Biology subjects

Pop, M.

Publications and source records attributed to Pop, M..

14 recordsLinked to original sources

bioRxiv2026

Detecting Random Mutations in 16S rRNA Sequences

Motivation: High-throughput sequencing technologies have driven rapid growth of biological sequence databases. Public repositories must therefore rely on automated computational heuristics to screen submitted sequences for errors and low quality. For example, SILVA SSU Ref, which exploits the conserved nature of 16S and 18S rRNA sequences, applies strict algorithmic quality controls yet still accepts sequences with up to 30% of their nucleotides deviating from any previously accepted sequence. This permissiveness creates opportunities for the admission of modified sequences, such as biologically plausible sequences generated by DNA foundation models. The vulnerability of public sequence databases to becoming polluted or poisoned with modified sequences necessitates the development of methods to detect such sequences. Results: We present the first investigation, to our knowledge, of the detectability of modified sequences. We consider simple computationally-modified 16S rRNA sequences that pass the quality control inclusion criteria of the SILVA database, which we generate via random substitutions. We present classifiers that can distinguish such modified sequences from natural 16S rRNA using conserved motifs. Our best classifier achieves over 90% sensitivity and specificity on our testing set when using a 5% artificial mutation rate. One feature used in our classifiers, gapped k-mers constructed from universally conserved nucleotides, was conserved across all three domains of life despite relying on exact matches to patterns found in E. coli, advancing our understanding of conserved grammatical structure in small subunit rRNA sequences. Availability and Implementation: Our source code is available at https://github.com/rainhaworth/16S-Mutation-Classifiers.

bioinformatics↗

bioRxiv2025

Discovery of Novel Probiotic Species to Improve Infant Health

Selecting an appropriate infant formula can be challenging for parents given the wide range of available options, which may lead to suboptimal choices or frequent switching. Our recent studies in mice demonstrated that the maternal microbiota--particularly Limosilactobacillus reuteri present in breast milk--plays a critical role in shaping neonatal immunity by promoting the production of immunoglobulin A (IgA), a key antibody for defense against enteric pathogens. To extend these findings to humans, we analyzed fecal samples from healthy breastfed infants using shotgun metagenomic sequencing combined with whole-bacterial-cell ELISA to assess associations between specific taxa and pathogen-reactive IgA. We found that members of the Oscillibacter species (including Oscillibacter sp. KLE1728, KLE1745, PC13, PEA192, and O. valericigenes) and Morganella (e.g., M. morganii, Morganella sp. EGD-HP17, and Morganella sp. HMSC11D09) were strongly correlated with IgA responses against Salmonella and Shigella. In contrast, IgA reactive to enterohemorrhagic Escherichia coli (EHEC) was associated with other genera, including Staphylococcus. Notably, Lactobacillus species (L. bombi, L. kefiri, L. equi, and L. rhamnosus) were consistently linked to elevated IgA responses against Salmonella, Shigella, and EHEC. Moreover, levels of anti-Salmonella and anti-Shigella IgA were positively correlated with the use of infant formula containing prebiotic fibers or human milk oligosaccharides (5-HMO). Collectively, these findings highlight novel commensal taxa with potential as probiotic candidates to guide the development of next-generation infant formulas aimed at enhancing mucosal immunity and supporting infant health. ImportanceEarly-life nutrition shapes immune maturation, yet the microbial factors driving mucosal antibody responses remain poorly defined. Through integrated metagenomic and IgA profiling, this study identifies commensal taxa, particularly Lactobacillus, Oscillibacter, and Morganella, associated with pathogen-specific IgA in infants. These taxa may enhance mucosal defense against enteric pathogens, revealing key microbial contributors to early immune development. The findings provide a foundation for designing next-generation infant formulas that leverage targeted probiotics or prebiotics to promote protective IgA responses and gut health.

microbiology↗

bioRxiv2025

TIPP-SD: A New Method for Species Detection in Microbiomes

In this study, we present TIPP-SD (i.e., TIPP for Species Detection), a new technique for species detection in a microbiome sample. TIPP-SD uses a modified version of TIPP3, which is a recently developed abundance profiling tool based on maximum likelihood phylogenetic placement into marker gene taxonomies. TIPP-SD depends on a parameter (i.e., "thresh-old") for the required support for species detection, thus allowing us to compute a precision-recall curve as we vary this parameter. In comparing the precision-recall curves for TIPP-SD, TIPP3, Kraken2, Bracken, and Metapresence, we find that TIPP-SD improves on the other methods with respect to accuracy under conditions where some species occur in low abundance, or where there is sequencing error. Under easier conditions, TIPP-SD is close to the best of these methods. Finally, although TIPP-SD is slower than Kraken2 and Bracken, it is still fast enough to be used on large datasets.

