dominated and Bifidobacterium-dominated patterns, and they gradually changed over time. In CS infants, Bifidobacterium was less abundant, and the delay in GM establishment could be partially restored by breastfeeding. The frequency of respiratory tract infection and diarrhoea consequently decreased. Conclusion This study fills some gaps in the understanding of the restoration of the GM in CS towards that in VD.

Background The human microbiota are complex systems with important roles in our physiological activities and diseases. Sequencing the microbial genomes in the microbiota can help in our interpretation of their activities. The vast majority of the microbes in the microbiota cannot be isolated for individual sequencing. Current metagenomics practices use short-read sequencing to simultaneously sequence a mixture of microbial genomes. However, these results are in ambiguity during genome assembly, leading to unsatisfactory microbial genome completeness and contig continuity. Linked-read sequencing is able to remove some of these ambiguities by attaching the same barcode to the reads from a long DNA fragment (10–100 kb), thus improving metagenome assembly. However, it is not clear how the choices for several parameters in the use of linked-read sequencing affect the assembly quality. Results We first examined the effects of read depth (C) on metagenome assembly from linked-reads in simulated data and a mock community. The results showed that C positively correlated with the length of assembled sequences but had little effect on their qualities. The latter observation was corroborated by tests using real data from the human gut microbiome, where C demonstrated minor impact on the sequence quality as well as on the proportion of bins annotated as draft genomes. On the other hand, metagenome assembly quality was susceptible to read depth per fragment (CR) and DNA fragment physical depth (CF). For the same C, deeper CR resulted in more draft genomes while deeper CF improved the quality of the draft genomes. We also found that average fragment length (μFL) had marginal effect on assemblies, while fragments per partition (NF/P) impacted the off-target reads involved in local assembly, namely, lower NF/P values would lead to better assemblies by reducing the ambiguities of the off-target reads. In general, the use of linked-reads improved the assembly for contig N50 when compared to Illumina short-reads, but not when compared to PacBio CCS (circular consensus sequencing) long-reads. Conclusions We investigated the influence of linked-read sequencing parameters on metagenome assembly comprehensively. While the quality of genome assembly from linked-reads cannot rival that from PacBio CCS long-reads, the case for using linked-read sequencing remains persuasive due to its low cost and high base-quality. Our study revealed that the probable best practice in using linked-reads for metagenome assembly was to merge the linked-reads from multiple libraries, where each had sufficient CR but a smaller amount of input DNA.

The cutaneous microbiota responses to skin health as well as atopic dermatitis. To reveal the microbiota effect in atopic dermatitis children under therapy with topical corticosteroids and antibiotics. 59 atopic dermatitis patients were randomized to two treatment groups (by corticosteroids or combination therapy) in Beijing Children's Hospital. The lesion microbial samples were collected for 16S rDNA sequencing and bioinformatics analysis. After treatment, 57 patients recovered significantly. Though topical antibiotics application blocked the restoration of commensal Streptococcus, no remarkable differences of cutaneous microbiota were identified between the two groups along the treatment. In subject 1081, who received the combination therapy, the Streptococcus and Pseudomonas as well as Chryseobacterium increased dramatically. On the contrary, the Staphylococcus aureus decreased sharply in subject 1107 with topical corticosteroids treatment Our preliminary study suggested the necessity to consider cutaneous microbiota profile when prescribing antibiotics.

The marine ciliate Mesodinium rubrum is famous for its ability to acquire and exploit chloroplasts and other cell organelles from some cryptophyte algal species. We sequenced genomes and transcriptomes of free-swimming Teleaulax amphioxeia, as well as well-fed and starved M. rubrum in order to understand cellular processes upon sequestration under different prey and light conditions. From its prey, the ciliate acquires the ability to photosynthesize as well as the potential to metabolize several essential compounds including lysine, glycan, and vitamins that elucidate its specific prey dependency. M. rubrum does not express photosynthesis related genes itself, but elicits considerable transcriptional control of the acquired cryptophyte organelles. This control is limited as light dependent transcriptional changes found in free-swimming T. amphioxeia got lost after sequestration. We found strong transcriptional rewiring of the cryptophyte nucleus upon sequestration, where 35% of the T. amphioxeia genes were significantly differentially expressed within well-fed M. rubrum. Qualitatively, 68% of all genes expressed within well-fed M. rubrum originated from T. amphioxeia. Quantitatively, these genes contributed up to 48% to the global transcriptome in well-fed M. rubrum and down to 11% in starved M. rubrum. This tertiary endosymbiosis system functions for several weeks, when deprived of prey. After this point in time, the ciliate dies if not supplied with fresh prey cells. M. rubrum represents one evolutionary way of acquiring photosystems from its algal prey, and might represent a step on the evolutionary way towards a permanent tertiary endosymbiosis.

