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Jia, P.

Publications and source records attributed to Jia, P..

33 records · Page 2Linked to original sources

Quartet DNA reference materials and datasets for comprehensively evaluating germline variants calling performance

Current methods for evaluating the accuracy of germline variant calls are restricted to easy-to-detect high-confidence regions, thus ignoring a substantial portion of difficult variants beyond the benchmark regions. We established four DNA reference materials from immortalized cell lines derived from a Chinese Quartet including parents and monozygotic twins. We integrated benchmark calls of 4.2 million small variants and 15,000 structural variants from multiple platforms and bioinformatic pipelines for evaluating the reliability of germline variant calls inside the benchmark regions. The genetic built-in-truth of the Quartet family design not only improved sensitivity of benchmark calls by removing additional false positive variants with apparently high quality, but also enabled estimation of the precision of variants calls outside the benchmark regions. Batch effects of variant calling in large-scale DNA sequencing efforts can be effectively identified with the concurrent use of the Quartet DNA reference materials along with study samples, and can be alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Matched RNA and protein reference materials were also established in the Quartet project, thereby enabling benchmark calls constructed from DNA reference materials for evaluation of variants calling performance on RNA and protein data. The Quartet DNA reference materials from this study are a resource for objective and comprehensive assessment of the accuracy of germline variant calls throughout the whole-genome regions.

bioinformatics↗

Haplotype-resolved assemblies and variant benchmark of a Chinese Quartet

As the state-of-the-art sequencing technologies and computational methods enable investigation of challenging regions in the human genome, an update variant benchmark is demanded. Herein, we sequenced a Chinese Quartet, consisting of two monozygotic twin daughters and their biological parents, with multiple advanced sequencing platforms, including Illumina, BGI, PacBio, and Oxford Nanopore Technology. We phased the long reads of the monozygotic twin daughters into paternal and maternal haplotypes using the parent-child genetic map. For each haplotype, we utilized advanced long reads to generate haplotype-resolved assemblies (HRAs) with high accuracy, completeness, and continuity. Based on the ingenious quartet samples, novel computational methods, high-quality sequencing reads, and HRAs, we established a comprehensive variant benchmark, including 3,883,283 SNVs, 859,256 Indels, 9,678 large deletions, 15,324 large insertions, 40 inversions, and 31 complex structural variants shared between the monozygotic twin daughters. In particular, the preciously excluded regions, such as repeat regions and the human leukocyte antigen (HLA) region, were systematically examined. Finally, we illustrated how the sequencing depth correlated with the de novo assembly and variant detection, from which we learned that 30 x HiFi is a balance between performance and cost. In summary, this study provides high-quality haplotype-resolved assemblies and a variant benchmark for two Chinese monozygotic twin samples. The benchmark expanded the regions of the previous report and adapted to the evolving sequencing technologies and computational methods.

genomics↗

Comparison and benchmark of long-read based structural variant detection strategies

BackgroundRecent advances in long-read callers and assembly methods have greatly facilitated structural variants (SV) detection via read-based and assembly-based detection strategies. However, the lack of comparison studies, especially for SVs at complex genomic regions, complicates the selection of proper detection strategy for ever-increasing demand of SV analysis. ResultsIn this study, we compared the two most widely-used strategies with six long-read datasets of HG002 genome and benchmarked them with well curated SVs at genomic regions of different complexity. First of all, our results suggest that SVs detected by assembly-based strategy are slightly affected by assemblers on HiFi datasets, especially for its breakpoint identity. Comparably, though read-based strategy is more versatile to different sequencing settings, aligners greatly affect SV breakpoints and type. Furthermore, our comparison reveals that 70% of the assembly-based calls are also detectable by read-based strategy and it even reaches 90% for SVs at high confident regions. While 60% of the assembly-based calls that are totally missed by read-based callers is largely due to the challenges of clustering ambiguous SV signature reads. Lastly, benchmarking with SVs at complex genomic regions, our results show that assembly-based approach outperforms read-based calling with at least 20X coverage, while read-based strategy could achieve 90% recall even with 5X coverage. ConclusionsTaken together, with sufficient sequencing coverage, assembly-based strategy is able to detect SVs more consistently than read-based strategy under different settings. However, read-based strategy could detect SVs at complex regions with high sensitivity and specificity but low coverage, thereby suggesting its great potential in clinical application.

