全基因组测序在病原菌分型与溯源中的应用研究进展

细菌分子分型已成为监测细菌感染性疾病的暴发流行与明确病原菌传播途径的重要工具.随着全基因组测序技术的日益兴起,公共数据库中已产生大量的细菌基因组数据,迫切需要研究人员充分认识和理解该技术,并掌握多种生物信息学工具挖掘并解读测序数据.本文系统概述了全基因组测序技术与生物信息学工具在病原菌分型与溯源中的应用,并对全基因组测...     

锈菌类真菌基因组结构分析研究进展

锈菌种群庞大,可以引起许多重要经济作物和林木病害,严重威胁全球粮食和林业生产安全。全基因组分析为锈菌基因功能研究、毒性变异研究及锈菌演化规律研究提供了重要基础,为制定锈病有效防控策略和创制抗锈新材料提供理论依据。本文综述了目前锈菌全基因组分析领域的进展,对锈菌的基因组结构、基因组成、基因组变异等特征进行了归纳分析,对基因组变异与其专性寄生特性的关系、基因组变异对其毒性变异的可能影响等进行了阐述。基因组学将为最终揭示锈菌生活史复杂性和毒性高度变异性的根本成因提供有力工具。     

复杂基因组测序技术研究进展

复杂基因组指的是无法使用常规测序和组装手段直接解析的一类基因组,通常指包含高比例重复序列、高杂合度、极端GC含量、存在难消除异源DNA污染的基因组。为了解决复杂基因组的测序和组装问题,需要分别从基因组测序实验方法、测序技术平台、组装算法与策略3个方面进行深入研究。本文详细介绍了复杂基因组测序组装相关的现有技术与方法,并结合复杂基因组经典实例介绍了复杂基因组测序的技术解决途径和发展历程,可为制订合适的复杂基因组测序策略提供参考。     

基于Web的基因组浏览器研究现状

测序技术的发展促使人类基因组测序成本急剧降低,测序速度迅速增加,对这些数据的分析和可视化已成为生命科学领域最重要的课题之一.基因组浏览器技术在基因序列分析,遗传密码解读,复杂疾病研究等方面具有重要意义.本文综述了9种主要的基因组浏览器技术,并从可视化内容、可视化形式、软件系统架构等角度分析了它们的特点.最后,探讨了基因组浏览器发展所面临的挑战.     

ABI PGM测序平台用于细菌基因组de novo测序的评价

为了探索加快细菌基因组研究的方法,利用ABI PGM测序平台测定了1株单细胞硫还原地杆菌的基因组序列。测序共获得1.4 Gbp数据,平均读长为177 bp。通过多个拼接软件并采用合适的组装策略,得到一个完整细菌基因组3.55 Mbp和一条完整质粒序列110 kbp。测定基因组序列与参考基因组kn400序列的相似性达到94%,参考基因组91%的基因能在测定基因组中找到相似基因。通过本研究表明采用ABI PGM测序平台结合灵活的拼接策略可快速构建细菌基因组精细图谱,为进一步的功能注释及深入的信息分析提供准确的数据,大大加快研究进程。     

微生物基因组研究进展

微生物基因组研究近年来发展很快,借助基因测序技术已完成多种微生物基因组测序。原核细胞生物基因组与真核细胞基因组具有各自特点。许多由基因控制的细菌遗传性状与细菌毒力有关,选择合适的靶基因研究可用于发展疫苗和开发新的诊断工具。同时,加快基因组功能性研究迫在眉睫。     

甘肃口岸输入性新型冠状病毒全基因组测序和基因特征分析

新型冠状病毒（SARS-CoV-2）作为一种单链RNA病毒，可引起人类严重的呼吸道综合征。本研究对2020年3月至2021年1月甘肃口岸入境人员中经RT-PCR检测SARS-CoV-2核酸阳性的8份样本进行全基因组测序，并用MEGA11软件以邻接法构建系统发育树，以揭示甘肃口岸输入性SARS-CoV-2基因组特征和遗传进化关系。全基因组测序获得8条长度为29 252bp～29 833bp的SARS-CoV-2基因组序列，基因组覆盖度97.82%～99.77%。与武汉参考株（GISAID号：EPI＿ISL＿402119）相比，共检测到64个错义突变，31个同义突变，3个移码突变和5个非编码等位基因。按照Pangolin分型法，8株输入性毒株属于B.1和B.4谱系。系统发育树分析显示来自伊朗的2例毒株聚集于同一进化支，其他6例毒株分布于不同的进化支中，与目前全球流行的关注变异株和武汉参考株均位于不同进化分支。值得注意的是，一株2020年10月份从俄罗斯输入的B.1.1.523谱系毒株出现时间早于文献报道的2021年3月份，说明该谱系可能在2020年10月或更早就已在俄罗斯流行。     