Background: Atopic dermatitis (AD) is a common cutaneous disease, associated with imbalances in the skin microbiota. Objective: To explore the characteristics of the cutaneous microbiota and its dynamic changes during clinical treatment. Methods: Cutaneous swab samples were collected from 51 AD patients before treatment, and 40 AD patients remained after a 2-week treatment with mometasone and mupirocin. Results: AD patients exhibited significant enrichments of Prevotella and Desulfovibrio as well as obvious reductions of Corynebacterium, Streptococcus and Parabacteroides. Based on the proportion of Staphylococcus aureus, the AD patients were further classified into S. aureus-predominant group (AD.S) and S. aureus-non-dominant (AD.ND) group. The AD.S group exhibited lower skin microbial diversity and higher atopic dermatitis (SCORAD) index. In the AD.S group, the cutaneous microbial diversity significantly increased from 2.9 ± 0.8 to 3.7 ± 1.0, while the relative abundance of S. aureus decreased from 42.5 ± 20.7 to 10.3 ± 28.4 after treatment. In contrast, no significant skin microbiota changes were detected in the AD.ND group. Conclusions: AD patients with predominant S. aureus had higher disease severity and lower microbiota diversity compared to patients in the AD.ND group. Mometasone and mupirocin therapy had significant effects on skin microbiota in AD.S patients, but had a paradoxical response in the AD.ND patients.

The SARS-CoV-2 virus has infected more than one million people worldwide to date. Knowing its genome and gene expressions is essential to understand the virus’ mechanism. Here, we propose a computational tool CovProfile to detect the viral genomic variations as well as viral gene expressions from the sequences obtained from Nanopore devices. We applied CovProfile to 11 samples, each from a terminally ill patient, and discovered that all the patients are infected by multiple viral strains, which might affect the reliability of phylogenetic analysis. Moreover, the expression of viral genes ORF1ab gene, S gene, M gene, and N gene are high among most of the samples. While performing the tests, we noticed a consistent abundance of transcript segments of MUC5B, presumably from the host, across all the samples.

Summary Every human somatic cell inherits a maternal and a paternal genome, which work together to give rise to cellular phenotypes. However, the allele-specific relationship between gene expression and genome structure through the cell cycle is largely unknown. By integrating haplotype-resolved genome-wide chromosome conformation capture, mature and nascent mRNA, and protein binding data from a B lymphoblastoid cell line, we investigate this relationship both globally and locally. We introduce the maternal and paternal 4D Nucleome, enabling detailed analysis of the mechanisms and dynamics of genome structure and gene function for diploid organisms. Our analyses find significant coordination between allelic expression biases and local genome conformation, and notably absent expression bias in universally essential cell cycle and glycolysis genes. We propose a model in which coordinated biallelic expression reflects prioritized preservation of essential gene sets., Graphical abstract, Highlights • We discover allele-specific relationships in genome architecture and gene expression • Integration of data modalities reveals an allele-specific 4D Nucleome • Allelic expression and conformation biases correspond to Pol II recruitment biases • We introduce a novel haplotype phasing algorithm, HaploHiC, Genomics; Bioinformatics; Genomic analysis

