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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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Related Experiment Video

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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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To join or not to join: handling biological replicates in long-read RNA sequencing data.

Fabian Jetzinger1,2,3, Alejandro Paniagua2,3, Stanley Cormack2,4

  • 1BioBam Bioinformatics S.L., Valencia, Spain.

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|December 22, 2025
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Summary

Two strategies for combining long-read RNA sequencing data, "Join & Call" and "Call & Join", were evaluated. Join & Call excels at novel isoform discovery, while Call & Join is more efficient for highly replicated datasets.

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Area of Science:

  • Transcriptomics
  • Bioinformatics
  • Genomics

Background:

  • Long-read RNA sequencing (lrRNA-seq) has advanced transcriptomics, enabling novel transcript discovery.
  • Handling biologically replicated lrRNA-seq data for consensus transcriptome generation is underexplored.
  • Combining samples in lrRNA-seq studies significantly impacts transcript identification.

Purpose of the Study:

  • To define and evaluate two distinct strategies for consensus transcriptome reconstruction from multi-sample lrRNA-seq data.
  • To compare the performance of "Join & Call" versus "Call & Join" across different sequencing technologies and tools.
  • To provide a framework for optimizing multi-sample lrRNA-seq data analysis.

Main Methods:

  • Applied "Join & Call" (combine reads first) and "Call & Join" (annotate then combine) strategies.
  • Utilized highly replicated mouse brain and kidney lrRNA-seq datasets (PacBio and ONT).
  • Evaluated six widely used transcript reconstruction tools for each strategy.

Main Results:

  • Optimal strategy is dependent on the computational tool and research goals.
  • "Join & Call" enhances detection of rare, novel isoforms by pooling evidence.
  • "Call & Join" is computationally efficient and suitable for highly replicated data when rare transcript discovery is not primary.

Conclusions:

  • The choice between "Join & Call" and "Call & Join" impacts lrRNA-seq analysis outcomes.
  • Findings offer practical guidance for best practices in multi-sample lrRNA-seq studies.
  • This framework aids researchers in maximizing insights from large-scale transcriptomic datasets.