评估元基因组工具对当前尖端基因组工程和技术的影响。

International journal of biochemistry and molecular biology Pub Date : 2023-08-15 eCollection Date: 2023-01-01
Tuward J Dweh, Subhashree Pattnaik, Jyoti Prakash Sahoo
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引用次数: 0

摘要

宏基因组学被定义为对自然界中发现的总微生物群基因组的研究,通常被称为微生物环境基因组学,因为它需要检查特定环境中来自不同生物群落的一组遗传成分(基因组)。它是组学技术的一个分支,包括脱氧核糖核酸(DNA)、核糖核酸(脱氧核糖核酸)、蛋白质和各种成分,以全系统的方式对生物分子的各个方面进行综合分析。聚集的规则间隔的回文重复序列及其核酸内切酶,CRISPR相关蛋白,形成了一种称为CRISPR-cas9技术的复合物,尽管这是一种用于对生物体基因组进行精确改变的不同技术,但它可以与宏基因组方法相结合,对基因组和序列读取进行更好、快速、更准确的描述。测序技术的不断进步,永远加深了我们对微生物基因组的理解。从使用传统方法只能对少量基因进行测序的时代(例如,Allan和Sanger开发的“正负”方法,以及以在聚丙烯酰胺凝胶辅助的聚合反应中通过放射性标记的DNA聚合酶引物对phiX174噬菌体基因组进行测序而闻名的“化学切割”方法)到全基因组时代测序,包括“连接测序”和“合成测序”,在合成新DNA时检测氢离子(第二代),然后是下一代测序技术(NGS)。有了这些技术,人类基因组计划(HGP)成为可能。这项研究通过检查随机选择的研究论文的发现,着眼于植物和动物宏基因组学的最新进展。所有选定的案例研究都使用高通量测序来产生不同的序列读数,检查了不同微生物群落的功能和分类分析。在动物方面,五项研究表明,斑马鱼、牲畜、家禽、牛、生态位和人类微生物组是如何利用土壤和水等环境样本来识别微生物群落及其功能的。它还被用于研究人类和其他生物的微生物组,包括肠道微生物组。最近的研究表明,这些技术可以更快、更准确地识别病原体,从而改善疾病诊断。他们还通过识别可能影响药物疗效和毒性的基因变异,促进了个性化药物的发展。测序技术的持续进步和CRISPR-Cas9工具的改进为科学研究和应用的变革性突破提供了更大的潜力。另一方面,宏基因组数据总是庞大且难以处理。分类谱、功能注释和复杂相互作用机制的复杂性仍然需要更好的生物信息学工具。目前的综述集中在更好的(例如,人工智能驱动的算法)工具上,这些工具可以预测代谢途径和相互作用,并操纵复杂的数据来解决准确解释的潜在偏差。
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Assessing the impact of meta-genomic tools on current cutting-edge genome engineering and technology.

Metagenomics is defined as the study of the genome of the total microbiota found in nature and is often referred to as microbial environmental genomics because it entails the examination of a group of genetic components (genomes) from a diverse community of organisms in a particular setting. It is a sub-branch of omics technology that encompasses Deoxyribonucleic Acid (DNA), Ribonucleic acid (DNA), proteins, and various components associated with comprehensive analysis of all aspects of biological molecules in a system-wide manner. Clustered regularly interspaced palindromic repeats and its endonuclease, CRISPR-associated protein which forms a complex called CRISPR-cas9 technology, though it is a different technique used to make precise changes to the genome of an organism, it can be used in conjunction with metagenomic approaches to give a better, rapid, and more accurate description of genomes and sequence reads. There have been ongoing improvements in sequencing that have deepened our understanding of microbial genomes forever. From the time when only a small amount of gene could be sequenced using traditional methods (e.g., "the plus and minus" method developed by Allan and Sanger and the "chemical cleavage" method that is known for its use in the sequencing the phiX174 bacteriophage genome via radio-labeled DNA polymerase-primer in a polymerization reaction aided by polyacrylamide gel) to the era of total genomes sequencing which includes "sequencing-by-ligation" and the "sequencing-by-synthesis" that detects hydrogen ions when new DNA is synthesized (Second Generation) and then Next Generation Sequencing technologies (NGS). With these technologies, the Human Genome Project (HGP) was made possible. The study looks at recent advancements in metagenomics in plants and animals by examining findings from randomly selected research papers. All selected case studies examined the functional and taxonomical analysis of different microbial communities using high-throughput sequencing to generate different sequence reads. In animals, five studies indicated how Zebrafish, Livestock, Poultry, cattle, niches, and the human microbiome were exploited using environmental samples, such as soil and water, to identify microbial communities and their functions. It has also been used to study the microbiome of humans and other organisms, including gut microbiomes. Recent studies demonstrated how these technologies have allowed for faster and more accurate identification of pathogens, leading to improved disease diagnostics. They have also enabled the development of personalized medicine by allowing for the identification of genetic variations that can impact drug efficacy and toxicity. Continued advancements in sequencing techniques and the refinement of CRISPR-Cas9 tools offer even greater potential for transformative breakthroughs in scientific research and applications. On the other hand, metagenomic data are always large and uneasy to handle. The complexity of taxonomical profiling, functional annotation, and mechanisms of complex interaction still needs better bioinformatics tools. Current review focuses on better (e.g., AI-driven algorithms) tools that can predict metabolic pathways and interactions, and manipulate complex data to address potential bias for accurate interpretation.

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