Colorectal cancer (CRC) causes high morbidity and mortality worldwide, and noninvasive gut microbiome (GM) biomarkers are promising for early CRC diagnosis. However, the GM varies significantly based on ethnicity, diet and living environment, suggesting varied GM biomarker performance in different regions. We performed a metagenomic association analysis on stools from 52 patients and 55 corresponding healthy family members who lived together to identify GM biomarkers for CRC in Chongqing, China. The GM of patients differed significantly from that of healthy controls. A total of 22 microbial genes were included as screening biomarkers with high accuracy in additional 46 cases and 40 randomly selected healthy adults in Chongqing (area under the receive-operation curve (AUC) = 0.905, 95% CI 0.832-0.977). The classifier based on the identified 22 biomarkers also performed well in the cohort from Hong Kong (AUC = 0.811, 95% CI 0.715-0.907) and French (AUC = 0.859, 95% CI 0.773-0.944) populations. Quantitative PCR was applied for measuring three selected biomarkers in the classification of CRC patients in independent Chongqing population containing 30 cases and 30 controls and the best biomarker from Coprobacillus performed well with high AUC (0.930, 95% CI 0.904-0.955). This study revealed increased sensitivity and applicability of our GM biomarkers compared with previous biomarkers significantly promoting the early diagnosis of CRC.

Current knowledge about the evolutionary history of donkeys is still incomplete due to the lack of archeological and whole-genome diversity data. To fill this gap, we have de novo assembled a chromosome-level reference genome of one male Dezhou donkey and analyzed the genomes of 126 domestic donkeys and seven wild asses. Population genomics analyses indicate that donkeys were domesticated in Africa and conclusively show reduced levels of Y chromosome variability and discordant paternal and maternal histories, possibly reflecting the consequences of reproductive management. We also investigate the genetic basis of coat color. While wild asses show diluted gray pigmentation (Dun phenotype), domestic donkeys display non-diluted black or chestnut coat colors (non-Dun) that were probably established during domestication. Here, we show that the non-Dun phenotype is caused by a 1 bp deletion downstream of the TBX3 gene, which decreases the expression of this gene and its inhibitory effect on pigment deposition., This work was supported by the National Natural Science Foundation of China (grant nos. 31671287 and 31601007), Agricultural Science and Technology Innovation Project of Shandong Academy of Agricultural Sciences (grant no. CXGC2016C02), Well-bred Program of Shandong Province (grant no. 2017LZGC020), Joint Innovation Funds of Dong E. E. Jiao and Shandong Academy of Agricultural Sciences, Taishan Leading Industry Talents-Agricultural Science of Shandong Province (grant no. LJNY201713), and Shandong Province Modern Agricultural Technology System Donkey Industrial Innovation Team (grant no. SDAIT-27).

The marine ciliate Mesodinium rubrum is famous for its ability to acquire and exploit chloroplasts and other cell organelles from some cryptophyte algal species. We sequenced genomes and transcriptomes of free-swimming Teleaulax amphioxeia, as well as well-fed and starved M. rubrum in order to understand cellular processes upon sequestration under different prey and light conditions. From its prey, the ciliate acquires the ability to photosynthesize as well as the potential to metabolize several essential compounds including lysine, glycan, and vitamins that elucidate its specific prey dependency. M. rubrum does not express photosynthesis-related genes itself, but elicits considerable transcriptional control of the acquired cryptophyte organelles. This control is limited as light-dependent transcriptional changes found in free-swimming T. amphioxeia got lost after sequestration. We found strong transcriptional rewiring of the cryptophyte nucleus upon sequestration, where 35% of the T. amphioxeia genes were significantly differentially expressed within well-fed M. rubrum. Qualitatively, 68% of all genes expressed within well-fed M. rubrum originated from T. amphioxeia. Quantitatively, these genes contributed up to 48% to the global transcriptome in well-fed M. rubrum and down to 11% in starved M. rubrum. This tertiary endosymbiosis system functions for several weeks, when deprived of prey. After this point in time, the ciliate dies if not supplied with fresh prey cells. M. rubrum represents one evolutionary way of acquiring photosystems from its algal prey, and might represent a step on the evolutionary way towards a permanent tertiary endosymbiosis.

Whole-exome sequencing has been successful in identifying genetic factors contributing to familial or sporadic Parkinson’s disease (PD). However, this approach has not been applied to explore the impact of de novo mutations on PD pathogenesis. Here, we sequenced the exomes of 39 early onset patients, their parents, and 20 unaffected siblings to investigate the effects of de novo mutations on PD. We identified 12 genes with de novo mutations (MAD1L1, NUP98, PPP2CB, PKMYT1, TRIM24, CEP131, CTTNBP2, NUS1, SMPD3, MGRN1, IFI35, and RUSC2), which could be functionally relevant to PD pathogenesis. Further analyses of two independent case-control cohorts (1,852 patients and 1,565 controls in one cohort and 3,237 patients and 2,858 controls in the other) revealed that NUS1 harbors significantly more rare nonsynonymous variants (P = 1.01E-5, odds ratio = 11.3) in PD patients than in controls. Functional studies in Drosophila demonstrated that the loss of NUS1 could reduce the climbing ability, dopamine level, and number of dopaminergic neurons in 30-day-old flies and could induce apoptosis in fly brain. Together, our data suggest that de novo mutations could contribute to early onset PD pathogenesis and identify NUS1 as a candidate gene for PD.

