基于高通量测序技术的微生物检测数据分析方法

高通量测序技术的发展正在逐渐改变诸多生物学领域的研究方法.为应对突发疫情以及新发未知微生物威胁的需求,微生物鉴定技术逐渐从传统的物理化学方法及核酸杂交等分子水平方法进一步走向利用无需培养的测序数据进行快速分析检测.随之而来的是对高通量数据分析在精度及速度的要求.基于高通量测序数据的微生物检测数据分析方法在近些年得到了快速的发展.本文分析了目前基于高通量测序数据的微生物检测数据分析方法,对其数据分析的处理流程和计算方法进行了研究,比较了各个微生物检测数据分析方法的特点及适用场景.最后结合本实验室工作总结微生物检测数据分析方法在实际应用中可能遇到的问题,希望对该应用领域的研究有一定的参考意义.     

利用二代测序技术高通量分析水稻品种间抗病性差异的分子基础

稻瘟病和白叶枯病是由稻温病菌(Magnaporthe oryzae)和白叶枯病菌(Xanthomonas oryzae pv.oryzae)引起的两种主要水稻病害,也是制约中国水稻生产的主要病害。为了从DNA水平探索造成水稻感病品种‘丽江新团黑谷’(LTH)和高抗品种‘特特普’(Tetep,TTP)间抗病性差异的分子基础,该研究对其已知的3个抗稻瘟病基因和3个抗白叶枯基因所在DNA区段分别进行PCR扩增,将等量混合的PCR产物再与基因组重测序样品按Ct值差值(ΔCt)~10的比例混合,采用二代测序技术进行一次性测序和比较分析,并对有差异的基因区域进行常规传统测序验证,以确定这2个品种中抗性基因(R基因)的数目和结构与品种抗病或感病表型的关联性。实验结果表明,二代测序能够快速并准确地寻找到2个不同水稻品种中多个特定基因的序列差异,且差异位点与常规测序结果相符。从LTH和TTP这2种抗性不同水稻品种在多个抗性基因的DNA水平差异来看,有差异的抗性基因位点在高抗品种TTP中大都与原始抗性基因序列相同,而对应的普感品种LTH的抗性基因往往多表现为氨基酸突变,这些序列差异很可能就是导致TTP与LTH抗性差异的分子基础。     

distAngsd: Fast and Accurate Inference of Genetic Distances for Next-Generation Sequencing Data

Commonly used methods for inferring phylogenies were designed before the emergence of high-throughput sequencing and can generally not accommodate the challenges associated with noisy, diploid sequencing data. In many applications, diploid genomes are still treated as haploid through the use of ambiguity characters; while the uncertainty in genotype calling—arising as a consequence of the sequencing technology—is ignored. In order to address this problem, we describe two new probabilistic approaches for estimating genetic distances: distAngsd-geno and distAngsd-nuc, both implemented in a software suite named distAngsd. These methods are specifically designed for next-generation sequencing data, utilize the full information from the data, and take uncertainty in genotype calling into account. Through extensive simulations, we show that these new methods are markedly more accurate and have more stable statistical behaviors than other currently available methods for estimating genetic distances—even for very low depth data with high error rates.     

DDBJ Read Annotation Pipeline: A Cloud Computing-Based Pipeline for High-Throughput Analysis of Next-Generation Sequencing Data

High-performance next-generation sequencing (NGS) technologies are advancing genomics and molecular biological research. However, the immense amount of sequence data requires computational skills and suitable hardware resources that are a challenge to molecular biologists. The DNA Data Bank of Japan (DDBJ) of the National Institute of Genetics (NIG) has initiated a cloud computing-based analytical pipeline, the DDBJ Read Annotation Pipeline (DDBJ Pipeline), for a high-throughput annotation of NGS reads. The DDBJ Pipeline offers a user-friendly graphical web interface and processes massive NGS datasets using decentralized processing by NIG supercomputers currently free of charge. The proposed pipeline consists of two analysis components: basic analysis for reference genome mapping and de novo assembly and subsequent high-level analysis of structural and functional annotations. Users may smoothly switch between the two components in the pipeline, facilitating web-based operations on a supercomputer for high-throughput data analysis. Moreover, public NGS reads of the DDBJ Sequence Read Archive located on the same supercomputer can be imported into the pipeline through the input of only an accession number. This proposed pipeline will facilitate research by utilizing unified analytical workflows applied to the NGS data. The DDBJ Pipeline is accessible at http://p.ddbj.nig.ac.jp/.     

