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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.
RNA-seq03:21

RNA-seq

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. 
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Protein Complex Assembly

Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types.  Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes.
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Improving Translational Accuracy

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

Improving Translational Accuracy

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Transposons

Transposons, or "jumping genes," are small mobile genetic elements (MGEs) that range from 700 to 40,000 base pairs in length. They are found in all organisms and can move within the same chromosome or transfer to different chromosomes. In some cases, transposons can also jump between different host DNA molecules, such as plasmids or viruses, contributing to genetic variability.Barbara McClintock first discovered these mobile genetic elements in the 1940s while studying maize genetics, and she...

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Invas: an inversion-aware method for transcriptome assembly.

Xuedong Wang1,2, Feiyue Wang1,2, Chenzhao Feng3,4

  • 1Department of Computer Science, City University of Hong Kong, Hong Kong, China.

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|July 11, 2026
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Summary

Invas, a new framework, accurately analyzes intragenic inversions missed by standard tools. This improves gene quantification and identifies novel disease-related genetic variants and tumor antigens.

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Area of Science:

  • Genomics
  • Transcriptomics
  • Bioinformatics

Background:

  • Intragenic inversions alter gene sequence orientation and create non-collinear splice junctions.
  • Standard transcript assemblers often miss these inversions, leading to incomplete gene reconstruction and biased quantification.

Purpose of the Study:

  • To develop an inversion-aware framework (Invas) for accurate analysis of structural variants in gene expression.
  • To improve the characterization of inversion-affected genes and identify novel disease-associated genetic variants and immunogenic tumor antigens.

Main Methods:

  • Invas integrates whole-genome sequencing breakpoints with transcriptomic sequencing data.
  • It rescues unmapped reads and assembles gene isoforms using conjugate flow optimization.
  • The framework was validated in silico across 42,000 events and applied to seven disease cohorts.

Main Results:

  • Invas demonstrated high precision and recall in detecting intragenic inversions.
  • It improved the quantification of unaffected transcripts compared to conventional tools.
  • The study identified recurrent germline susceptibility variants, somatic drivers, and predicted immunogenic somatic inversion-derived tumor-specific antigens.

Conclusions:

  • Invas effectively addresses the challenge of characterizing inversion-affected genes, filling a critical gap in structural variant-aware transcriptomics.
  • The framework facilitates the discovery of novel genetic variants in disease and the identification of potential cancer immunotherapies.