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Updated: Mar 11, 2026

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Alignment-free Transcriptomic and Metatranscriptomic Comparison Using Sequencing Signatures with Variable Length

Weinan Liao1, Jie Ren2, Kun Wang1

  • 1Department of Automation, Xiamen University, Xiamen, Fujian, 361005 China.

Scientific Reports
|November 24, 2016
PubMed
Summary
This summary is machine-generated.

Variable Length Markov Chain (VLMC) offers a data-driven approach for microbial community analysis, outperforming Fixed Order Markov Chain (FOMC) in modeling background sequences for metatranscriptomic data.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Comparing microbial sequencing data is crucial for understanding microbial community dynamics.
  • Alignment-free methods using Fixed Order Markov Chains (FOMC) show promise but face parameter estimation challenges with limited sequencing depth.
  • High-order FOMC models require extensive parameters that may be inaccurately estimated.

Purpose of the Study:

  • To introduce and evaluate a data-driven Variable Length Markov Chain (VLMC) approach for modeling background sequences in metatranscriptomic data.
  • To adapt VLMC, originally for long sequences, for high-throughput sequencing reads and develop parameter estimation strategies.
  • To compare the performance of VLMC-based beta diversity measures against FOMC in microbial community analysis.

Main Methods:

  • Extended Variable Length Markov Chain (VLMC) for application to high-throughput sequencing reads.
  • Developed strategies for estimating VLMC parameters, allowing adaptive order determination.
  • Applied VLMC-based beta diversity measures to bacterial RNA-Seq and metatranscriptomic datasets.

Main Results:

  • VLMC effectively models background sequences in transcriptomic and metatranscriptomic samples.
  • VLMC outperforms FOMC in modeling background sequences.
  • The flexible parameterization of VLMC avoids issues associated with high-order FOMC under limited sequencing depth.

Conclusions:

  • Variable Length Markov Chain (VLMC) provides a more robust and adaptive method for analyzing microbial community structure compared to Fixed Order Markov Chain (FOMC).
  • VLMC's data-driven approach enhances the accuracy of sequence modeling in metatranscriptomic studies.
  • The developed VLMC pipeline offers a valuable tool for microbial community comparison.