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Related Experiment Video

Updated: Feb 2, 2026

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TransFlow: a modular framework for assembling and assessing accurate de novo transcriptomes in non-model organisms.

Pedro Seoane1, Marina Espigares1, Rosario Carmona2

  • 1Departmento de Biología Molecular y Bioquímica, Universidad de Málaga, Campus de Teatinos s/n, Malaga, 29071, Spain.

BMC Bioinformatics
|November 21, 2018
PubMed
Summary

TransFlow automates de novo transcriptome assembly evaluation for organisms lacking a reference genome. This framework ensures reproducible and reliable transcriptome selection using various sequencing data and assemblers.

Keywords:
AssemblingNon-model organismPCATranscriptomeWorkflowpipeline

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput sequencing enables de novo transcriptome assembly for new organisms.
  • Automation and evaluation are crucial for reproducibility and selecting optimal transcriptomes.
  • Evaluating de novo transcriptomes without a reference genome remains a challenge.

Purpose of the Study:

  • To describe TransFlow, an automated, reproducible, and flexible framework for de novo transcriptome assembly evaluation.
  • To address the challenge of selecting the best transcriptome for organisms lacking a sequenced genome.

Main Methods:

  • TransFlow utilizes five independent modules to build customizable workflows.
  • It supports combinations of Illumina and Roche/454 sequencing data and is extensible to other platforms.
  • Evaluation is performed using principal component analyses for self-adaptation to different data and assemblers.

Main Results:

  • TransFlow can handle 181 different assembly strategies.
  • Arabidopsis and poplar transcriptomes served as reliable references.
  • Illumina paired-end reads (100 nt) assembled with OASES yield reliable transcriptomes; longer reads are beneficial when complementing short, single reads.

Conclusions:

  • TransFlow provides an objective and automated method for selecting the best transcriptome.
  • Optimal assembly depends on sequencing data type/quantity, assemblers (OASES, MIRA4, EULER-SR/CAP3), and strategy (scaffolding, merging).
  • The framework adapts to diverse datasets, prioritizing evaluation parameters for each case study.