Mihai Pop

The paper develops classifiers to detect modified 16S rRNA sequences that pass SILVA database quality controls by using conserved motifs and achieves over 90% sensitivity and specificity.

TIPP-SD improves species detection accuracy in microbiomes, especially for low-abundance species and with sequencing errors, while maintaining reasonable speed.

The paper identifies novel probiotic bacteria associated with IgA responses against enteric pathogens in infants, suggesting their potential use in improving infant formula.

TIPP3 and TIPP3-fast improve metagenomic abundance profiling accuracy, especially for novel genomes and error-containing reads, with TIPP3-fast offering faster runtime while maintaining high accuracy.

Strainy assembles and phases strain haplotypes from long-read metagenome data, outperforming existing methods in accuracy and completeness.

The paper shows that larger reference databases reduce species-level taxonomic resolution for prokaryotic marker genes, suggesting the need for more precise definitions and combined markers for accurate classification.

SeqScreen uses ensemble learning to accurately label pathogenic sequences with functional annotations, achieving high recall and precision for microbial pathogenesis.

This study benchmarks error-correction methods for next-generation sequencing data, finding significant variation in performance across datasets with no single method being optimal overall.

The paper introduces SALSA2, a Hi-C scaffolder that improves accuracy by using assembly graphs to correct inversion errors in chromosome-scale genome assemblies.

MetaCompass improves metagenomic assembly by combining reference-guided and de novo methods, enhancing completeness and accuracy over existing approaches.

The paper benchmarks computational metagenomics software using complex datasets, showing strengths and limitations in assembly, binning, and taxonomic profiling.

The paper develops a scaffolding method using Hi-C data to improve long read genome assembly contiguity and demonstrates its accuracy on two organisms.

The paper introduces iMetAMOS, an automated pipeline for ensemble genome assembly and validation, demonstrating its effectiveness on microbial genomes.