高等植物基因组测序回顾与展望

全基因组序列测定为揭示植物重要性状形成的分子和遗传机制提供了强大工具,基因组学研究正开始指引着农作物新品种培育向定向化和精确化转变.在新一代测序技术的带动下,植物全基因组测序的热潮已经到来.对迄今开展的高等植物基因组测序工作进行简要回顾,并对未来的研究热点进行展望.     

弯曲菌基因组学的研究进展

弯曲菌是世界范围内引起细菌性胃肠炎疾病的重要人兽共患病原菌,对公众健康造成严重威胁,其流行、传播、扩散的复杂性与基因组的高变异有关.全基因组测序(WGS)是一种基于基因水平的快速简便工具,可快速准确地进行物种鉴定、分析不同个体基因组间的差异、预测细菌耐药和毒力基因以及预测种群演化模型等.本文旨在对近年来全基因组测序在弯...     

苏云金芽胞杆菌LM1212菌株和突变株的全基因组序列比较分析

【背景】LM1212菌株是昆虫病原菌苏云金芽胞杆菌(Bacillus thuringiensis,Bt)中的一员,其芽胞和晶体分别产生于芽胞形成细胞和晶体产生细胞中,具有独特的细胞分化表型。与野生株LM1212相比,突变株LM1212-DB芽胞细胞比例明显降低并产生更高比例的晶体产生细胞,这使得LM1212-DB菌株成为研究晶体产生细胞形成机制和提高菌株杀虫活性的绝佳实验材料。【目的】比较LM1212菌株和LM1212-DB菌株的基因组差异,以便于揭示导致这两个菌株表型差异的原因。【方法】利用单分子测序技术(single molecular real-time,SMRT)和Pacbio RS II测序平台对两个菌株进行全基因组测序,对染色体和质粒、双组分信号系统和插入序列等进行差异分析,并构建表型特性相关基因的系统发育树。【结果】基因组分析发现,LM1212和LM1212-DB菌株均含有丰富的插入序列和双组分信号系统,暗示两个菌株极易发生基因重排且具有较强的环境适应性。与LM1212菌株相比,突变株LM1212-DB中发生了染色体和质粒片段缺失、质粒重排、质粒拷贝数变异。进一步分析缺失基因的功能发现,一些环境胁迫响应基因(如sigB)和芽胞形成相关基因(如abrB)等缺失;通过分析质粒拷贝数变异发现,具有增加晶体细胞比例功能的转录因子CpcR所在质粒的拷贝数增加1个,同时对CpcR的进化分析发现,与其亲缘关系最近的基因的从属菌株也产生与LM1212菌株相似的细胞分化表型。这些重要功能基因的缺失和拷贝数变异可能是导致两个菌株表型差异的原因。此外,突变株LM1212-DB缺失I型限制-修饰系统,这使得突变株LM1212-DB与野生菌株LM1212相比具有更好的外源DNA兼容性。【结论】突变株LM1212-DB染色体和质粒的结构变异可能是导致与野生株LM1212表型差异的潜在原因,这将为研究LM1212菌株的晶体细胞分化机制提供指导方向。     

Identification of candidate genes for lung cancer somatic mutation test kits

Over the past three decades, mortality from lung cancer has sharply and continuously increased in China, ascending to the first cause of death among all types of cancer. The ability to identify the actual sequence of gene mutations may help doctors determine which mutations lead to precancerous lesions and which produce invasive carcinomas, especially using next-generation sequencing (NGS) technology. In this study, we analyzed the latest lung cancer data in the COSMIC database, in order to find genomic “hotspots” that are frequently mutated in human lung cancer genomes. The results revealed that the most frequently mutated lung cancer genes are EGFR, KRAS and TP53. In recent years, EGFR and KRAS lung cancer test kits have been utilized for detecting lung cancer patients, but they presented many disadvantages, as they proved to be of low sensitivity, labor-intensive and time-consuming. In this study, we constructed a more complete catalogue of lung cancer mutation events including 145 mutated genes. With the genes of this list it may be feasible to develop a NGS kit for lung cancer mutation detection.     