Background Short tandem repeats (STRs) serve as genetic markers in forensic scenes due to their high polymorphism in eukaryotic genomes. A variety of STRs profiling systems have been developed for species including human, dog, cat, cattle, etc. Maintaining these systems simultaneously can be costly. These mammals share many high similar regions along their genomes. With the availability of the massive amount of the whole genomics data of these species, it is possible to develop a unified STR profiling system. In this study, our objective is to propose and develop a unified set of STR loci that could be simultaneously applied to multiple species. Result To find a unified STR set, we collected the whole genome sequence data of the concerned species and mapped them to the human genome reference. Then we extracted the STR loci across the species. From these loci, we proposed an algorithm which selected a subset of loci by incorporating the optimized combined power of discrimination. Our results show that the unified set of loci have high combined power of discrimination, >1−10−9, for both individual species and the mixed population, as well as the random-match probability, −7 for all the involved species, indicating that the identified set of STR loci could be applied to multiple species. Conclusions We identified a set of STR loci which shared by multiple species. It implies that a unified STR profiling system is possible for these species under the forensic scenes. The system can be applied to the individual identification or paternal test of each of the ten common species which are Sus scrofa (pig), Bos taurus (cattle), Capra hircus (goat), Equus caballus (horse), Canis lupus familiaris (dog), Felis catus (cat), Ovis aries (sheep), Oryctolagus cuniculus (rabbit), and Bos grunniens (yak), and Homo sapiens (human). Our loci selection algorithm employed a greedy approach. The algorithm can generate the loci under different forensic parameters and for a specific combination of species.

Single-cell RNA-seq studies profile thousands of cells in developmental processes. Current databases for human single-cell expression atlas only provide search and visualize functions for a selected gene in specific cell types or subpopulations. These databases are limited to technical properties or visualization of single-cell RNA-seq data without considering the biological relations of their collected cell groups. Here, we developed a database to investigate single-cell gene expression profiling during different developmental pathways (SCDevDB). In this database, we collected 10 human single-cell RNA-seq datasets, split these datasets into 176 developmental cell groups, and constructed 24 different developmental pathways. SCDevDB allows users to search the expression profiles of the interested genes across different developmental pathways. It also provides lists of differentially expressed genes during each developmental pathway, T-distributed stochastic neighbor embedding maps showing the relationships between developmental stages based on these differentially expressed genes, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analysis results of these differentially expressed genes. This database is freely available at https://scdevdb.deepomics.org.

Motivation: When a temperate bacteriophage (phage) integrates into the chromosome of its host or to remain as latent episomal DNA, the prophage plays an important role in bacterial virulence acquisition and increase. With the recent release of thousands of bacterial high throughput sequencing data, there has been a growing interest in analyzing the impact of interdependency between a prophage and its host. One of the essential tasks is to acquire and identify the complete sequence of the prophage, i.e., the functional prophages. However, no existing tools can identify functional prophages from bacterial genomes. A few tools can predict putative prophage in bacterial genomes, but they are unable to distinguish functional prophages from defective prophages. To reduce the cost and to relieve the tedious biological experiments for functional prophage analysis, computational methods for predicting functional prophages based on bacterial high-throughput sequencing data would be an effective alternation. Results: This paper presents a method, LysoPhD, the first tool to predict functional prophage sequences using bacterial HTS data automatically and accurately. The main idea is to obtain whole genome of functional prophages from the bacterial genome based on phage gene annotation and reads-mediated contig connection. LysoPhD obtains whole genome sequences of functional prophages based on improved models. The obtained the sequences are verified by wet lab experiments. Specifically, LysoPhD is applied to a set of staphylococcus HTS data in the case studies. Ten functional prophages are predicted from 72 staphylococcus isolates. The 72 bacterial strains are then induced with Mitomycin C and subsequent deep sequencing verified of the 10 functional prophages. This shows that LysoPhD is a sensitive and accurate program for functional prophage prediction.

