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Sequencing of mRNA from Whole Blood using Nanopore Sequencing
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Analysis of nanopore data using hidden Markov models.

Jacob Schreiber1, Kevin Karplus1

  • 1Nanopore Group, Department of Biomolecular Engineering, University of California Santa Cruz, CA 95064, USA.

Bioinformatics (Oxford, England)
|February 5, 2015
PubMed
Summary
This summary is machine-generated.

Automated nanopore sequencing alignment using hidden Markov models improves accuracy. This method reduces the error rate for distinguishing cytosine variants in nanopore data analysis.

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Area of Science:

  • Biotechnology
  • Bioinformatics
  • Genomics

Background:

  • Nanopore sequencing analyzes biosequence properties via ionic current changes as molecules pass through a pore.
  • Current methods typically require manual alignment of sequencing data to a reference genome.

Purpose of the Study:

  • To develop an automated method for aligning nanopore sequencing data to a reference.
  • To incorporate enzyme class and prior processing features into the alignment model.

Main Methods:

  • Utilized hidden Markov models for automated alignment of nanopore sequencing data.
  • Integrated features from prior processing steps and enzyme class into the HMM.

Main Results:

  • Achieved an automated error rate of 2-3% for distinguishing cytosine variants (cytosine, methylcytosine, hydroxymethylcytosine).
  • This automated error rate is significantly lower than the 10% error rate from previous manual alignment methods.
  • Validated on data from the M2MspA nanopore, known for its sensitivity to cytosine modifications.

Conclusions:

  • The proposed automated hidden Markov model approach enhances nanopore data alignment accuracy.
  • This automation reduces errors in distinguishing epigenetic modifications like cytosine variants.
  • The methodology offers a more efficient and precise alternative to manual alignment in nanopore sequencing analysis.