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Related Concept Videos

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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.
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
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Evolutionary Relationships through Genome Comparisons

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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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Updated: Jun 17, 2026

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies
12:08

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Published on: August 20, 2021

Improving de novo sequence assembly using machine learning and comparative genomics for overlap correction.

Lance E Palmer1, Mathaeus Dejori, Randall Bolanos

  • 1Siemens Corporate Research, 755 College Road East, Princeton, NJ, USA. lance.palmer@siemens.com

BMC Bioinformatics
|January 19, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning approach to improve de novo genome assembly by accurately identifying true read overlaps. This method enhances contig length and assembly quality using comparative genomics and sequence statistics.

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Last Updated: Jun 17, 2026

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies
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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Advancements in DNA sequencing enable leveraging existing data for new genome projects.
  • De novo assembly relies heavily on accurately identifying overlapping DNA reads.
  • Distinguishing true overlaps from spurious alignments due to repetitive sequences remains a challenge.

Purpose of the Study:

  • To enhance de novo genome assembly by improving the read overlap detection step.
  • To develop a data-driven method for classifying read overlaps as true or false.

Main Methods:

  • Extended the Minimus assembler with a machine learning-based overlap classification module.
  • Trained classification models using Weka with features like percent mismatch, k-mer frequencies, and comparative genomics scores.
  • Utilized read data from prior sequencing projects and reference genomes for training.

Main Results:

  • Nearly doubled the median contig length (N50) in E. coli and S. aureus whole-genome sequencing data.
  • Maintained genome coverage and did not increase the number of mis-assemblies.
  • Demonstrated the effectiveness of a curated set of overlaps in the contigging phase.

Conclusions:

  • Machine learning, incorporating comparative and non-comparative features, effectively classifies read overlaps.
  • This approach significantly improves the quality of de novo sequence assembly.