Comparison of Next-Generation Sequencing and Polymerase Chain Reaction for Personalized Treatment-Related Genomic Status in Patients with Metastatic Colorectal Cancer

Personalized treatments based on the genetic profiles of tumors can simultaneously optimize efficacy and minimize toxicity, which is beneficial for improving patient outcomes. This study aimed to integrate gene alterations associated with predictive and prognostic outcomes in patients with metastatic colorectal cancer (mCRC) with polymerase chain reaction (PCR) and in-house next-generation sequencing (NGS) to detect KRAS, NRAS, and BRAF mutations. In the present study, 41 patients with mCRC were assessed between August 2017 and June 2019 at a single institution. The overall concordance between NGS and PCR results for detecting KRAS, NRAS, and BRAF mutations was considerably high (87.8–92.7%), with only 15 discrepant results between PCR and NGS. Our companion diagnostic test analyzes KRAS, NRAS, and BRAF as a panel of CRC molecular targets; therefore, it has the advantages of requiring fewer specimens and being more time and cost efficient than conventional testing for separate analyses, allowing for the simultaneous analysis of multiple genes.     

Next-Generation Sequencing (NGS) in COVID-19: A Tool for SARS-CoV-2 Diagnosis,Monitoring New Strains and Phylodynamic Modeling in Molecular Epidemiology

This review discusses the current testing methodologies for COVID-19 diagnosis and explores next-generation sequencing (NGS) technology for the detection of SARS-CoV-2 and monitoring phylogenetic evolution in the current COVID-19 pandemic. The review addresses the development, fundamentals, assay quality control and bioinformatics processing of the NGS data. This article provides a comprehensive review of the obstacles and opportunities facing the application of NGS technologies for the diagnosis, surveillance, and study of SARS-CoV-2 and other infectious diseases. Further, we have contemplated the opportunities and challenges inherent in the adoption of NGS technology as a diagnostic test with real-world examples of its utility in the fight against COVID-19.     

Quantifying Population Genetic Differentiation from Next-Generation Sequencing Data

Over the past few years, new high-throughput DNA sequencing technologies have dramatically increased speed and reduced sequencing costs. However, the use of these sequencing technologies is often challenged by errors and biases associated with the bioinformatical methods used for analyzing the data. In particular, the use of naïve methods to identify polymorphic sites and infer genotypes can inflate downstream analyses. Recently, explicit modeling of genotype probability distributions has been proposed as a method for taking genotype call uncertainty into account. Based on this idea, we propose a novel method for quantifying population genetic differentiation from next-generation sequencing data. In addition, we present a strategy for investigating population structure via principal components analysis. Through extensive simulations, we compare the new method herein proposed to approaches based on genotype calling and demonstrate a marked improvement in estimation accuracy for a wide range of conditions. We apply the method to a large-scale genomic data set of domesticated and wild silkworms sequenced at low coverage. We find that we can infer the fine-scale genetic structure of the sampled individuals, suggesting that employing this new method is useful for investigating the genetic relationships of populations sampled at low coverage.     

Using Next-Generation Sequencing and Bioinformatic Methods to Predict New Genes That May Be Regulated by CD47 in Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) is one of the most common cancers in the world, and the incidence and death rate of OSCC in men is twice that of women. CD47 is a ubiquitous cell surface transmembrane protein, also known as integrin-related protein (IAP). Previous studies have pointed out that CD47 can inhibit the growth of OSCC, but the detailed mechanism is not clear. This study aimed to explore the effect of CD47 gene expression profiles in OSCC. The OSCC cell lines, OECM-1 and OC-2, overexpressed CD47, and the expression profiles of mRNAs were analyzed through next-generation sequencing (NGS) with a bioinformatic approach. A total of 14 differentially expressed genes (DEGs) were listed. In addition, ingenuity pathway analysis (IPA) was used to analyze the molecular function (MF), biological process (BP), and cellular component (CC) network signaling. The human protein atlas (HPA) database was used to analyze gene expression and the survivability of human cancer. The results found that HSPA5, HYOU1, and PDIA4 were involved in the IPA network and when highly expressed, mediated the survivability of cancer. In addition, HSPA5 was positively and significantly correlated with CD47 expression (p < 0.0001) and induced by CD47-overexpression in the OECM-1 and OC-2 OSCC cancer cell lines. These findings provide important insights into possible new diagnostic strategies, including unfolded protein for OSCC-targeting CD47.     

Sequencing Strategies for Fusion Gene Detection

Fusion genes formed by chromosomal rearrangements are common drivers of cancer. Recent innovations in the field of next-generation sequencing (NGS) have seen a dynamic shift from traditional fusion detection approaches, such as visual characterization by fluorescence, to more precise multiplexed methods. There are many different NGS-based approaches to fusion gene detection and deciding on the most appropriate method can be difficult. Beyond the experimental approach, consideration needs to be given to factors such as the ease of implementation, processing time, associated costs, and the level of expertise required for data analysis. Here, the different NGS-based methods for fusion gene detection, the basic principles underlying the techniques, and the benefits and limitations of each approach are reviewed. This article concludes with a discussion of how NGS will impact fusion gene detection in a clinical context and from where the next innovations are evolving.     