bioinformatics↗

bioRxiv2024

TIPP3 and TIPP3-fast: Improved Abundance Profiling in Metagenomics

We present TIPP3 and TIPP3-fast, new tools for abundance profiling in metagenomic datasets. Like its predecessor, TIPP2, the TIPP3 pipeline uses a maximum likelihood approach to place reads into labeled taxonomies using marker genes, but it achieves superior accuracy to TIPP2 by enabling the use of much larger taxonomies through improved algorithmic techniques. We show that TIPP3 outperforms leading methods for abundance profiling in two important contexts: when reads come from genomes not already in a public database (i.e., novel genomes) and when reads contain sequencing errors. We also show that TIPP3-fast has slightly lower accuracy than TIPP3, but is still more accurate than other leading methods and uses a small fraction of TIPP3s runtime. Additionally, we highlight the potential benefits of restricting abundance profiling methods to those reads that map to marker genes (i.e., using a filtered marker-gene based analysis), which we show typically improves accuracy. TIPP3 is freely available at https://github.com/c5shen/TIPP3. Author summaryTIPP3 is a new marker gene-based abundance profiling tool that builds on TIPP and TIPP2 with significant enhancements. TIPP3 supports larger reference packages ([~] 55,000 sequences per marker gene) and achieves higher accuracy in abundance profiling, especially with challenging input reads containing sequencing errors or novel genomes. TIPP3 outperforms TIPP2 and other leading methods in profiling accuracy, and its fast version TIPP3-fast is competitive in runtime with the competing methods while being more accurate under challenging conditions. TIPP3 is open-source and available at https://github.com/c5shen/TIPP3.

bioinformatics↗

bioRxiv2023

Database size positively correlates with the loss of species-level taxonomic resolution for the 16S rRNA and other prokaryotic marker genes

For decades, the 16S rRNA gene has been used to taxonomically classify prokaryotic species and to taxonomically profile microbial communities. The 16S rRNA gene has been criticized for being too conserved to differentiate between distinct species. We argue that the inability to differentiate between species is not a unique feature of the 16S rRNA gene. Rather, we observe the gradual loss of species-level resolution for other marker genes as the number of gene sequences increases in reference databases. We demonstrate this effect through the analysis of three commonly used databases of nearly-universal prokaryotic marker genes: the SILVA 16S rRNA gene database, the Genome Taxonomy Database (GTDB), and a set of 40 taxonomically-informative single-copy genes. Our results reflect a more fundamental property of the taxonomies themselves and have broad implications for bioinformatic analyses beyond taxonomic classification. Effective solutions for fine-level taxonomic classification require a more precise, and operationally-relevant, definition of the taxonomic labels being sought, and the use of combinations of genomic markers in the classification process. ImportanceThe use of reference databases for assigning taxonomic labels to genomic and metagenomic sequences is a fundamental bioinformatic task in the characterization of microbial communities. The increasing accessibility of high throughput sequencing has led to a rapid increase in the size and number of sequences in databases. This has been beneficial for improving our understanding of the global microbial genetic diversity. However, there is evidence that as the microbial diversity is more densely sampled, increasingly longer genomic segments are needed to differentiate between distinct species. The scientific community needs to be aware of this issue and needs to develop methods that better account for it when assigning taxonomic labels to metagenomic sequences from microbial communities.

bioinformatics↗

bioRxiv2023

stRainy: assembly-based metagenomic strain phasing using long reads

Bacterial species in microbial communities are often represented by mixtures of strains, distinguished by small variations in their genomes. Despite the functional importance of intra-strain heterogeneity, its characterization from metagenomic sequencing data remains challenging. Short-read approaches can be used to detect small-scale variation between strains, but fail to phase these variants into contiguous haplotypes. Long-read metagenome assemblers can generate contiguous bacterial chromosomes, but often suppress strain-level variation in favor of species-level consensus. Here we present Strainy - an algorithm for strain-level metagenome assembly and phasing from Nanopore and HiFi reads. Strainy takes a de novo metagenomic assembly as input, identifies strain variants which are then phased and assembled into contiguous haplotypes. Using simulated and mock Nanopore and HiFi metagenome data, we show that Strainy assembles accurate and complete strain haplotypes, outperforming current Nanopore-based methods and comparable with HiFi-based algorithms in completeness and accuracy. We then use Strainy to assemble strain haplotypes of a complex environmental metagenome, revealing distinct mutational patterns in bacterial species.

