Biology subjects

Pappas, N.

Publications and source records attributed to Pappas, N..

5 recordsLinked to original sources

bioRxiv2023

Benchmarking Bioinformatic Virus Identification Tools Using Real-World Metagenomic Data across Biomes

As most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right tools, parameters, and cutoffs. As all these tools measure different biological signals, and use different algorithms and training/reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance. We compared the performance of ten state-of-the-art virus identification tools in thirteen modes on eight paired viral and microbial datasets from three distinct biomes, including a new complex dataset from Antarctic coastal waters. The tools had highly variable true positive rates (0 - 68%) and false positive rates (0 - 15%). PPR-Meta best distinguished viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identified different subsets of the benchmarking data and all tools, except for Sourmash, found unique viral contigs. Tools performance could be improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered. Together, our independent benchmarking provides guidance on choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments for viromics researchers.

bioinformatics↗

bioRxiv2023

Integration of taxonomic signals from MAGs and contigs improves read annotation and taxonomic profiling of metagenomes

AO_SCPLOWBSTRACTC_SCPLOWMetagenomic analysis typically includes read-based taxonomic profiling, assembly, and binning of metagenome-assembled genomes (MAGs). Here we integrate these steps in Read Annotation Tool (RAT), which uses robust taxonomic signals from MAGs and contigs to enhance read annotation. RAT reconstructs taxonomic profiles with high precision and sensitivity, outperforming other state-of-the-art tools. In high-diversity groundwater samples, RAT annotates a large fraction of the metagenomic reads, calling novel taxa at the appropriate, sometimes high taxonomic ranks. Thus, RAT integrative profiling provides an accurate and comprehensive view of the microbiome. CAT/BAT/RAT is available at https://github.com/MGXlab/CAT. The CAT pack now also supports GTDB annotations.

bioinformatics↗

bioRxiv2022

Copiotrophs dominate rhizosphere microbiomes and growth rate potential is a major factor explaining the rhizosphere effect

The structure and function of the root microbial community is shaped by plant root activity, enriching specific microbial taxa and functions from the surrounding soil as the plant root grows. Knowledge of bacterial rhizosphere competence traits are important for predictive microbiome modeling and the development of viable bioinoculants for sustainable agriculture solutions. In this work we compared growth rate potential, a complex trait that recently became predictable from bacterial genome sequences, to functional traits encoded by proteins. We analyzed 84 paired rhizosphere- and soil-derived 16S rRNA metabarcoding datasets from 18 different plants and soil types, performed differential abundance analyses and estimated growth rates for each bacterial genus. This analysis revealed that bacteria with a high growth rate potential consistently dominated the rhizosphere. Next, we analyzed the genome sequences of 3270 bacterial isolates and 6707 MAGs from 1121 plant- and soil-associated metagenomes, confirming this trend in different bacterial phyla. We next investigated which functional traits were enriched in the rhizosphere, expanding the catalog of rhizosphere-associated traits with hundreds of new functions. When we compared the importance of different functional categories to the predicted growth rate potential using a machine learning model, we found that growth rate potential was the main feature for differentiating rhizosphere and soil bacteria, revealing the broad importance of this factor for explaining the rhizosphere effect. Together, we contribute new understanding of the bacterial traits needed for rhizosphere competence. As this trait may be inferred from (meta-) genome data, our work has implications for understanding bacterial community assembly in the rhizosphere, where many uncultivated bacteria reside.

microbiology↗

bioRxiv2019

The respiratory virome and exacerbations in patients with chronic obstructive pulmonary disease

IntroductionExacerbations are major contributors to morbidity and mortality in patients with chronic obstructive pulmonary disease (COPD), and respiratory bacterial and viral infections are an important trigger for the occurrence of such exacerbations. However, using conventional diagnostic techniques, a causative agent is not always found. Metagenomic next-generation sequencing (mNGS) allows analysis of the complete virome, but has not yet been applied in COPD exacerbations.\n\nObjectivesTo study the respiratory virome in nasopharyngeal samples during COPD exacerbations using mNGS.\n\nStudy design88 nasopharyngeal swabs from 63 patients from the Bergen COPD Exacerbation Study (2006-2010) were analysed by mNGS and in-house qPCR for respiratory viruses. Both DNA and RNA were sequenced simultaneously using an lllumina library preparation protocol with in-house adaptations.\n\nResultsBy mNGS, 23/88 samples tested positive. Sensitivity and specificity were both 96% for diagnostic targets (23/24 and 1067/1120, respectively). Viral pathogens only detected by mNGS were herpes simplex virus type 1 and coronavirus OC43. A positive correlation was found between Cq value and mNGS viral species reads (p=0.008). Patients with viral pathogens had lower percentages of bacteriophages (p<0.000). No correlation was found between viral reads (species and genus level) and clinical markers.\n\nConclusionsThe mNGS protocol used was highly sensitive and specific for semi-quantitative detection of respiratory viruses. Excellent negative predictive value implicates the power of mNGS to exclude any infectious cause in one test, with consequences for clinical decision making. Reduced abundance of bacteriophages in COPD patients with viral pathogens implicates skewing of the virome, and speculatively the bacterial population, during infection.

microbiology↗

bioRxiv2018

METAGENOMIC SEQUENCING FOR COMBINED DETECTION OF RNA AND DNA VIRUSES IN RESPIRATORY SAMPLES FROM PAEDIATRIC PATIENTS

IntroductionViruses are the main cause of respiratory tract infections. Metagenomic next-generation sequencing (mNGS) enables the unbiased detection of all potential pathogens in a clinical sample, including variants and even unknown pathogens. To apply mNGS in viral diagnostics, there is a need for sensitive and simultaneous detection of RNA and DNA viruses. In this study, the performance of an in-house mNGS protocol for routine diagnostics of viral respiratory infections, with single tube DNA and RNA sample-pre-treatment and potential for automated pan-pathogen detection was studied.\n\nMaterials and MethodsThe sequencing protocol and bioinformatics analysis was designed and optimized including the optimal concentration of the spike-in internal controls equine arteritis virus (EAV) and phocine-herpes virus-1 (PhHV-1).The whole genome of PhHV-1 was sequenced and added to the NCBI database. Subsequently, the protocol was retrospectively validated using a selection of 25 respiratory samples with in total 29 positive and 346 negative PCR results, previously sent to the lab for routine diagnostics.\n\nResultsThe results demonstrated that our protocol using Illumina Nextseq 500 sequencing with 10 million reads showed high repeatability. The NCBI RefSeq database as opposed to the NCBI nucleotide database led to enhanced specificity of virus classification. A correlation was established between read counts and PCR cycle threshold value, demonstrating the semi-quantitative nature of viral detection by mNGS. The results as obtained by mNGS appeared condordant with PCR based diagnostics in 25 out of the 29 (86%) respiratory viruses positive by PCR and in 315 of 346 (91%) PCR-negative results. Viral pathogens only detected by mNGS, not present in the routine diagnostic workflow were influenza C, KI polyomavirus, and cytomegalovirus.\n\nConclusionsSensitivity and analytical specificity of this mNGS protocol was comparable with PCR and higher when considering off-PCR target viral pathogens. All potential viral pathogens were detected in one single test, while it simultaneously obtained detailed information on detected viruses.

microbiology↗