高通量测序技术在动植物研究领域中的应用

高通量测序是核酸测序研究的一次革命性技术创新, 该技术以极低的单碱基测序成本和超高的数据产出量为特征, 为基因组学和后基因组学研究带来了新的科研方法和解决方案. 在动植物研究领域, 高通量测序引领了一次具有里程碑意义的科学研究模式革新, 科研人员可利用该技术在基因组、转录组和表观基因组等领域展开多层次多方面多水平研究. 本文就高通量测序技术应用于动植物基因组学和功能基因组学研究进展进行了系统阐述, 并对当前高通量测序技术的现状和热点及未来的发展趋势作了深入剖析和讨论.     

Effective use of a Limited Antiviral Stockpile for Pandemic Influenza

Just allocation of resources for control of infectious diseases can be profoundly influenced by the dynamics of those diseases. In this paper we discuss the use of antiviral drugs for treatment of pandemic influenza. While the primary effect of such drugs is to alleviate and shorten the duration of symptoms for treated individuals, they can have a secondary effect of reducing transmission in the community. However, existing stockpiles may be insufficient for all clinical cases. Here we use simple mathematical models to present scenarios where the optimum policies to minimise morbidity and mortality, with a limited drug stockpile, are not always the most intuitively obvious and may conflict with theories of justice. We discuss ethical implications of these findings.     

Integrating Culture-based Antibiotic Resistance Profiles with Whole-genome Sequencing Data for 11,087 Clinical Isolates

Emerging antibiotic resistance is a major global health threat. The analysis of nucleic acid sequences linked to susceptibility phenotypes facilitates the study of genetic antibiotic resistance determinants to inform molecular diagnostics and drug development. We collected genetic data(11,087 newly-sequenced whole genomes) and culture-based resistance profiles(10,991 out of the11,087 isolates comprehensively tested against 22 antibiotics in total) of clinical isolates including18 main species spanning a time period of 30 years. Species and drug specific resistance patterns were observed including increased resistance rates for Acinetobacter baumannii to carbapenems and for Escherichia coli to fluoroquinolones. Species-level pan-genomes were constructed to reflect the genetic repertoire of the respective species, including conserved essential genes and known resistance factors. Integrating phenotypes and genotypes through species-level pan-genomes allowed to infer gene–drug resistance associations using statistical testing. The isolate collection and the analysis results have been integrated into GEAR-base, a resource available for academic research use free of charge at https://gear-base.com.     

Rapid Detection of the ACMG/ACOG-Recommended 23 CFTR Disease-Causing Mutations Using Ion Torrent Semiconductor Sequencing

Cystic fibrosis (CF) is one of the most frequently diagnosed autosomal-recessive diseases in the Caucasian population. For general-population CF carrier screening, the American College of Medical Genetics (ACMG)/American College of Obstetricians and Gynecologists (ACOG) have recommended a core panel of 23 mutations that will identify 49–98% of carriers, depending on ethnic background. Using a genotyping technology that can rapidly identify disease-causing mutations is important for high-throughput general-population carrier screening, confirming clinical diagnosis, determining treatment options, and prenatal diagnosis. Here, we describe a proof-of-concept study to determine whether the Ion Torrent Personal Genome Machine (PGM) sequencer platform can reliably identify all ACMG/ACOG 23 CF transmembrane conductance regulator (CFTR) mutations. A WT CF specimen along with mutant DNA specimens representing all 23 CFTR mutations were sequenced bidirectionally on the Ion Torrent 314 chip to determine the accuracy of the PGM for CFTR variant detection. We were able to reliably identify all of the targeted mutations except for 2184delA, which lies in a difficult, 7-mer homopolymer tract. Based on our study, we believe PGM sequencing may be a suitable technology for identifying CFTR mutations in the future. However, as a result of the elevated rate of base-calling errors within homopolymer stretches, mutations within such regions currently need to be evaluated carefully using an alternative method.     

基于第二代测序技术的基因资源挖掘

转录组研究一直是生命科学研究的一个重要方向,在第二代测序技术问世以前,已经产生了一些行之有效的转录组研究方法,但这些方法存在一定的局限性。第二代测序技术的出现不仅使转录组研究很快进入了高速发展期,同时也为遗传资源的挖掘提供了一套全新的技术平台。本文简要介绍了第二代测序技术的化学原理和特性,重点阐述了利用第二代测序技术进行转录组测序,从而在此基础上挖掘遗传资源的研究。