Cetaceans (whales, dolphins, and porpoises) are a group of mammals adapted to various aquatic habitats, from oceans to freshwater rivers. We report the sequencing, de novo assembly and analysis of a finless porpoise genome, and the re-sequencing of an additional 48 finless porpoise individuals. We use these data to reconstruct the demographic history of finless porpoises from their origin to the occupation into the Yangtze River. Analyses of selection between marine and freshwater porpoises identify genes associated with renal water homeostasis and urea cycle, such as urea transporter 2 and angiotensin I-converting enzyme 2, which are likely adaptations associated with the difference in osmotic stress between ocean and rivers. Our results strongly suggest that the critically endangered Yangtze finless porpoises are reproductively isolated from other porpoise populations and harbor unique genetic adaptations, supporting that they should be considered a unique incipient species., Whales, dolphins and porpoises are adapted to various aquatic habitats. Here, Zhou et al. show that polymorphisms associated with renal function and the urea cycle have undergone selection in the freshwater Yangtze finless porpoise and provide genomic evidence of incipient speciation.

Haplotype phasing is essential to study diploid eukaryotic organisms. High-throughput sequencing, including next-generation sequencing and third-generation sequencing from different technologies, brings possibilities for haplotype assembly. Although there exist multiple haplotype phasing algorithms, only a few are portable across sequencing technologies with the premise of efficiency and accuracy. Herein, we proposed SpecHap, a novel haplotype assembly tool that leverages spectral graph theory, transforming haplotype phasing into an algebraic problem. On both in silico and whole-genome-sequencing datasets, SpecHap consumed less memory and required less CPU time, yet achieved comparable accuracy comparing to state-of-art methods across all the test instances of next-generation sequencing, linked-reads, high-throughput chromosome conformation capture sequencing, PacBio single-molecule real-time sequencing and Oxford Nanopore long-reads sequencing data. Furthermore, SpecHap successfully phased an individual Ambystoma mexicanumm, a species with gigantic diploid genomes, within 6 CPU hours and 945MB peak memory usage, while other tools failed to yield results either due to a memory overflow (40GB) or a time limit excess (5 days). Our results demonstrated that SpecHap is scalable, efficient and accurate for diploid phasing, supporting diverse sequencing platforms.

Published by the Combined DNA Index System (CODIS) program of the Federal Bureau of Investigation (FBI) in 1997, the 13 core short tandem repeat (STR) loci are widely adopted as genetic markers in forensic applications, e.g., identity testing and paternity testing. However, these loci may be biased and suffer from reduced sensitivities toward specific population groups. In addition, the rapid growth of entries in forensic databases raises the chance of random hits, which can cause false recognitions of criminal suspects. A solution to these problems is to introduce more effective STR markers. The availability of whole genome sequencing enables us to identify more reliable STRmarkers for forensic applicationscomputationally. In this paper, we proposed an algorithm to identify STR markers with high discriminative abilities from the next-generation sequencing data. Our algorithm could select a customized set of loci for a given population with pre-specified discriminative thresholds. We have applied the method to 320 Chinese individuals from the 1000 Genomes Project and obtained various numbers of loci, which were able to statistically identify an individualworldwide and had higher combined powers of discrimination and combined probabilities of exclusion than the existing CODIS 13 loci. For identity testing, themean frequencyofDNAprofile (FDP) with the selected 11 STRs was smaller than that with CODIS 13 STRs by student's t-test. With more loci, much smaller FDPs were obtained. The databasematching probabilities for selected loci were also lower than that for CODIS 13 STRs in a database with 10 billion entries. Moreover, the selected loci were able to provide considerably low chance of random profile matches so that statistically no false positives could occur. The selected loci also reduced the risk of random allele matches when doing the familial search, with lower random allele matching probabilities. In addition, the selected STRs were statistically better than CODIS STRs for paternity testing in our simulated data, with lower probabilities of false inclusions and exclusions.

