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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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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Updated: Feb 8, 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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segment_liftover : a Python tool to convert segments between genome assemblies.

Bo Gao1,2, Qingyao Huang1,2, Michael Baudis1,2

  • 1Institute of molecular Life Sciences, University of Zürich, Zürich, CH-8057, Switzerland.

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|June 30, 2018
PubMed
Summary
This summary is machine-generated.

Genome assembly coordinate conversion is crucial for comparative studies. New tool, segment_liftover, preserves segment continuity for accurate structural variation analysis, minimizing data loss.

Keywords:
Genome assemblycopy number segment.liftoverremap

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome assemblies are iterative, leading to coordinate differences between versions.
  • Accurate genome coordinate conversion is essential for comparative genomics and integrative studies.
  • Existing tools often focus on single-coordinate conversion, risking data loss for larger genomic regions.

Purpose of the Study:

  • To develop a bioinformatics tool, segment_liftover, for continuity-preserving genome segment remapping between assemblies.
  • To address data loss issues in copy number variation (CNV) analysis caused by disrupted segment continuity during coordinate conversion.

Main Methods:

  • segment_liftover facilitates continuity-preserving remapping of genome segments.
  • The tool supports approximate locus conversion for flexibility.
  • Features include automated batch processing and comprehensive logging for large-scale data.

Main Results:

  • segment_liftover enables accurate remapping of larger genome segments, maintaining continuity.
  • The tool mitigates data loss in analyses like CNV detection where regional integrity is key.
  • Facilitates efficient processing of extensive structural genome variation datasets.

Conclusions:

  • segment_liftover offers a robust solution for genome segment conversion between assemblies.
  • The tool enhances the reliability of comparative genomic studies, especially those involving structural variations.
  • Improved data integrity in analyses like CNV is a key benefit of using segment_liftover.