Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

101
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
101
Fineness Modulus01:19

Fineness Modulus

793
The fineness modulus (FM) of aggregate is a numerical index that measures the coarseness or fineness of the particles. It is calculated by adding the cumulative percentages of aggregate retained on each of a specified series of sieves and dividing the sum by 100.
Consider performing sieve analysis on sand through a set of ASTM sieves. The weight of aggregate retained in each sieve and pan placed at the bottom is recorded, as given in Column B of Table 1.
To determine the fineness modulus of...
793
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

4.3K
Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
4.3K
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

11.5K
In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
11.5K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

712
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
712
Optimizing Chromatographic Separations01:15

Optimizing Chromatographic Separations

489
Optimizing chromatographic separations is crucial for obtaining clean separations in a minimum amount of time. Optimization is required for several factors, including kinetic effects related to band broadening, plate height, capacity factor, and separation factor.
Band broadening refers to spreading solute bands as they travel through the column. This broadening can impact resolution. Plate height (H) represents the length required for one theoretical plate. A lower plate height corresponds to...
489

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Biodegradability of commercial biopolyesters in marine sediments under anoxic conditions.

Chemosphere·2026
Same author

Response to: "best practices when benchmarking CATCH for the design of genome enrichment probes".

Bioinformatics (Oxford, England)·2026
Same author

Scalable de novo classification of antibiotic resistance of Mycobacterium tuberculosis.

Bioinformatics (Oxford, England)·2024
Same author

Suffix sorting via matching statistics.

Algorithms for molecular biology : AMB·2024
Same author

Chemical-physical parameters and microbial community changes induced by electrodes polarization inhibit PCB dechlorination in a marine sediment.

Journal of hazardous materials·2024
Same author

An Experimental Performance Assessment of Galileo OSNMA.

Sensors (Basel, Switzerland)·2024

相关实验视频

Updated: Sep 11, 2025

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
10:23

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

204

终结器:用于$k$-mer集的可变长度有限频率最小化器.

Jarno N Alanko, Elena Biagi, Simon J Puglisi

    IEEE transactions on computational biology and bioinformatics
    |August 14, 2025
    PubMed
    概括
    此摘要是机器生成的。

    我们引入了频率有限的最小化器 (终极化器),以改善基因组学中的k-mer索引. 最终调节器可以动态调整m-mer长度以限制频率,确保更快的查询时间和比传统的最小化方案更好的回忆.

    更多相关视频

    Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
    14:06

    Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

    Published on: June 23, 2012

    15.3K
    Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
    07:30

    Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

    Published on: June 8, 2020

    12.2K

    相关实验视频

    Last Updated: Sep 11, 2025

    A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
    10:23

    A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

    Published on: July 11, 2025

    204
    Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
    14:06

    Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

    Published on: June 23, 2012

    15.3K
    Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
    07:30

    Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

    Published on: June 8, 2020

    12.2K

    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
    • 数据结构和算法数据结构和算法

    背景情况:

    • 在元基因组学和泛基因组学中,最小化器对k-mer索引至关重要,但频繁的最小化器会降低性能.
    • 在对齐工具中常见最小化器的现有启发式常常会损害其他指标,如回忆或空间使用.
    • 需要索引方法来保证可预测的查询时间,而不会牺牲效率.

    研究的目的:

    • 引入频率有限的最小化器 (终极化器) 作为对k-mers的索引集的新方法.
    • 解决频繁最小化的问题,并提供最坏情况下的查询时间保证.
    • 开发使用先进数据结构的最终化器的高效实现.

    主要方法:

    • 引入了终极化器,一种新型的最小化器,其中m-mer长度变化到低于值"t"的边界频率.
    • 使用了光谱洞穴-轮子变换 (SBWT),并增加了最长常见后信息,以实现高效的最终化器.
    • 调查了t=1的特殊情况,简化了索引结构,使方案无参数 (除了k).

    主要成果:

    • 最终化器有效地解决了非常频繁的最小化器的问题,为查询时间提供了最坏的情况下的保证.
    • 基于SBWT的实现是高效的,原型实现了与最先进的最小化方案相比或比其更快的k-mer本地化时间.
    • 该t=1终极化方案展示了具有竞争力的性能和简单性,需要最小的参数调整.

    结论:

    • 基于频率的最小化器 (终极化器) 为传统的基于最小化器的索引提供了强大的和高效的替代方案.
    • 拟议的方法提高了查询时间的可预测性和k-mer索引任务的性能.
    • 完成器代表了对大规模基因组应用 (如元基因组学和泛基因组学) 的索引的重大进步.