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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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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Related Experiment Video

Updated: Jan 7, 2026

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C
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Reference-Guided Chromosome-by-Chromosome de novo Assembly at Scale Using Low-Coverage High-Fidelity Long-Reads with

Zhongjun Jiang1,2, Weihua Pan3, Runtian Gao1,2

  • 1College of Life Science, Northeast Forestry University, Harbin, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|December 26, 2025
PubMed
Summary

HiFiCCL is a new framework for assembling low-coverage, high-fidelity sequencing data, improving structural variant detection in population genomics. This advances genome assembly for large populations and diverse species.

Keywords:
chromosome‐by‐chromosomelong high‐fidelity readslow coveragepopulation genomicsreference‐guided de novo assembly

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Area of Science:

  • Genomics and Bioinformatics
  • Population Genetics
  • Structural Variant Detection

Background:

  • Short-read sequencing in population genomics misses structural variants (SVs), impacting heritability in genome-wide association studies.
  • Long-read sequencing improves pangenome construction but high-fidelity data is expensive, limiting large-scale population studies.
  • Current assemblers perform poorly with low-coverage sequencing data, necessitating improved assembly methods.

Purpose of the Study:

  • To introduce HiFiCCL, the first assembly framework designed for low-coverage, high-fidelity (HiFi) reads.
  • To address the limitations of existing assemblers in low-coverage sequencing scenarios.
  • To enhance the detection of structural variants in population genomics.

Main Methods:

  • Developed HiFiCCL, a reference-guided, chromosome-by-chromosome assembly framework.
  • Evaluated HiFiCCL's performance on human and plant datasets with low-coverage HiFi reads.
  • Compared HiFiCCL combined with hifiasm against state-of-the-art assemblers.

Main Results:

  • HiFiCCL improves the performance of existing low-coverage assemblers.
  • HiFiCCL outperforms state-of-the-art assemblers on human and plant datasets.
  • On human datasets (∼5× coverage), HiFiCCL with hifiasm reduced misassembled contig length by an average of 21.19%.

Conclusions:

  • HiFiCCL enables robust genome assembly from low-coverage HiFi data, crucial for large-scale population genomics.
  • Improved assemblies facilitate better detection of large germline structural variants and minimize mis-scaffolding.
  • HiFiCCL enhances the discovery of germline and tumor somatic structural variants using pangenome graphs.