bioinformatics↗

A single-cell atlas of the human brain in Alzheimer's disease and its implications for personalized drug repositioning

Alzheimers disease (AD) is a neurodegenerative disease with complex pathophysiology, and AD-dysregulated pathways are inconsistent across different brain regions and patients. Although single-cell RNA sequencing (scRNA-seq) has been performed in different regions of postmortem AD brains, the common and distinct molecular features among different regions remains largely unclear. This hinders the discovery of repurposable and personalized drugs for AD. We combined four scRNA-seq datasets and systematically investigated the common and distinct cellular responses, cell subpopulations, and transcription factors involved in AD. Moreover, we explored the transcriptional heterogeneity of different AD subtypes at the single-cell level. Finally, we conducted individual-based drug repurposing analysis to explore repurposable and personalized drugs. Six major brain cell types were detected after scRNA-seq batch-effect removal and noise cells filtering. Integration with genome-wide association studies (GWAS) summary statistics demonstrated that AD-susceptible genes were mainly enriched with differentially expressed genes (DEGs) in glial cells rather than neuronal cells. While most of DEGs were regulated in opposite directions among different cell types, cell-cell communication analysis revealed several common cellular interaction events involved in neurogenesis, as well as increased cell-cell adhesion. Our comprehensive drug repositioning analysis identified new candidates for AD treatment, including trichostatin, which was predicted to be broadly applicable to different identified AD subtypes, and vorinostat, which was specific for one subtype of AD. In summary, we delineated a cell-specific atlas of the AD transcriptome. Our work illustrated strong cellular heterogeneity in AD for defining AD subtypes. The cell-specific features are important for understanding AD etiology, progression, and drug discovery.

bioinformatics↗

Long read sequencing reveals sequential complex rearrangements driven by Hepatitis B virus integration

Integration of Hepatitis B virus (HBV) into human genome disrupts genetic structures and cellular functions. Here, we conducted multiplatform long read sequencing on two cell lines and five clinical samples of HBV-infected hepatocellular carcinomas (HCC). We resolved three types of viral integration-induced complex genome rearrangements (CGR) and proposed a model of multi-hits and sequential-breaks to depict their formation process by differentiating inserted HBV copies with HiFi long reads. We deduced that all three complex types were initialized from focal replacement and fragile virus-human junctions triggered subsequent rearrangements. We further revealed that such rearrangements caused a prevalent loss-of-heterozygosity at chr4q, accounting for 19.5% of HCC samples in ICGC cohort and contributing to immune and metabolic dysfunction. Overall, our long read based analysis reveals novel sequential rearrangement processes initiated by HBV integration, hinting its structural and functional impact on HCC.

genomics↗

deCS: A tool for systematic cell type annotations of single-cell RNA sequencing data among human tissues

Single-cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex and dynamic cellular mechanisms. However, cell-type annotation remains a main challenge as it largely relies on a priori knowledge and manual curation, which is cumbersome and less accurate. The increasing number of scRNA-seq data sets, as well as numerous published genetic studies, motivated us to build a comprehensive human cell type reference atlas. Here, we present deCS (decoding Cell type-Specificity), an automatic cell type annotation method augmented by a comprehensive collection of human cell type expression profiles and marker genes. We used deCS to annotate scRNA-seq data from various tissue types and systematically evaluated the annotation accuracy under different conditions, including reference panels, sequencing depth and feature selection strategies. Our results demonstrated that expanding the references is critical for improving annotation accuracy. Compared to many existing state-of-the-art annotation tools, deCS significantly reduced computation time and increased accuracy. deCS can be integrated into the standard scRNA-seq analytical pipeline to enhance cell type annotation. Finally, we demonstrated the broad utility of deCS to identify trait-cell type associations in 51 human complex traits, providing deeper insights into the cellular mechanisms of disease pathogenesis. All documents, including source code, user manual, demo data, and tutorials, are freely available at https://github.com/bsml320/deCS.

