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Related Concept Videos

Meiosis I01:49

Meiosis I

Meiosis is a carefully orchestrated set of cell divisions, the goal of which—in humans—is to produce haploid sperm or eggs, each containing half the number of chromosomes present in somatic cells elsewhere in the body. Meiosis I is the first such division, and involves several key steps, among them: condensation of replicated chromosomes in diploid cells; the pairing of homologous chromosomes and their exchange of information; and finally, the separation of homologous chromosomes by a...
Meiosis I03:09

Meiosis I

Meiosis is the division of a diploid cell into haploid cells forming sperm and eggs in animals through differentiation. Meiosis I is the first stage of meiosis, where the genetic recombination of homologous chromosomes and the reduction of the ploidy level by half occurs.
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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.
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Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

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Meiosis II02:02

Meiosis II

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Related Experiment Video

Updated: May 13, 2026

Determination of the Mating Efficiency of Haploids in Saccharomyces cerevisiae
05:39

Determination of the Mating Efficiency of Haploids in Saccharomyces cerevisiae

Published on: December 2, 2022

MixSIH: a mixture model for single individual haplotyping.

Hirotaka Matsumoto1, Hisanori Kiryu

  • 1Department of Computational Biology, Faculty of Frontier Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan. matsumoto@cb.k.u-tokyo.ac.jp

BMC Genomics
|March 1, 2013
PubMed
Summary

This study introduces MixSIH, a novel computational method for haplotype assembly, which infers an individual's two haplotypes from DNA sequence data. MixSIH provides a quality score to extract highly accurate haplotype segments, improving genetic analysis reliability.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Haplotype information is crucial for genetic analyses like genome-wide association studies.
  • Experimental determination of haplotypes is challenging, necessitating computational inference methods.
  • Current computational approaches for single individual haplotyping (haplotype assembly) lack efficient methods for extracting high-confidence regions.

Purpose of the Study:

  • To develop a novel computational method for accurate haplotype assembly.
  • To introduce a quality score for evaluating the reliability of assembled haplotype segments.
  • To address the need for extracting highly confident haplotype regions from genomic data.

Main Methods:

  • Developed a probabilistic model, MixSIH, with two mixture components representing individual haplotypes.
  • Defined a 'minimum connectivity' (MC) score based on the optimized model to assess segment quality.
  • Created a new accuracy measure based on pairwise consistency for evaluating partially assembled haplotype segments.

Main Results:

  • MixSIH successfully infers haplotypes and extracts highly accurate haplotype segments using MC scores.
  • The developed accuracy measure effectively evaluates partially assembled haplotype segments on simulated and real data.
  • Identified chimeric read fragments in an existing dataset that negatively impact haplotype assembly quality.

Conclusions:

  • MixSIH offers a novel and effective method for haplotype assembly.
  • The MC score provides a reliable indicator of haplotype segment accuracy.
  • MixSIH successfully extracts reliable haplotype segments, enhancing downstream genetic analyses.