Jia Liu

Bio: Jia Liu is an academic researcher. The author has contributed to research in topics: Genome & Medicine. The author has an hindex of 4, co-authored 4 publications receiving 1623 citations.

Human Genome Sequencing Using Unchained Base Reads on Self-Assembling DNA Nanoarrays

Abstract: Genome sequencing of large numbers of individuals promises to advance the understanding, treatment, and prevention of human diseases, among other applications. We describe a genome sequencing platform that achieves efficient imaging and low reagent consumption with combinatorial probe anchor ligation chemistry to independently assay each base from patterned nanoarrays of self-assembling DNA nanoballs. We sequenced three human genomes with this platform, generating an average of 45- to 87-fold coverage per genome and identifying 3.2 to 4.5 million sequence variants per genome. Validation of one genome data set demonstrates a sequence accuracy of about 1 false variant per 100 kilobases. The high accuracy, affordable cost of $4400 for sequencing consumables, and scalability of this platform enable complete human genome sequencing for the detection of rare variants in large-scale genetic studies.

Accurate whole-genome sequencing and haplotyping from 10 to 20 human cells.

Abstract: Recent advances in whole-genome sequencing have brought the vision of personal genomics and genomic medicine closer to reality. However, current methods lack clinical accuracy and the ability to describe the context (haplotypes) in which genome variants co-occur in a cost-effective manner. Here we describe a low-cost DNA sequencing and haplotyping process, long fragment read (LFR) technology, which is similar to sequencing long single DNA molecules without cloning or separation of metaphase chromosomes. In this study, ten LFR libraries were made using only ∼100 picograms of human DNA per sample. Up to 97% of the heterozygous single nucleotide variants were assembled into long haplotype contigs. Removal of false positive single nucleotide variants not phased by multiple LFR haplotypes resulted in a final genome error rate of 1 in 10 megabases. Cost-effective and accurate genome sequencing and haplotyping from 10–20 human cells, as demonstrated here, will enable comprehensive genetic studies and diverse clinical applications. A new DNA analysis method termed long fragment read technology is described, and the approach is used to determine parental haplotypes and to sequence human genomes cost-effectively and accurately from only 10 to 20 cells. Many of the hoped-for advances in the field of personalized medicine are dependent on the development of low-cost genome-sequencing technology that combines clinical accuracy with the ability to describe the context (the genetic haplotype) in which variants occur on an individual chromosome. The technique described here, termed long-fragment read technology, is similar to that used to sequence long single DNA molecules, but without DNA cloning or chromosome separation. The authors demonstrate the potential of this approach by generating seven accurate human genome sequences, as well as haplotype data, from samples containing just 10–20 cells. This advance shows that it should be possible to achieve clinical quality and scale in personal genome sequencing of microbiopsies and circulating cancer cells.

Quantitative Whole Genome Sequencing of Circulating Tumor Cells Enables Personalized Combination Therapy of Metastatic Cancer

Abstract: Much effort has been dedicated to developing circulating tumor cells (CTC) as a noninvasive cancer biopsy, but with limited success as yet. In this study, we combine a method for isolation of highly pure CTCs using immunomagnetic enrichment/fluorescence-activated cell sorting with advanced whole genome sequencing (WGS), based on long fragment read technology, to illustrate the utility of an accurate, comprehensive, phased, and quantitative genomic analysis platform for CTCs. Whole genomes of 34 CTCs from a patient with metastatic breast cancer were analyzed as 3,072 barcoded subgenomic compartments of long DNA. WGS resulted in a read coverage of 23× per cell and an ensemble call rate of >95%. These barcoded reads enabled accurate detection of somatic mutations present in as few as 12% of CTCs. We found in CTCs a total of 2,766 somatic single-nucleotide variants and 543 indels and multi-base substitutions, 23 of which altered amino acid sequences. Another 16,961 somatic single nucleotide variant and 8,408 indels and multi-base substitutions, 77 of which were nonsynonymous, were detected with varying degrees of prevalence across the 34 CTCs. On the basis of our whole genome data of mutations found in all CTCs, we identified driver mutations and the tissue of origin of these cells, suggesting personalized combination therapies beyond the scope of most gene panels. Taken together, our results show how advanced WGS of CTCs can lead to high-resolution analyses of cancers that can reliably guide personalized therapy. Cancer Res; 77(16); 4530-41. ©2017 AACR.

Co-barcoded sequence reads from long DNA fragments: a cost-effective solution for “perfect genome” sequencing

Abstract: Next generation sequencing (NGS) technologies, primarily based on massively parallel sequencing, have touched and radically changed almost all aspects of research worldwide. These technologies have allowed for the rapid analysis, to date, of the genomes of more than 2,000 different species. In humans, NGS has arguably had the largest impact. Over 100,000 genomes of individual humans (based on various estimates) have been sequenced allowing for deep insights into what makes individuals and families unique and what causes disease in each of us. Despite all of this progress, the current state of the art in sequence technology is far from generating a “perfect genome” sequence and much remains to be understood in the biology of human and other organisms’ genomes. In the article that follows, we outline why the “perfect genome” in humans is important, what is lacking from current human whole genome sequences, and a potential strategy for achieving the “perfect genome” in a cost effective manner.