bioinformatics↗

High-quality Arabidopsis thaliana genome assembly with Nanopore and HiFi long reads

Arabidopsis thaliana is an important and long-established model species for plant molecular biology, genetics, epigenetics, and genomics. However, the latest version of reference genome still contains significant number of missing segments. Here, we report a high-quality and almost complete Col-0 genome assembly with two gaps (Col-XJTU) using combination of Oxford Nanopore Technology ultra-long reads, PacBio high-fidelity long reads, and Hi-C data. The total genome assembly size is 133,725,193 bp, introducing 14.6 Mb of novel sequences compared to the TAIR10.1 reference genome. All five chromosomes of Col-XJTU assembly are highly accurate with consensus quality (QV) scores > 60 (ranging from 62 to 68), which are higher than those of TAIR10.1 reference (QV scores ranging from 45 to 52). We have completely resolved chromosome (Chr) 3 and Chr5 in a telomere-to-telomere manner. Chr4 has been completely resolved except the nucleolar organizing regions, which comprise long repetitive DNA fragments. The Chr1 centromere (CEN1), reportedly around 9 Mb in length, is particularly challenging to assemble due to the presence of tens of thousands of CEN180 satellite repeats. Using the cutting-edge sequencing data and novel computational approaches, we assembled about 4 Mb of sequence for CEN1 and a 3.5-Mb-long CEN2. We investigated the structure and epigenetics of centromeres. We detected four clusters of CEN180 monomers, and found that the centromere-specific histone H3-like protein (CENH3) exhibits a strong preference for CEN180 cluster 3. Moreover, we observed hypomethylation patterns in CENH3-enriched regions. We believe that this high-quality genome assembly, Col-XJTU, would serve as a valuable reference to better understand the global pattern of centromeric polymorphisms, as well as genetic and epigenetic features in plants.

genomics↗

Association of CXCR6 with COVID-19 severity: Delineating the host genetic factors in transcriptomic regulation

BackgroundThe coronavirus disease 2019 (COVID-19) is an infectious disease that mainly affects the host respiratory system with [~]80% asymptomatic or mild cases and [~]5% severe cases. Recent genome-wide association studies (GWAS) have identified several genetic loci associated with the severe COVID-19 symptoms. Delineating the genetic variants and genes is important for better understanding its biological mechanisms. MethodsWe implemented integrative approaches, including transcriptome-wide association studies (TWAS), colocalization analysis and functional element prediction analysis, to interpret the genetic risks using two independent GWAS datasets in lung and immune cells. To understand the context-specific molecular alteration, we further performed deep learning-based single cell transcriptomic analyses on a bronchoalveolar lavage fluid (BALF) dataset from moderate and severe COVID-19 patients. ResultsWe discovered and replicated the genetically regulated expression of CXCR6 and CCR9 genes. These two genes have a protective effect on the lung and a risk effect on whole blood, respectively. The colocalization analysis of GWAS and cis-expression quantitative trait loci highlighted the regulatory effect on CXCR6 expression in lung and immune cells. In the lung resident memory CD8+ T (TRM) cells, we found a 3.32-fold decrease of cell proportion and lower expression of CXCR6 in the severe than moderate patients using the BALF transcriptomic dataset. Pro-inflammatory transcriptional programs were highlighted in TRM cells trajectory from moderate to severe patients. ConclusionsCXCR6 from the 3p21.31 locus is associated with severe COVID-19. CXCR6 tends to have a lower expression in lung TRM cells of severe patients, which aligns with the protective effect of CXCR6 from TWAS analysis. We illustrate one potential mechanism of host genetic factor impacting the severity of COVID-19 through regulating the expression of CXCR6 and TRM cell proportion and stability. Our results shed light on potential therapeutic targets for severe COVID-19.

genetics↗

MSIsensor-pro: fast, accurate and matched-normal-sample-free detection of microsatellite instability

We developed MSIsensor-pro (https://github.com/xjtu-omics/msisensor-pro), an open-source single sample microsatellite instability (MSI) scoring method for research and clinical applications. MSIsensor-pro introduces a multinomial distribution model to quantify polymerase slippages for each tumor sample and a discriminative sites selection method to enable MSI detection without matched normal samples. For samples of various sequencing depths and tumor purities, MSIsensor-pro significantly outperformed the current leading methods in terms of both accuracy and computational cost.

