Biology subjects

Callahan, B. J.

Publications and source records attributed to Callahan, B. J..

11 recordsLinked to original sources

bioRxiv2026

Generating, curating, and evaluating trnL reference sequence databases: Benchmarking OBITools3/ecoPCR, RESCRIPt, and MetaCurator

Plant DNA metabarcoding enables the identification of plant taxa in mixed samples, with the trnL (UAA) intron and its P6 loop mini-barcode region performing as well as or better than other commonly used markers. Reliable metabarcoding requires high-quality reference databases, yet a regularly maintained trnL resource is currently lacking. Consequently, most studies use uncurated sequences downloaded directly from public repositories without essential validation. We address these gaps by providing guidance through a systematic comparison of three database curation tools - OBITools3/ecoPCR, RESCRIPt, and MetaCurator - to generate three trnL reference sequence databases and evaluate their classification performance across commonly sequenced trnL regions (CD, CH, and GH). Reference trnL sequences and taxonomy files were retrieved from public sequence repositories and curated using standardized filtering steps to reduce taxonomic errors, sequence ambiguity, and redundancy. Four simulated query datasets--two base sets and their mutated counterparts--were constructed to assess classification performance of the databases using the Naive Bayesian Classifier implemented in DADA2.- The evaluation showed that performance differed by trnL region: MetaCurator and RESCRIPt yielded higher and similar metrics for trnL CD; OBITools3/ecoPCR and RESCRIPt were comparable for trnL CH; and MetaCurator attained the highest performance for trnL GH region. All reference databases, taxonomy, and evaluation files are available at Zenodo (https://doi.org/10.5281/zenodo.17969450). The complete computational workflow and scripts are available on GitHub (https://github.com/oskuddar/trnL_DB). Although evaluation was focused on plant taxa in the United States, the resulting databases are suitable for use as global trnL reference databases.

bioinformatics↗

bioRxiv2025

Assessing contamination in DNA extraction kits commonly used for microbiome research

Sequencing-based measurements are now routinely used to investigate the microbial world, however, contamination by DNA outside the intended sample remains a problem. Contaminants obscure the true microbial signal and can lead to misleading scientific interpretations. Much work has been done to address the effects of these contaminants including best practices as outlined in Eisenhofer et al. and Fierer et al. Yet, even with best practices in place, the current literature consensus is that contaminants remain impactful, at least in low biomass environments (5, 7, 11, 13, 16). One well-known source of contaminants are those found within DNA extraction kits, as was shown clearly in the pioneering work of Salter et al. 2012 and Karstens et al. 2019. However, given the rapid evolution of DNA sequencing methods, it would be worthwhile to revisit the issue of contaminants in contemporary DNA extraction kits (the "kitome"). Here we provide an updated characterization of the kitomes of DNA extraction kits commonly used for microbiome research. ImportanceMicrobial contamination in commonly used DNA extraction kits has not been recently assessed. Here we evaluate the contamination in DNA extraction kits commonly used in microbiome studies over the past several years, and provide actionable guidance on appropriate DNA extraction kits for low biomass microbiome measurements.

molecular biology↗

bioRxiv2023

Serovar-level Identification of Bacterial Foodborne Pathogens From Full-length 16S rRNA Gene Sequencing

The resolution of variation within species is critical for interpreting and acting on many microbial measurements. In the key foodborne pathogens Escherichia coli and Salmonella, the primary sub-species classification scheme used is serotyping: differentiating variants within these species by surface antigen profiles. Serotype prediction from whole-genome sequencing (WGS) of isolates is now seen as comparable or preferable to traditional laboratory methods where WGS is available. However, laboratory and WGS methods depend on an isolation step that is time-consuming and incompletely represents the sample when multiple strains are present. Community sequencing approaches that skip the isolation step are therefore of interest for pathogen surveillance. Here we evaluated the viability of amplicon sequencing of the full-length 16S rRNA gene for serotyping S. enterica and E. coli. We developed a novel algorithm for serotype prediction, implemented as an R package (Seroplacer), which takes as input full-length 16S rRNA gene sequences and outputs serovar predictions after phylogenetic placement into a reference phylogeny. We achieved over 89% accuracy in predicting Salmonella serotypes on in silico test data, and identified key pathogenic serovars of Salmonella and E. coli in isolate and environmental test samples. Although serotype prediction from 16S sequences is not as accurate as serotype prediction from WGS of isolates, the potential to identify dangerous serovars directly from amplicon sequencing of environmental samples is intriguing for pathogen surveillance. The capabilities developed here are also broadly relevant to other applications where intra-species variation and direct sequencing from environmental samples could be valuable.