Background Genome rearrangement describes gross changes of chromosomal regions, plays an important role in evolutionary biology and has profound impacts on phenotype in organisms ranging from microbes to humans. With more and more complete genomes accomplished, lots of genomic comparisons have been conducted in order to find genome rearrangements and the mechanisms which underlie the rearrangement events. In our opinion, genomic comparison of different individuals/strains within the same species (pan-genome) is more helpful to reveal the mechanisms for genome rearrangements since genomes of the same species are much closer to each other. Results We study the mechanism for inversion events via core-genome scaffold comparison of different strains within the same species. We focus on two kinds of bacteria, Pseudomonas aeruginosa and Escherichia coli, and investigate the inversion events among different strains of the same species. We find an interesting phenomenon that long (larger than 10,000 bp) inversion regions are flanked by a pair of Inverted Repeats (IRs). This mechanism can also explain why the breakpoint reuses for inversion events happen. We study the prevalence of the phenomenon and find that it is a major mechanism for inversions. The other observation is that for different rearrangement events such as transposition and inverted block interchange, the two ends of the swapped regions are also associated with repeats so that after the rearrangement operations the two ends of the swapped regions remain unchanged. To our knowledge, this is the first time such a phenomenon is reported for transposition event. Conclusions In both Pseudomonas aeruginosa and Escherichia coli strains, IRs were found at the two ends of long sequence inversions. The two ends of the inversion remained unchanged before and after the inversion event. The existence of IRs can explain the breakpoint reuse phenomenon. We also observed that other rearrangement operations such as transposition, inverted transposition, and inverted block interchange, had repeats (not necessarily inverted) at the ends of each segment, where the ends remained unchanged before and after the rearrangement operations. This suggests that the conservation of ends could possibly be a popular phenomenon in many types of chromosome rearrangement events. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3655-0) contains supplementary material, which is available to authorized users.

Cerebral palsy (CP) and epilepsy are two interactive neurological diseases, and their clinical treatment can cause severe side-effects in children's development, especially when it involves long-term administration of antiepileptic drugs. Accumulating studies on the gut-brain axis indicated that the gut microbiota (GM), which participates in various neurological diseases, would provide a harmless therapeutic target for the treatment of CP and epilepsy. To explore the GM characteristics in children with both CP and epilepsy (CPE), we collected fecal samples from 25 CPE patients (CPE group) and 21 healthy children (Healthy group) for 16S rDNA sequencing. In this study, we discovered significantly higher microbial diversity in the CPE group compared to healthy group (P < 0.001). After selecting the top 15 most abundant genera in each group, we found significantly enriched Bifidobacterium, Streptococcus, Akkermansia, Enterococcus, Prevotella, Veillonella, Rothia, and Clostridium IV in the CPE group, and noticeably reduced Bacteroides, Faecalibacterium, Blautia, Ruminococcus, Roseburia, Anaerostipes, and Parasutterella. A GM co-occurrence network was also constructed, and negative correlations were discovered between Bacteroides and Lactobacillus (r = −0.768, P < 0.001, FDR < 0.001), as well as Intestinibacter and Bifidobacterium (r = −0.726, P < 0.001, FDR < 0.001). After KEGG annotation and functional enrichment, 24 functional categories exhibited different enrichment levels between the CPE and Healthy groups. The functions, associated with xenobiotics metabolism, immune system diseases, and neurodegenerative diseases, were enriched in the CPE group. Conversely, the functional categories related to the biosynthesis of secondary metabolites were reduced. Furthermore, the neurodegenerative diseases were mainly attributed to Streptococcus, while an increased risk of immune system diseases was associated with enriched Akkermansia in the CPE patients. Generally, this study characterized the GM in CPE patients, illustrated the microbial co-occurrence relationships, and detected the functional distributions of the bacteria.

