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RNA-seq03:21

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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. 
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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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.
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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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Related Experiment Video

Updated: Sep 10, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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A Hitchhiker Guide to Structural Variant Calling: A Comprehensive Benchmark Through Different Sequencing

Giuseppe Giovanni Nardone1, Valentina Andrioletti2, Aurora Santin1,3

  • 1Department of Medicine, Surgery and Health Sciences, University of Trieste, 34149 Trieste, Italy.

Biomedicines
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

Choosing the right alignment software significantly impacts structural variant (SV) detection accuracy in whole-genome sequencing (WGS). Performance varies between short-read (srWGS) and long-read (lrWGS) technologies, highlighting the need for standardized methods.

Keywords:
benchmarkbest practiceslong readsstructural variantsvariant callingwhole-genome sequencing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Structural variants (SVs) are crucial for gene function and are linked to human diseases.
  • Whole-genome sequencing (WGS) is vital for identifying SVs, but tool variability hinders accuracy.
  • Standardized methodologies for SV detection are lacking.

Purpose of the Study:

  • To evaluate the accuracy of structural variant detection across different sequencing technologies.
  • To assess the impact of variant calling algorithms, reference genomes, alignment strategies, and sequencing coverage on SV detection.
  • To compare short-read (srWGS) and long-read (lrWGS) sequencing performance for SV identification.

Main Methods:

  • Assessed SV detection accuracy using deletion calls from the HG002 benchmark dataset.
  • Tested Illumina short reads, PacBio long reads, and Oxford Nanopore Technologies (ONT) long reads.
  • Examined the influence of variant callers, reference genomes, alignment strategies, and sequencing coverage.

Main Results:

  • DRAGEN v4.2 showed highest accuracy for srWGS; graph-based references improved calling in complex regions.
  • Combining minimap2 with Manta achieved srWGS performance comparable to DRAGEN.
  • Sniffles2 excelled for PacBio lrWGS; minimap2 alignment was best for ONT lrWGS; Duet and Dysgu performed best at different coverages.

Conclusions:

  • Alignment software choice significantly impacts srWGS structural variant calling accuracy.
  • Long-read sequencing (lrWGS) performance for SV detection is dependent on the specific technology and sequencing coverage.
  • These findings offer insights into optimizing SV detection across diverse sequencing platforms.