Biology subjects

Teigen, L.

Publications and source records attributed to Teigen, L..

3 recordsLinked to original sources

bioRxiv2023

Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles

Recent advancements in human gut microbiome research have revealed its crucial role in shaping innovative predictive healthcare applications. We introduce Gut Microbiome Wellness Index 2 (GMWI2), an advanced iteration of our original GMWI prototype, designed as a robust, disease-agnostic health status indicator based on gut microbiome taxonomic profiles. Our analysis involved pooling existing 8069 stool shotgun metagenome data across a global demographic landscape to effectively capture biological signals linking gut taxonomies to health. GMWI2 achieves a cross-validation balanced accuracy of 80% in distinguishing healthy (no disease) from non-healthy (diseased) individuals and surpasses 90% accuracy for samples with higher confidence (i.e., outside the "reject option"). The enhanced classification accuracy of GMWI2 outperforms both the original GMWI model and traditional species-level -diversity indices, suggesting a more reliable tool for differentiating between healthy and non-healthy phenotypes using gut microbiome data. Furthermore, by reevaluating and reinterpreting previously published data, GMWI2 provides fresh insights into the established understanding of how diet, antibiotic exposure, and fecal microbiota transplantation influence gut health. Looking ahead, GMWI2 represents a timely pivotal tool for evaluating health based on an individuals unique gut microbial composition, paving the way for the early screening of adverse gut health shifts. GMWI2 is offered as an open-source command-line tool, ensuring it is both accessible to and adaptable for researchers interested in the translational applications of human gut microbiome science.

bioinformatics↗

bioRxiv2023

Common Analysis of Direct RNA SequencinG CUrrently Leads to Misidentification of 5-Methylcytosine Modifications at GCU Motifs

RNA modifications, such as methylation, can be detected with Oxford Nanopore Technologies direct RNA sequencing. One commonly used tool for detecting 5-methylcytosine (m5C) modifications is Tombo, which uses an "Alternative Model" to detect putative modifications from a single sample. We examined direct RNA sequencing data from diverse taxa including virus, bacteria, fungi, and animals. The algorithm consistently identified a 5-methylcytosine at the central position of a GCU motif. However, it also identified a 5-methylcytosine in the same motif in fully unmodified in vitro transcribed RNA, suggesting that this a frequent false prediction. In the absence of further validation, several published predictions of 5-methylcytosine in human coronavirus and human cerebral organoid RNA in a GCU context should be reconsidered. IMPORTANCEThe detection of chemical modifications to RNA is a rapidly expanding field within epigenetics. Nanopore sequencing technology provides an attractive means of detecting these modifications directly on the RNA, but accurate modification predictions are dependent upon the software developed to interpret the sequencing results. One of these tools, Tombo, allows users to detect modifications using sequencing results from a single RNA sample. However, we find that this method falsely predicts modifications in a specific sequence context across a variety of RNA samples, including RNA that lacks modifications. Results from previous publications include predictions in human coronaviruses with this sequence context and should be reconsidered. Our results highlight the importance of using RNA modification detection tools with caution in the absence of a control RNA sample for comparison.

genomics↗

bioRxiv2018

Targeted enrichment outperforms other enrichment techniques and enables more multi-species RNA-Seq analyses

Enrichment methodologies enable analysis of minor members in multi-species transcriptomic analyses. We compared standard enrichment of bacterial and eukaryotic mRNA to targeted enrichment with Agilent SureSelect (AgSS) capture for Brugia malayi, Aspergillus fumigatus, and the Wolbachia endosymbiont of B. malayi (wBm). Without introducing significant systematic bias, the AgSS quantitatively enriched samples, resulting in more reads mapping to the target organism. The AgSS-enriched libraries consistently had a positive linear correlation with its unenriched counterpart (r2=0.559-0.867). Up to a 2,242-fold enrichment of RNA from the target organism was obtained following a power law (r2=0.90), with the greatest fold enrichment achieved in samples with the largest ratio difference between the major and minor members. While using a single total library for prokaryote and eukaryote in a single sample could be beneficial for samples where RNA is limiting, we observed a decrease in reads mapping to protein coding genes and an increase of multi-mapping reads to rRNAs in AgSS enrichments from eukaryotic total RNA libraries as opposed to eukaryotic poly(A)-enriched libraries. Our results support a recommendation of using Agilent SureSelect targeted enrichment on poly(A)-enriched libraries for eukaryotic captures and total RNA libraries for prokaryotic captures to increase the robustness of multi-species transcriptomic studies.

genomics↗