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Normal Stress01:19

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Normal stress is a type of stress that occurs when forces act perpendicular, or normal, to a material's cross-sectional area. This stress often arises in structures when subjected to axial loading, which is the application of force along the axis of an object. A practical example of this can be found in bridge truss members.
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The normal, a continuous distribution, is the most important of all the distributions. Its graph is a bell-shaped symmetrical curve, which is observed in almost all disciplines. Some of these include psychology, business, economics, the sciences, nursing, and, of course, mathematics. Some instructors may use the normal distribution to help determine students’ grades. Most IQ scores are normally distributed. Often real-estate prices fit a normal distribution. The normal distribution is...
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Metagenomic Analysis of Silage
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SolidBin: improving metagenome binning with semi-supervised normalized cut.

Ziye Wang1,2,3, Zhengyang Wang2, Yang Young Lu4

  • 1Centre for Computational Systems Biology, School of Mathematical Sciences, Shanghai, China.

Bioinformatics (Oxford, England)
|April 13, 2019
PubMed
Summary
This summary is machine-generated.

SolidBin, a new semi-supervised spectral clustering method, improves metagenomic contig binning by integrating biological information. It outperforms existing tools, especially on real datasets, enhancing genome assembly accuracy.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Genomics

Background:

  • Metagenomic contig binning is crucial for assembling genomes from complex microbial communities.
  • Current methods often fail to fully leverage available biological information beyond sequence composition and coverage.
  • Accurate binning is essential for understanding microbial community structure and function.

Purpose of the Study:

  • To develop a novel semi-supervised contig binning method that effectively integrates additional biological information.
  • To improve the accuracy and performance of metagenomic contig binning compared to existing state-of-the-art tools.

Main Methods:

  • Developed SolidBin, a method based on semi-supervised spectral clustering (Normalized Cut).
  • Incorporated 'must-link' and 'cannot-link' constraints derived from sequence similarity and taxonomic assignments.
  • Integrated these constraints into the spectral clustering framework to guide the binning process.

Main Results:

  • SolidBin demonstrated superior performance across multiple benchmark datasets (simulated and real, single- and multi-sample).
  • Achieved higher scores in F-score, Adjusted Rand Index, and Normalized Mutual Information compared to CONCOCT, COCACOLA, MaxBin, MetaBAT, and BMC3C.
  • Showed particular effectiveness on real-world datasets and single-sample datasets.

Conclusions:

  • SolidBin offers a significant advancement in metagenomic contig binning by effectively utilizing diverse biological data.
  • The method provides more accurate genome assemblies from metagenomic data, particularly in challenging scenarios.
  • The developed tool is publicly available, facilitating its adoption in microbial genomics research.