bioinformatics↗

The Long Pentraxin 3 (PTX3) Suppresses Immunity to Cutaneous Leishmaniasis by Negatively Regulating Th17 Response

The long Pentraxin 3 (PTX3), a soluble pattern recognition molecule, plays a critical role in inflammation, tissue repair and wound healing. Here, we show that PTX3 regulates disease pathogenesis in cutaneous leishmaniasis (CL). PTX3 expression is increased in active skin lesions in patients and mice during CL, with higher levels being expressed in individuals with severe disease. PTX3 deficient (PTX3-/-) mice were highly resistant to L. major infection and the enhanced resistance was associated with increased IL-17 response. Neutralization of IL-17A abolished this enhanced resistance while treatment with recombinant PTX3 resulted in reduced IL-17A response and increased susceptibility to L. major infection. Naive CD4+ T cells from PTX3-/- mice displayed increased differentiation into Th17 cells, which was reversed in the presence of recombinant PTX3. The enhanced Th17 response observed in PTX3-/- cells was associated with increased Leishmania specific IL-6 production from dendritic cells along with enhanced expression of Th17-specific transcription factors including ROR{gamma}t, AhR and STAT3. Addition of recombinant PTX3 significantly inhibited the expression of Th17-specific transcription factors and dramatically reduced the frequency of Th17 cells in Th17-polarizing cultures of PTX3-/- CD4+ T cells. Collectively, our results show that PTX3 contributes to the pathogenesis of CL by suppressing Th17 differentiation and IL-17A production.\n\nAuthor SummaryCutaneous leishmaniasis (CL) is caused by several species of Leishmania. Currently, there is no approved vaccine against human CL because of the poor understanding of the mechanisms that regulate disease pathogenesis and correlates of protective immunity. Because the long pentraxin 3 (PTX3, a soluble pattern recognition molecule that forms an integral part of the host innate immunity), regulates inflammation and tissue repair, which are critical physiological events associated with resolution of skin lesions during CL, we investigated its role in disease pathogenesis.\n\nHere, we show that PTX3 levels were elevated in skin-lesions in patients and mice during CL. Using a loss of function approach, we showed that PTX3 contributes to pathogenesis, and this was associated with increased IL-17A responses. Neutralization and recombinant cytokine treatment studies showed that the increased resistance of PTX3 deficient mice to L. major is due to enhanced Th17 response in these mice. We further show that PTX3 negatively regulates IL-6 production by dendritic cells and the expression of IL-17A-specific transcription factors (including ROR{gamma}T, STAT3, IRF4, BATF and AhR) in CD4+ T cells. Collectively, these findings show that PTX3 is a negative regulator of Th17 response and protective immunity during L. major infection.

immunology↗

Tissue-Specific Enrichment Analysis (TSEA) to decode tissue specificity

MotivationDiseases and traits are under dynamic tissue-specific regulation. However, heterogeneous tissues are often collected in biomedical studies, which reduce the power in the identification of disease-associated variants and gene expression profiles.\n\nResultsWe present TSEA, an R package to conduct Tissue-Specific Enrichment Analysis (TSEA) with two built-in reference panels. Statistical methods are developed and implemented for detecting tissue-specific genes and for enrichment test of different forms of query data. Our applications using multi-trait genome-wide association data and cancer expression data showed that TSEA could effectively identify the most relevant tissues for each query trait or sample, providing insights for future studies.\n\nAvailabilityhttps://github.com/bsml320/TSEA\n\nContactpeilin.jia@uth.tmc.edu or zhongming.zhao@uth.tmc.edu

bioinformatics↗

A computational protocol to characterize elusive Candidate Phyla Radiation bacteria in oral environments using metagenomic data

