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DNA metabarcoding captures different macroinvertebrate biodiversity than morphological identification approaches across a continental scale
Article describes how DNA-based aquatic biomonitoring methods show promise to provide rapid, standardized, and efficient biodiversity assessment to supplement and in some cases replace current morphology-based approaches that are often less efficient and can produce inconsistent results. The authors present a comparison of DNA metabarcoding and morphological identification, leveraging national-scale, open-source, ecological datasets from the National Ecological Observatory Network.
Date:
July 19, 2023
Creator:
Emmons, Sean C.; Compson, Zacchaeus Greg; Malish, Megan C.; Busch, Michelle H.; Saenz, Veronica; Higgins, Kierstyn T. et al.
System:
The UNT Digital Library
Benchmarking Metagenomic Classifiers on Simulated Ancient and Modern Metagenomic Data
Article describes how taxonomic profiling of ancient metagenomic samples is challenging due to the accumulation of specific damage patterns on DNA over time. The authors performed a comprehensive evaluation on simulated metagenomes representing human dental calculus microbiome, with the level of DNA damage successively raised to mimic modern to ancient metagenomes.
Date:
October 2, 2023
Creator:
Pusadkar, Vaidehi & Azad, Rajeev K.
System:
The UNT Digital Library
Using Machine Learning to Predict Genes Underlying Differentiation of Multipartite and Unipartite Traits in Bacteria
Article describes how, since the discovery of the second chromosome in the Rhodobacter spaeroides 2.4.1 in 1989 and the revelation of gene sequences, multipartite genomes have been reported in over three hundred bacterial species under nine different phyla. In this study, the authors have attempted to leverage machine learning as a means to identify the genetic factors that underlie the differentiation of bacteria with multipartite and unipartite genomes.
Date:
November 13, 2023
Creator:
Almalki, Fatemah; Sunuwar, Janak & Azad, Rajeev K.
System:
The UNT Digital Library
A gene network-driven approach to infer novel pathogenicity-associated genes: application to Pseudomonas aeruginosa PAO1
Article describes how within the genome of a pathogen is the information regarding factors responsible for its pathogenicity. the authors of the article developed a novel pipeline that uses standard protocol in combination with gene co-expression network of a pathogen constructed using publicly available RNA-Seq data sets.
Date:
November 3, 2023
Creator:
De, Ronika; Whiteley, Marvin & Aza, Rajeev K.
System:
The UNT Digital Library