Immune mechanism of low bone mineral density caused by ankylosing spondylitis based on bioinformatics and machine learning

Abstract: Background and Objective: This study aims to find the key immune genes and mechanisms of low bone mineral density (LBMD) in ankylosing spondylitis (AS) patients. Methods: AS and LBMD datasets were downloaded from the GEO database, and differential expression gene analysis was performed to obtain DEGs. Immune-related genes (IRGs) were obtained from ImmPort. Overlapping DEGs and IRGs got I-DEGs. Pearson coefficients were used to calculate DEGs and IRGs correlations in the AS and LBMD datasets. Louvain community discovery was used to cluster the co-expression network to get gene modules. The module most related to the immune module was defined as the key module. Metascape was used for enrichment analysis of key modules. Further, I-DEGs with the same trend in AS and LBMD were considered key I-DEGs. Multiple machine learning methods were used to construct diagnostic models based on key I-DEGs. IID database was used to find the context of I-DEGs, especially in the skeletal system. Gene–biological process and gene-pathway networks were constructed based on key I-DEGs. In addition, immune infiltration was analyzed on the AS dataset using the CIBERSORT algorithm. Results: A total of 19 genes were identified I-DEGs, of which IFNAR1, PIK3CG, PTGER2, TNF, and CCL3 were considered the key I-DEGs. These key I-DEGs had a good relationship with the hub genes of key modules. Multiple machine learning showed that key I-DEGs, as a signature, had an excellent diagnostic performance in both AS and LBMD, and the SVM model had the highest AUC value. Key I-DEGs were closely linked through bridge genes, especially in the skeletal system. Pathway analysis showed that PIK3CG, IFNAR1, CCL3, and TNF participated in NETs formation through pathways such as the MAPK signaling pathway. Immune infiltration analysis showed neutrophils had the most significant differences between case and control groups and a good correlation with key I-DEG. Conclusion: The key I-DEGs, TNF, CCL3, PIK3CG, PTGER2, and IFNAR1, can be utilized as biomarkers to determine the risk of LBMD in AS patients. They may affect neutrophil infiltration and NETs formation to influence the bone remodeling process in AS.

The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data

Abstract: Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

A framework for variation discovery and genotyping using next-generation DNA sequencing data

Abstract: Recent advances in sequencing technology make it possible to comprehensively catalogue genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (1) initial read mapping; (2) local realignment around indels; (3) base quality score recalibration; (4) SNP discovery and genotyping to find all potential variants; and (5) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We discuss the application of these tools, instantiated in the Genome Analysis Toolkit (GATK), to deep whole-genome, whole-exome capture, and multi-sample low-pass (~4×) 1000 Genomes Project datasets.

An integrated map of genetic variation from 1,092 human genomes

Abstract: By characterizing the geographic and functional spectrum of human genetic variation, the 1000 Genomes Project aims to build a resource to help to understand the genetic contribution to disease. Here we describe the genomes of 1,092 individuals from 14 populations, constructed using a combination of low-coverage whole-genome and exome sequencing. By developing methods to integrate information across several algorithms and diverse data sources, we provide a validated haplotype map of 38 million single nucleotide polymorphisms, 1.4 million short insertions and deletions, and more than 14,000 larger deletions. We show that individuals from different populations carry different profiles of rare and common variants, and that low-frequency variants show substantial geographic differentiation, which is further increased by the action of purifying selection. We show that evolutionary conservation and coding consequence are key determinants of the strength of purifying selection, that rare-variant load varies substantially across biological pathways, and that each individual contains hundreds of rare non-coding variants at conserved sites, such as motif-disrupting changes in transcription-factor-binding sites. This resource, which captures up to 98% of accessible single nucleotide polymorphisms at a frequency of 1% in related populations, enables analysis of common and low-frequency variants in individuals from diverse, including admixed, populations.

Sequencing technologies-the next generation

Abstract: Demand has never been greater for revolutionary technologies that deliver fast, inexpensive and accurate genome information. This challenge has catalysed the development of next-generation sequencing (NGS) technologies. The inexpensive production of large volumes of sequence data is the primary advantage over conventional methods. Here, I present a technical review of template preparation, sequencing and imaging, genome alignment and assembly approaches, and recent advances in current and near-term commercially available NGS instruments. I also outline the broad range of applications for NGS technologies, in addition to providing guidelines for platform selection to address biological questions of interest.