Several studies have documented the diversity and potential pathogenic associations of organisms in the human oral cavity. Although much progress has been made in understanding the complex bacterial community inhabiting the human oral cavity, our understanding of some microorganisms is less resolved due to a variety of reasons. One such little-understood group is the candidate phyla radiation (CPR), which is a recently identified, but highly abundant group of ultrasmall bacteria with reduced genomes and unusual ribosomes. Here, we present a computational protocol for the detection of CPR organisms from metagenomic data. Our approach relies on a self-constructed dataset comprising published CPR genomic sequences as a filter to identify CPR sequences from metagenomic sequencing data. After assembly and functional prediction, the taxonomic affiliation of CPR contigs can be identified through phylogenetic analysis with publically available 16S rRNA gene and ribosomal proteins, in addition to sequence similarity analyses (e.g., average nucleotide identity calculations and contig mapping). Using this protocol, we reconstructed two draft genomes of organisms within the TM7 superphylum, that had genome sizes of 0.594 Mb and 0.678 Mb. Among the predicted functional genes of the constructed genomes, a high percentage were related to signal transduction, cell motility, and cell envelope biogenesis, which could contribute to cellular morphological changes in response to environmental cues.\n\nImportanceCandidate phyla radiation (CPR) bacterial group is a recently identified, but highly diverse and abundant group of ultrasmall bacteria exhibiting reduced genomes and limited metabolic capacities. A number of studies have reported their potential pathogenic associations in multiple mucosal diseases including periodontitis, halitosis, and inflammatory bowel disease. However, CPR organisms are difficult to cultivate and are difficult to detect with PCR-based methods due to divergent genetic sequences. Thus, our understanding of CPR has lagged behind that of other bacterial component. Here, we used metagenomic approaches to overcome these previous barriers to CPR identification, and established a computational protocol for detection of CPR organisms from metagenomic samples. The protocol describe herein holds great promise for better understanding the potential biological functioning of CPR. Moreover, the pipeline could be applied to other organisms that are difficult to cultivate.

bioinformatics↗

Differential DNA modification of an enhancer at the IGF2 locus affects dopamine synthesis in patients with major psychosis

Dopamine dysregulation is central to the pathogenesis of diseases with major psychosis, but its molecular origins are unclear. In an epigenome-wide investigation in neurons, individuals with schizophrenia and bipolar disorder showed reduced DNA modifications at an enhancer in IGF2, which disrupted the regulation of the dopamine synthesis enzyme tyrosine hydroxylase and striatal dopamine levels in transgenic mice. Epigenetic control of this enhancer may be an important molecular determinant of psychosis.

neuroscience↗

Gene2Vec: Distributed Representation of Genes Based on Co-Expression

Existing functional description of genes are categorical, discrete, and mostly through manual process. In this work, we explore the idea of gene embedding, distributed representation of genes, in the spirit of word embedding. From a pure data-driven fashion, we trained a 300 dimension vector representation of all human genes, using gene co-expression patterns in 984 data sets from the GEO databases. These vectors capture functional relatedness of genes in terms of recovering known pathways - the average inner product (similarity) of genes within a pathway is 1.68X greater than that of random genes. Using t-SNE, we produced a gene co-expression map that shows local concentrations of tissue specific genes. We also illustrated the usefulness of the embedded gene vectors, laden with rich information on gene co-expression patterns, in tasks such as gene-gene interaction prediction. Overall, we believe that this distributed representation of genes may be useful for more bioinformatics applications.

bioinformatics↗

scRNASeqDB: a database for gene expression profiling in human single cell by RNA-seq

Summary: Single-cell RNA sequencing (scRNA-Seq) is quickly becoming a powerful tool for high-throughput transcriptomic analysis of cell states and dynamics. Both the number and quality of scRNA-Seq datasets have dramatically increased recently. So far, there is no database that comprehensively collects and curates scRNA-Seq data in humans. Here, we present scRNASeqDB, a database that includes almost all the currently available human single cell transcriptome datasets (n= 36) covering 71 human cell lines or types and 8910 samples. Our online web interface allows user to query and visualize expression profiles of the gene(s) of interest, search for genes that are expressed in different cell types or groups, or retrieve differentially expressed genes between cell types or groups. The scRNASeqDB is a valuable resource for single cell transcriptional studies.\n\nAvailability: The database is available at https://bioinfo.uth.edu/scrnaseqdb/.\n\nContact: zhongming.zhao@uth.tmc.edu

bioinformatics↗