Single-cell RNA-sequencing (scRNA-seq) is essential for the study of cell-specific transcriptome landscapes. The scRNA-seq techniques capture merely a small fraction of the gene due to “dropout” events. When analyzing with scRNA-seq data, the dropout events receive intensive attentions. Imputation tools are proposed to estimate the values of the dropout events and de-noise the data. To evaluate the imputation tools, researchers have developed different clustering criteria by incorporating the ground-truth cell subgroup labels. There lack measurements without cell subgroup knowledge. A reliable imputation tool should follow the “self-consistency” principle; that is, the tool reports the results only if it finds no further errors or dropouts from the data. Here, we propose “self-consistency” as an explicit evaluation criterion; also, we propose I-Impute, a “self-consistent” method, to impute scRNA-seq data. I-Impute lever-ages continuous similarities and dropout probabilities and refines the data iteratively to make the final output self-consistent. On the in silico data sets, I-Impute exhibited the highest Pearson correlations for different dropout rates consistently compared with the state-of-art methods SAVER and scImpute. On the datasets of 90.87%, 70.98% and 56.65% zero rates, I-Impute exhibited the correlations as 0.78, 0.90, and 0.94, respectively, between ground truth entries and predicted values, while SAVER exhibited the correlations as 0.58, 0.79 and 0.88, respectively and scImpute exhibited correlations as 0.65, 0.86, and 0.93, respectively. Furthermore, we collected three wetlab datasets, mouse bladder cells dataset, embryonic stem cells dataset, and aortic leukocyte cells dataset, to evaluate the tools. I-Impute exhibited feasible cell subpopulation discovery efficacy on all the three datasets. It achieves the highest clustering accuracy compared with SAVER and scImpute; that is, I-Impute displayed the adjusted Rand indices of the three datasets as 0.61, 0.7, 0.52, which improved the indices of SAVER by 0.01 to 0.17, and improved the indices of scImpute by 0.19 to 0.4. Also, I-impute promoted normalized mutual information of the three datasets by 0.01 to 0.09 comparing with SAVER, and by 0.15 to 0.34 comparing with scImpute. I-Impute exhibits robust imputation ability and follows the “self-consistency” principle. It offers perspicacity to uncover the underlying cell subtypes in real scRNA-Seq data. Source code of I-Impute can be accessed at https://github.com/xikanfeng2/I-Impute.

The gut microbiota (GM) is related to obesity and other metabolic diseases. To detect GM markers for obesity in patients with different metabolic abnormalities and investigate their relationships with clinical indicators, 1,914 Chinese adults were enrolled for 16S rRNA gene sequencing in this retrospective study. Based on GM composition, Random forest classifiers were constructed to screen the obesity patients with (Group OA) or without metabolic diseases (Group O) from healthy individuals (Group H), and high accuracies were observed for the discrimination of Group O and Group OA (areas under the receiver operating curve (AUC) equal to 0.68 and 0.76, respectively). Furthermore, six GM markers were shared by obesity patients with various metabolic disorders (Bacteroides, Parabacteroides, Blautia, Alistipes, Romboutsia and Roseburia). As for the discrimination with Group O, Group OA exhibited low accuracy (AUC = 0.57). Nonetheless, GM classifications to distinguish between Group O and the obese patients with specific metabolic abnormalities were not accurate (AUC values from 0.59 to 0.66). Common biomarkers were identified for the obesity patients with high uric acid, high serum lipids and high blood pressure, such as Clostridium XIVa, Bacteroides and Roseburia. A total of 20 genera were associated with multiple significant clinical indicators. For example, Blautia, Romboutsia, Ruminococcus2, Clostridium sensu stricto and Dorea were positively correlated with indicators of bodyweight (including waistline and body mass index) and serum lipids (including low density lipoprotein, triglyceride and total cholesterol). In contrast, the aforementioned clinical indicators were negatively associated with Bacteroides, Roseburia, Butyricicoccus, Alistipes, Parasutterella, Parabacteroides and Clostridium IV. Generally, these biomarkers hold the potential to predict obesity-related metabolic abnormalities, and interventions based on these biomarkers might be beneficial to weight loss and metabolic risk improvement.