bioinformatics↗

bioRxiv2021

Paraoxonase and acylated homoserine lactones in urine from patients with urinary tract infections--relationship to microbial diversity by 16S rRNA gene sequencing

Paraoxonase (PON) comprises a trio of mammalian enzymes that have been reported to have a number of roles including the inhibition of bacterial virulence and biofilm formation by microorganisms that quorum sense with acylated homoserine lactones (AHLs). PON have previously been reported to inhibit P. aeruginosa biofilm formation in mammalian airways and skin. An innate immune role for PON in urinary tract infection has not previously been reported. We performed western blots for PON1 in urine from patients with urinary tract infection (UTI), and also tested UTI urine for the presence of AHLs using a cellular reporter system. Urine sample microbiota was assessed through sequencing of the 16S rRNA marker gene. We report here that PON1 was not found in the urine of control subjects, however, in patients with UTI, PON1 was associated with the presence of E. coli in urine. AHLs, but not PON, were found in the bulk urine of those with P. aeruginosa UTI. Microbial consortia of PON positive UTI urine was found to be distinct from PON negative UTI urine; differentially over-represented bacteria in PON positive samples included a number of environmental opportunists. We hypothesize that PON may inhibit the quorum sensing activity of AHLs in UTI, as has previously described in skin and airways.

microbiology↗

bioRxiv2021

SeqScreen: Accurate and Sensitive Functional Screening of Pathogenic Sequences via Ensemble Learning

The COVID-19 pandemic has emphasized the importance of detecting known and emerging pathogens from clinical and environmental samples. However, robust characterization of pathogenic sequences remains an open challenge. To this end, we developed SeqScreen, which can accurately characterize short nucleotide sequences using taxonomic and functional labels, and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed pathogen characterization and is available for download at: www.gitlab.com/treangenlab/seqscreen

bioinformatics↗

bioRxiv2021

The capacity to produce hydrogen sulfide (H2S) via cysteine degradation is ubiquitous in the human gut microbiome

As one of the three mammalian gasotransmitters, hydrogen sulfide (H2S) plays a major role in maintaining physiological homeostasis. Endogenously produced H2S plays numerous beneficial roles including mediating vasodilation and conferring neuroprotection. Due to its high membrane permeability, exogenously produced H2S originating from the gut microbiota can also influence human physiology and is implicated in reducing intestinal mucosal integrity and potentiating genotoxicity and is therefore a potential target for therapeutic interventions. Gut microbial H2S production is often attributed to dissimilatory sulfate reducers such as Desulfovibrio and Bilophila species. However, an alternative source for H2S production, cysteine degradation, is present in gut microbes, but the genes responsible for cysteine degradation have not been systematically annotated in gut microbes. To better understand the potential for H2S production via cysteine degradation by the human gut microbiome, we performed a comprehensive search for genes encoding cysteine-degrading genes in 4,644 bacterial genomes from the Unified Human Gastrointestinal Genome (UHGG) catalogue. We identified 407 gut bacterial species as putative cysteine degrading bacteria, 328 of which have not been previously implicated in H2S production. We identified the presence of at least one putative cysteine degrading bacteria in metagenomic data of 100% of 6,644 healthy subjects and the expression of cysteine-degrading genes in metatranscriptomics data of 100% of 59 samples. Additionally, putative cysteine-degrading bacteria are more abundant than sulfate reducing bacteria (p<2.2e-16). Overall, this study improves our understanding of the capacity for H2S production by the human gut microbiome and may help to inform interventions to therapeutically modulate gut microbial H2S production.

microbiology↗

bioRxiv2019

Benchmarking of computational error-correction methods for next-generation sequencing via unique molecular identifiers