microbiology↗

bioRxiv2023

Gardnerella Diversity and Ecology in Pregnancy and Preterm Birth

The vaginal microbiome has been linked to numerous negative health outcomes including preterm birth. Specific taxa, including Gardnerella spp., have been identified as risk factors for these conditions. Historically, microbiome analysis methods have treated all Gardnerella spp. as one species, but the broad diversity of Gardnerella has recently become more apparent. In the present study, we explore the diversity of Gardnerella clades and genomic species in the vaginal microbiome of pregnant women and their impacts on microbiome composition and associations with preterm birth. Shotgun metagenomic sequencing data collected longitudinally from three distinct cohorts of pregnant women were assessed. Relative abundance of Gardnerella clades and genomic species and other taxa was quantified, and associations between Gardnerella clades and signatures of the vaginal microbiome were measured. We also assessed the diversity and abundance of Gardnerella variants in 16S rRNA gene amplicon sequencing data from seven previously conducted studies in differing populations on the vaginal microbiome and preterm birth. Individual microbiomes often contained multiple Gardnerella variants, and the number of clades was associated with increased microbial load. The genus Gardnerella was also associated with increased microbial load, or the ratio of non-human reads to human reads. Taxon co-occurrence patterns matched previously described community structures, and were largely consistent across Gardnerella clades and among cohorts. Some variants previously described as rare were prevalent in other cohorts, highlighting the importance of surveying a diverse set of populations to fully capture the diversity of Gardnerella. The diversity of Gardnerella both across populations and within individual vaginal microbiomes has long been unappreciated, as has been the intra-species diversity of many other members of the vaginal microbiome.1 The broad genomic diversity of Gardnerella has led to its reclassification as multiple species; here we demonstrate the diversity of Gardnerella found within and between vaginal microbiomes. Further studies should investigate the phenotypes of Gardnerella variants that may underlie the mechanisms by which Gardnerella species may differentially shape the vaginal microbiome.

microbiology↗

bioRxiv2022

Implications of taxonomic bias for microbial differential-abundance analysis

Differential-abundance (DA) analyses enable microbiome researchers to assess how microbial species vary in relative or absolute abundance with specific host or environmental conditions, such as health status or pH. These analyses typically use sequencing-based community measurements that are taxonomically biased to measure some species more efficiently than others. Understanding the effects that taxonomic bias has on the results of a DA analysis is essential for achieving reliable and translatable findings; yet currently, these effects are unknown. Here, we characterized these effects for DA analyses of both relative and absolute abundances, using a combination of mathematical theory and data analysis of real and simulated case studies. We found that, for analyses based on species proportions, taxonomic bias can cause significant errors in DA results if the average measurement efficiency of the community is associated with the condition of interest. These errors can be avoided by using more robust DA methods (based on species ratios) or quantified and corrected using appropriate controls. Wide adoption of our recommendations can improve the reproducibility, interpretability, and translatability of microbiome DA studies. This manuscript was rendered from commit 7412a36 of https://github.com/mikemc/differential-abundance-theory. Supporting data analyses can be found in the accompanying computational research notebook. Please post comments or questions on GitHub. The manuscript is licensed under a CC BY 4.0 License. See the GitHub Releases or Zenodo record for earlier versions.

