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Related Experiment Video

Updated: Nov 5, 2025

Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Profile hidden Markov model sequence analysis can help remove putative pseudogenes from DNA barcoding and

T M Porter¹, M Hajibabaei²

¹Department of Integrative Biology and Centre for Biodiversity Genomics, University of Guelph, 50 Stone Road East, Guelph, ON, Canada. terrimporter@gmail.com.

BMC Bioinformatics

|May 20, 2021

Summary

This summary is machine-generated.

This study developed a method to filter pseudogenes, non-functional gene copies, from DNA barcoding and metabarcoding data. Incorporating this pseudogene screening improves the accuracy of COI sequence analysis in large datasets.

Keywords:

Bioinformatics COI mtDNA DNA barcode Hidden Markov model Metabarcode NuMT Nuclear encoded mitochondrial sequences Pseudogene

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Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

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Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Next-generation Sequencing of 16S Ribosomal RNA Gene Amplicons

Published on: August 29, 2014

Area of Science:

Genomics
Bioinformatics
Molecular Evolution

Background:

Pseudogenes, non-functional gene copies, can distort DNA barcoding and metabarcoding results.
Current bioinformatics pipelines lack specific methods to identify and remove pseudogenes from protein-coding marker genes.
Nuclear mitochondrial DNA segments (nuMTs) are a type of pseudogene that can interfere with COI-based analyses.

Purpose of the Study:

To develop and implement a method for screening nuclear mitochondrial DNA segments (nuMTs) in large COI datasets.
To assess the impact of pseudogene removal on metabarcoding data accuracy.
To integrate a pseudogene filtering step into existing bioinformatics pipelines for COI metabarcoding.

Main Methods:

Characterized gene and nuMT features using an artificial COI barcode dataset.
Simulated nuMTs in community datasets to evaluate pseudogene removal methods.
Utilized open reading frame (ORF) length and Hidden Markov Model (HMM) profile analysis for pseudogene detection.
Incorporated a pseudogene filtering step into an Illumina paired-end COI metabarcoding pipeline.

Main Results:

Identifying nuMTs is more challenging with shorter metabarcoding sequences compared to full-length DNA barcodes.
High percentages of nuMTs in datasets further complicate their identification.
Existing pipelines remove some nuMTs, but an additional pseudogene filter can remove up to 5% more sequences.
ORF length and HMM analysis effectively identified pseudogenes in simulations.

Conclusions:

Open reading frame length filtering, with or without HMM analysis, effectively screens pseudogenes from large datasets.
Further research is needed on the frequency, taxonomic distribution, and evolution of COI nuMTs.
Encourages submission of verified COI nuMTs to public databases to aid future studies.