BackgroundRecent advancements in next-generation sequencing have rapidly improved our ability to study genomic material at an unprecedented scale. Despite substantial improvements in sequencing technologies, errors present in the data still risk confounding downstream analysis and limiting the applicability of sequencing technologies in clinical tools. Computational error-correction promises to eliminate sequencing errors, but the relative accuracy of error correction algorithms remains unknown. ResultsIn this paper, we evaluate the ability of error-correction algorithms to fix errors across different types of datasets that contain various levels of heterogeneity. We highlight the advantages and limitations of computational error correction techniques across different domains of biology, including immunogenomics and virology. To demonstrate the efficacy of our technique, we apply the UMI-based high-fidelity sequencing protocol to eliminate sequencing errors from both simulated data and the raw reads. We then perform a realistic evaluation of error correction methods. ConclusionsIn terms of accuracy, we find that method performance varies substantially across different types of datasets with no single method performing best on all types of examined data. Finally, we also identify the techniques that offer a good balance between precision and sensitivity

bioinformatics↗

bioRxiv2018

Assignment of virus and antimicrobial resistance genes to microbial hosts in a complex microbial community by combined long-read assembly and proximity ligation

The characterization of microbial communities by metagenomic approaches has been enhanced by recent improvements in short-read sequencing efficiency and assembly algorithms. We describe the results of adding long-read sequencing to the mix of technologies used to assemble a highly complex cattle rumen microbial community, and compare the assembly to current short read-based methods applied to the same sample. Contigs in the long-read assembly were 7-fold longer on average, and contained 7-fold more complete open reading frames (ORF), than the short read assembly, despite having three-fold lower sequence depth. The linkages between long-read contigs, provided by proximity ligation data, supported identification of 188 novel viral-host associations in the rumen microbial community that suggest cross-species infectivity of specific viral strains. The improved contiguity of the long-read assembly also identified 94 antimicrobial resistance genes, compared to only seven alleles identified in the short-read assembly. Overall, we demonstrate a combination of experimental and computational methods that work synergistically to improve characterization of biological features in a highly complex rumen microbial community.

microbiology↗

bioRxiv2018

Integrating Hi-C links with assembly graphs for chromosome-scale assembly

Long-read sequencing and novel long-range assays have revolutionized de novo genome assembly by automating the reconstruction of reference-quality genomes. In particular, Hi-C sequencing is becoming an economical method for generating chromosome-scale scaffolds. Despite its increasing popularity, there are limited open-source tools available. Errors, particularly inversions and fusions across chromosomes, remain higher than alternate scaffolding technologies. We present a novel open-source Hi-C scaffolder that does not require an a priori estimate of chromosome number and minimizes errors by scaffolding with the assistance of an assembly graph. We demonstrate higher accuracy than the state-of-the-art methods across a variety of Hi-C library preparations and input assembly sizes. The Python and C++ code for our method is openly available at https://github.com/machinegun/SALSA\n\nAuthor summaryHi-C technology was originally proposed to study the 3D organization of a genome. Recently, it has also been applied to assemble large eukaryotic genomes into chromosome-scale scaffolds. Despite this, there are few open source methods to generate these assemblies. Existing methods are also prone to small inversion errors due to noise in the Hi-C data. In this work, we address these challenges and develop a method, named SALSA2. SALSA2 uses sequence overlap information from an assembly graph to correct inversion errors and provide accurate chromosome-scale assemblies.

bioinformatics↗

bioRxiv2017

MetaCompass: Reference-guided Assembly of Metagenomes

Metagenomic studies have primarily relied on de novo approaches for reconstructing genes and genomes from microbial mixtures. While database driven approaches have been employed in certain analyses, they have not been used in the assembly of metagenomes. Here we describe the first effective approach for reference-guided metagenomic assembly of low-abundance bacterial genomes that can complement and improve upon de novo metagenomic assembly methods. When combined with de novo assembly approaches, we show that MetaCompass can generate more complete assemblies than can be obtained by de novo assembly alone, and improve on assemblies from the Human Microbiome Project (over 2,000 samples).

bioinformatics↗

bioRxiv2016

Scaffolding of long read assemblies using long range contact information

MotivationLong read technologies have made a revolution in de novo genome assembly by generating contigs of size orders of magnitude more than that of short read assemblies. Although the assembly contiguity has increased, it still does not span a chromosome or an arm of the chromosome, resulting in an unfinished chromosome level assembly. To address this problem, we develop a scalable and computationally efficient scaffolding method that can boost the contiguity of the assembly by a large extent using genome wide chromatin interaction data such as Hi-C. Particularly, we demonstrate an algorithm that uses Hi-C data for longer-range scaffolding of de novo long read genome assemblies.\n\nResultsWe tested our methods on two long read assemblies of different organisms. We compared our method with previously developed method and show that our approach performs better in terms of accuracy of scaffolding.\n\nAvailabilityThe software is available for free use and can be downloaded from here: https://github.com/machinegun/hi-c-scaffold\n\nContactjchin@pacificbiosciences.com

genomics↗