Paul D. N. Hebert

The paper introduces Hubbell regression to estimate biodiversity descriptors and finds actual evapotranspiration is the largest predictor of arthropod diversity, with human activity reducing insect richness in tropical and dry zones.

The paper introduces MAP, a metabarcoding pipeline that handles long-read data and provides accurate biodiversity estimates, outperforming existing platforms for COI metabarcoding.

The paper introduces BIP, a pipeline for DNA barcode inference from sequencing data, which outperforms existing tools by accurately distinguishing target and non-target sequences.

The paper tests the performance of Qitan Technology's nanopore sequencer, finding it generates accurate DNA barcodes at a lower cost than Oxford Nanopore Technologies' sequencer.

The paper introduces a pipeline for DNA barcoding using Nanopore sequencing that resolves mixed sequence signals without reference databases, enabling accurate identification of closely related variants.

The paper identifies multiple functional COI barcode variants in North European sawflies using high-throughput sequencing, challenging the one-barcode-per-specimen assumption in DNA barcoding.

This study finds that haplodiploid insect lineages exhibit higher rates of amino acid substitution and more frequent indels in the COI gene compared to diploid lineages.

The paper introduces CORAL, a transfer learning method that improves prediction and inference for rare species using DNA metabarcoding data from Madagascar.

This study examines chimeric sequences in nematode-infected arthropods using nanopore sequencing, finding higher incidence in 18S than COI and linking chimera formation to DNA stability and sequence conservation.

This paper introduces a new workflow to improve the reliability and accuracy of plant DNA barcode databases by addressing post-analytical validation shortcomings and demonstrating the impact of clustering and threshold-based identification.

This paper releases a dataset of 5.6 million arthropod images for training machine learning models in taxonomic identification.

This study demonstrates a cost-effective method for DNA barcoding using Oxford Nanopore Technologies, enabling the processing of 100,000 specimens per flow cell at $0.01 per specimen.

The paper presents a DNA barcode library for Diptera in Churchill, Canada, revealing biodiversity patterns and molecular evolution insights.

This study evaluates seven taxonomic assignment methods for metabarcoding eukaryotes using mock communities, finding that BLAST top hit performs competitively when optimized and that reference database quality impacts accuracy.

debar is an R package that denoises COI-5P DNA barcode data using a profile hidden Markov model, reducing indel errors by 75-94% with a low false correction rate.

This paper describes a low-cost diagnostic test for COVID-19 using sequence variation in the envelope protein gene, enabling analysis of 10,000 samples per run with RNA extraction, RT-PCR, and high-throughput sequencing.

This study investigates whether misidentifications in DNA barcoding of insect species stem from reference libraries or query sequences, finding that errors originated from the latter and proposing a workflow to reduce them.

This study uses eDNA to survey fish and vertebrate communities in a tropical lake, finding new species records and confirming methods for detecting invasive species.

This paper evaluates SMRT sequencing on the SEQUEL platform for amplicon sequencing, finding it provides more complete coverage and reduces costs compared to Sanger sequencing.