bioinformatics↗

bioRxiv2020

Ultra-accurate Microbial Amplicon Sequencing with Synthetic Long Reads

Out of the many pathogenic bacterial species that are known, only a fraction are readily identifiable directly from a complex microbial community using standard next generation DNA sequencing technology. Long-read sequencing offers the potential to identify a wider range of species and to differentiate between strains within a species, but attaining sufficient accuracy in complex metagenomes remains a challenge. Here, we describe and analytically validate LoopSeq, a commercially-available synthetic long-read (SLR) sequencing technology that generates highly-accurate long reads from standard short reads. LoopSeq reads are sufficiently long and accurate to identify microbial genes and species directly from complex samples. LoopSeq applied to full-length 16S rRNA genes from known strains in a microbial community perfectly recovered the full diversity of full-length exact sequence variants in a known microbial community. Full-length LoopSeq reads had a per-base error rate of 0.005%, which exceeds the accuracy reported for other long-read sequencing technologies. 18S-ITS and genomic sequencing of fungal and bacterial isolates confirmed that LoopSeq sequencing maintains that accuracy for reads up to 6 kilobases in length. Analysis of rinsate from retail meat samples demonstrated that LoopSeq full-length 16S rRNA synthetic long-reads could accurately classify organisms down to the species level, and could differentiate between different strains within species identified by the CDC as potential foodborne pathogens. The order-of-magnitude improvement in both length and accuracy over standard Illumina amplicon sequencing achieved with LoopSeq enables accurate species-level and strain identification from complex and low-biomass microbiome samples. The ability to generate accurate and long microbiome sequencing reads using standard short read sequencers will accelerate the building of quality microbial sequence databases and removes a significant hurdle on the path to precision microbial genomics.Competing Interest StatementMichael Balamotis and Tuval Ben Yehezkel are employees of Loop Genomics, the vendor for the synthetic long-read sequencing technology analyzed in this manuscript.View Full Text

microbiology↗

bioRxiv2019

Characterization of C. difficile strains isolated from companion animals and the associated changes in the host fecal microbiota

BackgroundClostridioides difficile is an enteric pathogen historically known to cause hospital associated (HA)-infections in humans. A major risk factor for CDI in humans is antibiotic usage as it alters the gut microbiota and there is a loss of colonization resistance against C. difficile. In recent years there has been an increase in community associated (CA)-C. difficile infection that does not have the same risk factors as HA-CDI. Potential sources of CA-CDI have been proposed and include animals, food, water, and the environment, however these sources remain poorly investigated. Here, we define the prevalence of C. difficile strains found in different companion animals (canines, felines, and equines) to investigate a potential zoonotic link. C. difficile strains were identified by toxin gene profiling, fluorescent PCR ribotyping, and antimicrobial susceptibility testing. 16s rRNA gene sequencing was done on animal feces to investigate the relationship between the presence of C. difficile and the gut microbiota in different hosts.\n\nResultsHere, we show that C. difficile was recovered from 20.9% of samples (42/201), which included 33 canines, 2 felines, and 7 equines. Over 69% (29/42) of the isolates were toxigenic and belonged to 14 different ribotypes, with overlap between HA- and CA-CDI cases in humans. The presence of C. difficile results in a shift in the fecal microbial community structure in both canines and equines. Commensal Clostridia C. hiranonis was negatively associated with C. difficile in canines. Further experimentation showed a clear antagonistic relationship between the two strains in vitro, suggesting that commensal Clostridia might play a role in colonization resistance against C. difficile in different hosts.\n\nConclusionsIn this study we investigated a potentially important source of C. difficile transmission: the companion animal population. C. difficile carriage was common in dogs, cats, and horses. C. difficile isolates from companion animals included many of the same ribotypes known to cause HA- and CA-CDI in humans, and had similar antimicrobial resistance profiles as those isolated from human populations. These data contribute to our understanding of non-hospital exposure to C. difficile in the human population and suggest new avenues for reducing C. difficile prevalence in companion animals and, perhaps, thereby reducing CA-CDI in humans.

microbiology↗

bioRxiv2019

Consistent and correctable bias in metagenomic sequencing measurements

Measurements of biological communities by marker-gene and metagenomic sequencing are biased: The measured relative abundances of taxa or their genes are systematically distorted from their true values because each step in the experimental workflow preferentially detects some taxa over others. Bias can lead to qualitatively incorrect conclusions and makes measurements from different protocols quantitatively incomparable. A rigorous understanding of bias is therefore essential. Here we propose, test, and apply a simple mathematical model of how bias distorts marker-gene and metagenomics measurements: Bias multiplies the true relative abundances within each sample by taxon-and protocol-specific factors that describe the different efficiencies with which taxa are detected by the workflow. Critically, these factors are consistent across samples with different compositions, allowing bias to be estimated and corrected. We validate this model in 16S rRNA gene and shotgun metagenomics data from bacterial communities with defined compositions. We use it to reason about the effects of bias on downstream statistical analyses, finding that analyses based on taxon ratios are less sensitive to bias than analyses based on taxon proportions. Finally, we demonstrate how this model can be used to quantify bias from samples of defined composition, partition bias into steps such as DNA extraction and PCR amplification, and to correct biased measurements. Our model improves on previous models by providing a better fit to experimental data and by providing a composition-independent approach to analyzing, measuring, and correcting bias.

