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Related Concept Videos

RNA-seq03:21

RNA-seq

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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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Ribosome Profiling02:24

Ribosome Profiling

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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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Related Experiment Video

Updated: Jun 13, 2025

Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
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A Systematic Benchmark of High-Accuracy PacBio Long-Read RNA Sequencing for Transcript-Level Quantification.

David Wissel1,2,3, Madison M Mehlferber4,5, Khue M Nguyen6

  • 1Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.

Biorxiv : the Preprint Server for Biology
|June 12, 2025
PubMed
Summary

High-depth PacBio long-read RNA sequencing (lrRNA-seq) shows strong concordance with Illumina short-read sequencing. This benchmarking study confirms PacBio Kinnex lrRNA-seq as a reliable method for accurate transcriptome profiling.

Keywords:
Long-read RNA-seqPacBioendothelial cellsquantification

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Area of Science:

  • Genomics and Transcriptomics
  • Molecular Biology
  • Biotechnology

Background:

  • Long-read RNA sequencing (lrRNA-seq) offers full-length transcript profiling, but its quantification accuracy, especially with PacBio data, requires robust validation.
  • Previous PacBio lrRNA-seq studies were limited by low to moderate sequencing depth, hindering comprehensive accuracy assessments.

Purpose of the Study:

  • To rigorously benchmark the quantification accuracy of PacBio Kinnex lrRNA-seq against Illumina short-read RNA sequencing.
  • To characterize potential biases in transcript quantification between these two major RNA sequencing platforms.

Main Methods:

  • Utilized a high-depth PacBio Kinnex lrRNA-seq dataset.
  • Employed sample-matched Illumina short-read RNA-seq data.
  • Benchmarked quantification accuracy using a dataset of induced pluripotent stem cell differentiation into primordial endothelial cells.

Main Results:

  • Identified inferential variability in Illumina data, potentially biasing abundance estimates for complex splicing genes.
  • Detected length-related biases in PacBio Kinnex data.
  • Demonstrated strong concordance between PacBio and Illumina quantification results.

Conclusions:

  • PacBio Kinnex lrRNA-seq, even at high depth, is a reliable method for transcriptome profiling.
  • The strong concordance supports the use of PacBio lrRNA-seq for downstream biological analyses.
  • Understanding platform-specific biases is crucial for accurate transcript abundance estimation.