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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
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Deleterious substances in aggregates can be detrimental to the quality and durability of concrete. These substances include organic impurities like loam, which interfere with cement hydration and are usually present in the sand. These prevent a good bond between aggregate and cement paste. Organic impurities can be detected using the colorimetric test, where the darkness of a solution after agitation indicates the level of organic content.
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Chemicals play important roles in controlling microbial growth by targeting microbial structures and functions as sanitizers, antiseptics, disinfectants, and sterilants.Alcohols are commonly used sanitizers, effectively disrupting lipid membranes, which compromises cell integrity. They are also used as antiseptics and disinfectants due to their rapid action and versatility.Phenols and their derivatives phenolics , known for denaturing proteins and disrupting cell membranes, are particularly...
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HoCoRT:主体污染去除工具

Ignas Rumbavicius1, Trine B Rounge2,3, Torbjørn Rognes4,5

  • 1Centre for Bioinformatics, Department of Informatics, University of Oslo, PO Box 1080 Blindern, 0316, Oslo, Norway.

BMC bioinformatics
|October 2, 2023
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概括
此摘要是机器生成的。

HoCoRT是一种新的工具,可以有效地从中枪元基因组数据中去除宿主污染. 它提供了速度和准确性,优于现有的微生物组分析方法.

关键词:
分类 分类 分类 分类.污染 污染 污染 污染微生物组是一个微生物组.一把枪的元基因组.软件 软件 软件 软件 软件

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 步枪元基因组测序容易受到宿主生物的污染.
  • 移除宿主序列对于准确的微生物组分析至关重要,减少偏差和计算负载.
  • 现有的主机删除工具往往过时或难以使用.

研究的目的:

  • 开发HoCoRT,这是一个快速和用户友好的工具,可以从元基因组数据中优化移除宿主序列.
  • 评估HoCoRT中实施的各种宿主清除方法的速度和准确性.
  • 根据阅读长度和微生物群类型,为最佳宿主清除策略提供建议.

主要方法:

  • HoCoRT是一个开源的命令行工具,用于移除主机污染.
  • 它使用各种映射,分类和对齐方法 (例如BioBloom,Bowtie2,HISAT2,Kraken2,Minimap2).
  • 使用合成和公共的人体肠道和口腔微生物群数据集进行了评估,这些数据集具有短和长的读数.

主要成果:

  • HoCoRT提供了一个用户友好的界面,提供基因组索引和方法选择的选项.
  • 对于简短的阅读,BioBloom,Bowtie2和HISAT2为肠道微生物提供了最佳的速度/准确性;Kraken2是最快但更不准确的.
  • 对于长时间的读数,Kraken2和Minimap2获得了最高的准确性 (59%的人类读数被检测到).

结论:

  • 对于移除主机,HoCoRT比DeconSeq更快,更准确一些.
  • 推的最佳方法因读数长度和微生物群类型而异.
  • 作为一个Bioconda包,HoCoRT提供开源代码和麻省理工学院许可证.