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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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Related Experiment Video

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Comparative Lesions Analysis Through a Targeted Sequencing Approach
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Progressive approach for SNP calling and haplotype assembly using single molecular sequencing data.

Fei Guo1, Dan Wang2, Lusheng Wang2,3

  • 1School of Computer Science and Technology, Tianjin University, Tianjin Haihe Education Park, Tianjin, China.

Bioinformatics (Oxford, England)
|February 24, 2018
PubMed
Summary

This study introduces a novel progressive approach for accurate single nucleotide polymorphism (SNP) calling and haplotype assembly using high-error Single Molecular Sequencing (SMS) data. The method demonstrates high-quality genomic analysis from SMS reads alone.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Haplotype information is crucial for understanding genomes, genetic diversity, and ancestry.
  • Single Molecular Sequencing (SMS) offers extensive genomic coverage but suffers from high error rates, complicating SNP calling and haplotype assembly.
  • Existing methods struggle with the challenges posed by SMS data.

Purpose of the Study:

  • To develop a robust method for SNP calling and haplotype assembly specifically designed for high-error Single Molecular Sequencing (SMS) data.
  • To demonstrate the feasibility of achieving high-quality genomic analysis using SMS reads alone.

Main Methods:

  • A progressive approach was developed to handle large-scale genomic data from SMS reads.
  • The method was designed to manage millions of reads and process substantial genomic regions, including over 200 million non-N bases on Chromosome 1.
  • The approach effectively addresses the high error rates inherent in SMS data.

Main Results:

  • The developed method successfully processed extensive genomic data, handling millions of reads and large blocks of bases.
  • Achieved competitive false discovery and false negative rates (15.7% and 11.0% on NA12878; 16.5% and 11.0% on NA24385).
  • Demonstrated low overall switch errors (7.26% on NA12878; 5.21% on NA24385) with a high number of SNP sites per block.

Conclusions:

  • The progressive approach significantly improves SNP calling and haplotype assembly accuracy for SMS data.
  • SMS reads, despite their error rate, can be utilized to generate high-quality genomic insights.
  • The developed method provides a viable solution for analyzing large-scale genomic data from emerging sequencing technologies.