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Related Concept Videos

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Updated: Feb 23, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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HiMMe: using genetic patterns as a proxy for genome assembly reliability assessment.

Jordi Abante1, Noushin Ghaffari2,3, Charles D Johnson2,3

  • 1Whitaker Biomedical Engineering Institute, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, USA. jabante1@jhu.edu.

BMC Genomics
|September 7, 2017
PubMed
Summary
This summary is machine-generated.

HiMMe is a novel tool that uses hidden Markov models (HMMs) to score genome assemblies by detecting genetic patterns. This method provides a quality metric for contigs and overall assemblies, improving downstream analysis.

Keywords:
Genome assembliesHidden Markov modelsMarkov chainsSequence analysisStochastic processesSupervised learningde novo assemblies

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Genome information is vital for organism development.
  • DNA sequencing technologies have advanced rapidly, but introduce noise and assembly ambiguities.
  • Accurate genome assembly is crucial for understanding cellular processes.

Purpose of the Study:

  • To introduce HiMMe, a novel tool for scoring genome assemblies.
  • To address noise and inconsistencies in genome assembly data.
  • To provide researchers with a reliable method for evaluating genome assemblies.

Main Methods:

  • Utilizes a hidden Markov model (HMM) approach.
  • Employs Markov chains to detect characteristic genetic patterns.
  • Incorporates emission probabilities to account for sequencing noise.
  • Allows prior knowledge integration for organism- or technology-specific training.

Main Results:

  • HiMMe effectively recognizes genetic patterns even with small k-mer sizes.
  • The method performs well with limited computational resources.
  • Demonstrates the ability to identify patterns crucial for assembly quality.

Conclusions:

  • HiMMe offers an individual quality metric per contig and an overall genome assembly score.
  • The tool's time complexity is significantly lower than aligners.
  • Provides valuable statistical insights for selecting contigs and assemblies for further research.