bioinformatics↗

bioRxiv2018

High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution

Targeted PCR amplification and high-throughput sequencing (amplicon sequencing) of 16S rRNA gene fragments is widely used to profile microbial communities. New long-read sequencing technologies can sequence the entire 16S rRNA gene, but higher error rates have limited their attractiveness when accuracy is important. Here we present a high-throughput amplicon sequencing methodology based on PacBio circular consensus sequencing and the DADA2 sample inference method that measures the full-length 16S rRNA gene with single-nucleotide resolution and a near-zero error rate.\n\nIn two artificial communities of known composition, our method recovered the full complement of full-length 16S sequence variants from expected community members without residual errors. The measured abundances of intra-genomic sequence variants were in the integral ratios expected from the genuine allelic variants within a genome. The full-length 16S gene sequences recovered by our approach allowed E. coli strains to be correctly classified to the O157:H7 and K12 sub-species clades. In human fecal samples, our method showed strong technical replication and was able to recover the full complement of 16S rRNA alleles in several E. coli strains.\n\nThere are likely many applications beyond microbial profiling for which high-throughput amplicon sequencing of complete genes with single-nucleotide resolution will be of use.

microbiology↗

bioRxiv2017

Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data

BackgroundThe accuracy of microbial community surveys based on marker-gene and metagenomic sequencing (MGS) suffers from the presence of contaminants -- DNA sequences not truly present in the sample. Contaminants come from various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do not eliminate it. Here we introduce decontam (https://github.com/benjjneb/decontam), an open-source R package that implements a statistical classification procedure that identifies contaminants in MGS data based on two widely reproduced patterns: contaminants appear at higher frequencies in low-concentration samples, and are often found in negative controls.\n\nResultsdecontam classified amplicon sequence variants (ASVs) in a human oral dataset consistently with prior microscopic observations of the microbial taxa inhabiting that environment and previous reports of contaminant taxa. In metagenomics and marker-gene measurements of a dilution series, decontam substantially reduced technical variation arising from different sequencing protocols. The application of decontam to two recently published datasets corroborated and extended their conclusions that little evidence existed for an indigenous placenta microbiome, and that some low-frequency taxa seemingly associated with preterm birth were contaminants.\n\nConclusionsdecontam improves the quality of metagenomic and marker-gene sequencing by identifying and removing contaminant DNA sequences. decontam integrates easily with existing MGS workflows, and allows researchers to generate more accurate profiles of microbial communities at little to no additional cost.

bioinformatics↗

bioRxiv2017

Exact sequence variants should replace operational taxonomic units in marker gene data analysis

Recent advances have made it possible to analyze high-throughput marker-gene sequencing data without resorting to the customary construction of molecular operational taxonomic units (OTUs): clusters of sequencing reads that differ by less than a fixed dissimilarity threshold. New methods control errors sufficiently that sequence variants (SVs) can be resolved exactly, down to the level of single-nucleotide differences over the sequenced gene region. The benefits of finer taxonomic resolution are immediately apparent, and arguments for SV methods have focused on their improved resolution. Less obvious, but we believe more important, are the broad benefits deriving from the status of SVs as consistent labels with intrinsic biological meaning identified independently from a reference database. Here we discuss how those features grant SVs the combined advantages of closed-reference OTUs -- including computational costs that scale linearly with study size, simple merging between independently processed datasets, and forward prediction -- and of de novo OTUs -- including accurate diversity measurement and applicability to communities lacking deep coverage in reference databases. We argue that the improvements in reusability, reproducibility and comprehensiveness are sufficiently great that SVs should replace OTUs as the standard unit of marker gene analysis and reporting.

microbiology↗