Biology subjects

Frank, L. E.

Publications and source records attributed to Frank, L. E..

4 recordsLinked to original sources

bioRxiv2023

Rapid molecular species identification of mammalian scat samples using nanopore adaptive sampling

AO_SCPLOWBSTRACTC_SCPLOWAccurate species identification is essential to mammalogy. Despite this necessity, rapid and accurate identification of cryptic, understudied, and elusive mammals remains challenging. Traditional barcoding of mitochondrial genes is standard for molecular identification but requires time-consuming wet-lab methodologies. Recent bioinformatic advancements for nanopore sequencing data offer exciting opportunities for non-invasive and field-based identification of mammals. Nanopore adaptive sampling (NAS), a PCR-free method, selectively sequences regions of DNA according to user-specified reference databases. Here, we utilized NAS to enrich mammalian mitochondrial genome sequencing to identify species. Fecal DNA extractions were sequenced from nine mammals, several collected in collaboration with Minnesota Tribal Nations, to demonstrate utility for NAS-barcoding of non-invasive samples. By mapping to the entire National Center for Biotechnology Information (NCBI) mammalian mitochondrial reference genome database and bioinformatically analyzing highly similar matches, we successfully produced species identifications for all of our fecal samples. Eight of nine species identifications matched previous PCR or animal/fecal morphological identifications. For the ninth species, our genetic data indicate a misidentification stemming from the original study. Our approach has a range of applications, particularly field-based wildlife research, conservation, disease surveillance, and monitoring of wildlife trade. Of importance to Minnesota tribes is invasive species monitoring, detections, and confirmation as climate impacts causes changes in biodiversity and shifts in species distributions. The rapid assessment techniques described here will be useful as new introductions and range expansions of native and invasive species may first be detected by the presence of signs such as scat rather than direct observations and will be helpful for chronically understaffed tribal natural resources agencies.

genomics↗

bioRxiv2023

The Genome of the Soybean Gall Midge (Resseliella maxima)

The cecidomyiid fly, soybean gall midge, Resseliella maxima Gagne, is a recently discovered insect that feeds on soybean plants in the Midwest US. Resseliella maxima larvae feed on soybean stems which may induce plant death and can cause considerable yield losses, making it an important agricultural pest. From three pools of 50 adults each, we used long-read nanopore sequencing to assemble a R. maxima reference genome. The final genome assembly is 206 Mb with 64.88X coverage, consisting of 1009 contigs with an N50 size of 714 kb. The assembly is high quality with a BUSCO score of 87.8%. Genome-wide GC level is 31.60% and DNA methylation was measured at 1.07%. The R. maxima genome is comprised of 21.73% repetitive DNA, which is in line with other cecidomyiids. Protein prediction annotated 14,798 coding genes with 89.9% protein BUSCO score. Mitogenome analysis indicated that R. maxima assembly is a single circular contig of 15,301 bp and shares highest identity to the mitogenome of the Asian rice gall midge, Orseolia oryzae (Wood-Mason). The R. maxima genome has one of the highest completeness levels for a cecidomyiid and will provide a resource for research focused on the biology, genetics, and evolution of cecidomyiids, as well as plant-insect interactions in this important agricultural pest.

genomics↗

bioRxiv2023

Bridging Big Data: Procedures for Combining Non-equivalent Cognitive Measures from the ENIGMA Consortium

Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. These efforts unveil new questions about integrating data arising from distinct sources and instruments. We focus on the most frequently assessed cognitive domain - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated global raw data from 53 studies totaling N = 10,505 individuals. A mega-analysis was conducted using empirical bayes harmonization to remove site effects, followed by linear models adjusting for common covariates. A continuous item response theory (IRT) model estimated each individuals latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance while preserving covariate effects, and our conversion tool is freely available online. This demonstrates that large-scale data sharing and harmonization initiatives can address reproducibility and integration challenges across the behavioral sciences. TeaserWe present a global effort to devise harmonization procedures necessary to meaningfully leverage big data.

neuroscience↗

bioRxiv2022

Evaluating methods for measuring background connectivity in slow event-related functional MRI designs

Resting-state functional MRI (fMRI) is widely used for measuring functional interactions between brain regions, significantly contributing to our understanding of large-scale brain networks and brain-behavior relationships. Furthermore, idiosyncratic patterns of resting-state connections can be leveraged to identify individuals and predict individual differences in clinical symptoms, cognitive abilities, and other individual factors. Idiosyncratic connectivity patters are thought to persist across task states, suggesting task-based fMRI can be similarly leveraged for individual differences analyses. Here, we tested the degree to which functional interactions occurring in the background of a task during slow event-related fMRI parallel or differ from those captured during resting-state fMRI. We compared two approaches for removing task- evoked activity from task-based fMRI: (1) applying a low-pass filter to remove task- related frequencies in the signal, or (2) extracting residuals from a general linear model (GLM) that accounts for task-evoked responses. We found that the organization of large-scale cortical networks and individuals idiosyncratic connectivity patterns are preserved during task-based fMRI. In contrast, individual differences in connection strength can vary more substantially between rest and task. Compared to low-pass filtering, background connectivity obtained from GLM residuals produced idiosyncratic connectivity patterns and individual differences in connection strength that more resembled rest. However, all background connectivity measures were highly similar when derived from the low-pass filtered signal or GLM residuals, indicating that both methods are suitable for measuring background connectivity. Together, our results highlight new avenues for the analysis of task-based fMRI datasets and the utility of each background connectivity method.

neuroscience↗