R. C. Edgar

The paper introduces Reseek, a protein structure search algorithm achieving the highest accuracy with practical scalability for large databases.

This study introduces CLOAK, an MSA cleaning algorithm that improves gene tree inference by removing alignment errors while preserving informative residues.

This paper organizes a community challenge to develop automated virus taxonomy classification tools and evaluates their performance on a combined dataset of expert-curated and metagenomic sequences.

The paper shows that structure alignment algorithms overestimate significance and introduces a new method for accurate statistical significance estimation.

The paper identifies and validates an alternative to VSV-G for lentivector pseudotyping to improve in vivo T cell transgene delivery for tumor therapy.

Muscle-3D is a scalable multiple structure alignment method that combines structural context with advanced alignment techniques, showing strong performance on benchmark datasets.

Assembling the Sequence Read Archive (27.3 million accessions) with over 100-fold compression enables minutes-scale k-mer search and mining of enzymes, plasmids, proteins and viral reactivation.

This paper introduces Reseek, a protein alignment algorithm that improves sensitivity to remote homologs and speed over existing methods like Foldseek.

The paper introduces Muscle5, an algorithm that generates MSA ensembles to improve confidence in phylogenetic and homology inferences by showing some high-bootstrap topologies are incorrect.

The paper introduces Palmscan, an algorithm for identifying polymerase palmprints in viral sequences, and PALMdb, a database of these palmprints, to classify riboviruses based on polymerase sequence identity.

The paper develops a cloud computing infrastructure for petabase-scale sequence alignment, identifying over 105 novel RNA viruses and expanding known species by an order of magnitude.

The paper introduces syncmers as an alternative to minimizers for selecting conserved k-mers, showing they are more sensitive and robust to mutations.

URMAP is a fast read mapping algorithm that outperforms BWA and Bowtie2 in speed while maintaining comparable accuracy on simulated human genome data.

This paper introduces two new alpha diversity estimators for OTU tables generated from NGS data, showing they are effective under limited conditions but face practical challenges.

An algorithm that flags counts from reads landing in the wrong sample in 16S rRNA OTU tables, catching ~80% to 90% of spurious counts on a mock community.

This paper introduces octave plots to visualize microbial OTU abundance distributions, revealing insights into diversity metrics and experimental artifacts.

The paper reassesses the 97% identity threshold for 16S rRNA OTUs using modern data, finding higher thresholds better approximate species.

The paper estimates cross-talk in 16S amplicon sequencing and introduces UNCROSS, an algorithm to detect and filter cross-talk in OTU tables.

Word counting flags candidate regions and conserved motifs pin down gene boundaries, annotating 16S rRNA genes in microbial genomes and metagenomic sequence with reported sensitivity above 99%.

SINAPS predicts microbial traits from 16S marker gene sequences using a word-counting algorithm, achieving >90% accuracy for several traits.

16S amplicon read frequencies correlate poorly with true microbial species abundances, and the UNBIAS correction method for copy number and primer mismatches yields only modest improvements when reference identity drops.

Quality-score-free UNOISE2 clusters unique Illumina amplicon reads by abundance skew and edit distance with two preset parameters, matching or exceeding DADA2 accuracy on 16S mock communities.