BackgroundThe imbalanced respiratory microbiota observed in pneumonia causes high morbidity and mortality in childhood. Respiratory metagenomic analysis demands a comprehensive microbial gene catalogue, which will significantly advance our understanding of host–microorganism interactions.ResultsWe collected 334 respiratory microbial samples from 171 healthy children and 76 children with pneumonia. The respiratory microbial gene catalogue we established comprised 2.25 million non-redundant microbial genes, covering 90.52% of prevalent genes. The major oropharyngeal microbial species found in healthy children were Prevotella and Streptococcus. In children with Mycoplasma pneumoniae pneumonia (MPP), oropharyngeal microbial diversity and associated gene numbers decreased compared with those of healthy children. The concurrence network of oropharyngeal microorganisms in patients predominantly featured Staphylococcus spp. and M. pneumoniae. Functional orthologues, which are associated with the metabolism of various lipids, membrane transport, and signal transduction, accumulated in the oropharyngeal microbiome of children with pneumonia. Several antibiotic resistance genes and virulence factor genes were identified in the genomes of M. pneumoniae and 13 other microorganisms reconstructed via metagenomic data. Although the common macrolide/β-lactam resistance genes were not identified in the assembled M. pneumoniae genome, a single-nucleotide polymorphism (A2063G) related to macrolide resistance was identified in a 23S ribosomal RNA gene.ConclusionsThe results of this study will facilitate exploration of unknown microbial components and host–microorganism interactions in studies of the respiratory microbiome. They will also yield further insights into the microbial aetiology of MPP.

IPW (imprinted gene in the Prader-Willi syndrome region), a long non-coding RNA, is a paternally expressed gene in the PWS/AS imprinted domain on human chromosome 15 and mouse chromosome 7. Disruption of the PWS/AS region is associated with three neurogenic disorders in humans. In this study, we identified the bovine homolog of the IPW gene; multiple transcripts obtained by RT-PCR and RACE showed a complex and tissue-specific expression pattern of IPW in the brain, heart, kidney, liver, lung, spleen and skeletal muscle. An informative single nucleotide polymorphism (rs133341090) in the long exon H was identified by sequencing the genomic DNA, and mono-allelic expression of IPW was confirmed by sequencing the cDNAs of heterozygous individuals, indicating that IPW may be imprinted in cattle. The protein-coding potential of IPW transcripts was assessed using coding potential calculator (cpc) software, which showed a negative score. In addition, sequencing analysis also indicated multiple small open reading frames in the bovine IPW transcript, but none of the ATGs was consistent with Kozak consensus. Taken together, the IPW transcripts are most likely long non-coding RNAs.

Recently, the prevalence and importance of RNA editing have been illuminated in mammals. However, studies on RNA editing of pigs, a widely used biomedical model animal, are rare. Here we collected RNA sequencing data across 11 tissues and identified more than 490,000 RNA editing sites. We annotated their biological features, detected flank sequence characteristics of A-to-I editing sites and the impact of A-to-I editing on miRNA&ndash, mRNA interactions, and identified RNA editing quantitative trait loci (edQTL). Sus scrofa RNA editing sites showed high enrichment in repetitive regions with a median editing level as 15.38%. Expectedly, 96.3% of the editing sites located in non-coding regions including intron, 3&prime, UTRs, intergenic, and gene proximal regions. There were 2233 editing sites located in the coding regions and 980 of them caused missense mutation. Our results indicated that to an A-to-I editing site, the adjacent four nucleotides, two before it and two after it, have a high impact on the editing occurrences. A commonly observed editing motif is CCAGG. We found that 4552 A-to-I RNA editing sites could disturb the original binding efficiencies of miRNAs and 4176 A-to-I RNA editing sites created new potential miRNA target sites. In addition, we performed edQTL analysis and found that 1134 edQTLs that significantly affected the editing levels of 137 RNA editing sites. Finally, we constructed PRESDB, the first pig RNA editing sites database. The site provides necessary functions associated with Sus scrofa RNA editing study.

Elysia chlorotica, a sacoglossan sea slug found off the East Coast of the United States, is well-known for its ability to sequester chloroplasts from its algal prey and survive by photosynthesis for up to 12 months in the absence of food supply. Here we present a draft genome assembly of E. chlorotica that was generated using a hybrid assembly strategy with Illumina short reads and PacBio long reads. The genome assembly comprised 9,989 scaffolds, with a total length of 557 Mb and a scaffold N50 of 442 kb. BUSCO assessment indicated that 93.3% of the expected metazoan genes were completely present in the genome assembly. Annotation of the E. chlorotica genome assembly identified 176 Mb (32.6%) of repetitive sequences and a total of 24,980 protein-coding genes. We anticipate that the annotated draft genome assembly of the E. chlorotica sea slug will promote the investigation of sacoglossan genetics, evolution, and particularly, the genetic signatures accounting for the long-term functioning of algal chloroplasts in an animal. Machine-accessible metadata file describing the reported data (ISA-Tab format)