Identifying cancer genes from cancer mutation profiles by cancer functions

It is of great importance to identify new cancer genes from the data of large scale genome screenings of gene mutations in cancers. Considering the alternations of some essential functions are indispensable for oncogenesis, we define them as cancer functions and select, as their approximations, a group of detailed functions in GO (Gene Ontology) highly enriched with known cancer genes. To evaluate the efficiency of using cancer functions as features to identify cancer genes, we define, in the screened genes, the known protein kinase cancer genes as gold standard positives and the other kinase genes as gold standard negatives. The results show that cancer associated functions are more efficient in identifying cancer genes than the selection pressure feature. Furthermore, combining cancer functions with the number of non-silent mutations can generate more reliable positive predictions. Finally, with precision 0.42, we suggest a list of 46 kinase genes as candidate cancer genes which are annotated to cancer functions and carry at least 3 non-silent mutations.     

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.     

Needles galore, but no place to sew

Large-scale sequencing projects to identify mutations related to cancer and psychiatric disorders will generate a lot of data. But the usefulness of these results will be limited unless we have good models to test their contribution to disease.     

MEGSA: A Powerful and Flexible Framework for Analyzing Mutual Exclusivity of Tumor Mutations

The central challenges in tumor sequencing studies is to identify driver genes and pathways, investigate their functional relationships, and nominate drug targets. The efficiency of these analyses, particularly for infrequently mutated genes, is compromised when subjects carry different combinations of driver mutations. Mutual exclusivity analysis helps address these challenges. To identify mutually exclusive gene sets (MEGS), we developed a powerful and flexible analytic framework based on a likelihood ratio test and a model selection procedure. Extensive simulations demonstrated that our method outperformed existing methods for both statistical power and the capability of identifying the exact MEGS, particularly for highly imbalanced MEGS. Our method can be used for de novo discovery, for pathway-guided searches, or for expanding established small MEGS. We applied our method to the whole-exome sequencing data for 13 cancer types from The Cancer Genome Atlas (TCGA). We identified multiple previously unreported non-pairwise MEGS in multiple cancer types. For acute myeloid leukemia, we identified a MEGS with five genes (FLT3, IDH2, NRAS, KIT, and TP53) and a MEGS (NPM1, TP53, and RUNX1) whose mutation status was strongly associated with survival (p = 6.7 × 10⁻⁴). For breast cancer, we identified a significant MEGS consisting of TP53 and four infrequently mutated genes (ARID1A, AKT1, MED23, and TBL1XR1), providing support for their role as cancer drivers.     

Bi-Directional SIFT Predicts a Subset of Activating Mutations

Advancements in sequencing technologies have empowered recent efforts to identify polymorphisms and mutations on a global scale. The large number of variations and mutations found in these projects requires high-throughput tools to identify those that are most likely to have an impact on function. Numerous computational tools exist for predicting which mutations are likely to be functional, but none that specifically attempt to identify mutations that result in hyperactivation or gain-of-function. Here we present a modified version of the SIFT (Sorting Intolerant from Tolerant) algorithm that utilizes protein sequence alignments with homologous sequences to identify functional mutations based on evolutionary fitness. We show that this bi-directional SIFT (B-SIFT) is capable of identifying experimentally verified activating mutants from multiple datasets. B-SIFT analysis of large-scale cancer genotyping data identified potential activating mutations, some of which we have provided detailed structural evidence to support. B-SIFT could prove to be a valuable tool for efforts in protein engineering as well as in identification of functional mutations in cancer.     

Cancer driver mutations: predictions and reality

Cancer cells accumulate many genetic alterations throughout their lifetime, but only a few of them drive cancer progression, termed driver mutations. Driver mutations may vary between cancer types and patients, can remain latent for a long time and become drivers at particular cancer stages, or may drive oncogenesis only in conjunction with other mutations. The high mutational, biochemical, and histological tumor heterogeneity makes driver mutation identification very challenging. In this review we summarize recent efforts to identify driver mutations in cancer and annotate their effects. We underline the success of computational methods to predict driver mutations in finding novel cancer biomarkers, including in circulating tumor DNA (ctDNA). We also report on the boundaries of their applicability in clinical research.