Conifers are the dominant plant species throughout the high latitude boreal forests as well as some lower latitude temperate forests of North America, Europe, and Asia. As such, they play an integral economic and ecological role across much of the world. This study focused on the characterization of needle transcriptomes from four ecologically important and understudied North American white pines within the Pinus subgenus Strobus. The populations of many Strobus species are challenged by native and introduced pathogens, native insects, and abiotic factors. RNA from the needles of western white pine (Pinus monticola), limber pine (Pinus flexilis), whitebark pine (Pinus albicaulis), and sugar pine (Pinus lambertiana) was sampled, Illumina short read sequenced, and de novo assembled. The assembled transcripts and their subsequent structural and functional annotations were processed through custom pipelines to contend with the challenges of non-model organism transcriptome validation. Orthologous gene family analysis of over 58,000 translated transcripts, implemented through Tribe-MCL, estimated the shared and unique gene space among the four species. This revealed 2025 conserved gene families, of which 408 were aligned to estimate levels of divergence and reveal patterns of selection. Specific candidate genes previously associated with drought tolerance and white pine blister rust resistance in conifers were investigated.
Summary: Long read sequencing platforms, which include the widely used Pacific Biosciences (PacBio) platform and the emerging Oxford Nanopore platform, aim to produce sequence fragments in excess of 15-20 kilobases, and have proved advantageous in the identification of structural variants and easing genome assembly. However, long read sequencing remains relatively expensive and error prone, and failed sequencing runs represent a significant problem for genomics core facilities. To quantitatively assess the underlying mechanics of sequencing failure, it is essential to have highly reproducible and controllable reference data sets to which sequencing results can be compared. Here, we present SiLiCO, the first in silico simulation tool to generate standardized sequencing results from both of the leading long read sequencing platforms. Availability: SiLiCO is an open source package written in Python. It is freely available at
It is well‐documented that feedforward cardiovascular responses occur at the onset of exercise, but it is unclear if such responses are associated with other types of movements. In this study, we tested the hypothesis that feedforward cardiovascular responses occur when a passive (imposed) 60° head‐up tilt is anticipated, such that changes in heart rate and carotid artery blood flow (CBF) commence prior to the onset of the rotation. A light cue preceded head‐up tilts by 10 sec, and heart rate and CBF were determined for 5‐sec time periods prior to and during tilts. Even after these stimuli were provided for thousands of trials spanning several months, no systematic changes in CBF and heart rate occurred prior to tilts, and variability in cardiovascular adjustments during tilt remained substantial over time. We also hypothesized that substitution of 20° for 60° tilts in a subset of trials would result in exaggerated cardiovascular responses (as animals expected 60° tilts), which were not observed. These data suggest that cardiovascular adjustments during passive changes in posture are mainly elicited by feedback mechanisms, and that anticipation of passive head‐up tilts does not diminish the likelihood that a decrease in carotid blood flow will occur during the movements.
As spatially-resolved multiplex profiling of RNA and proteins becomes more prominent, it is increasingly important to understand the statistical power available to test specific hypotheses when designing and interpreting such experiments. Ideally, it would be possible to create an oracle that predicts sampling requirements for generalized spatial experiments. However, the unknown number of relevant spatial features and the complexity of spatial data analysis makes this challenging. Here, we enumerate multiple parameters of interest that should be considered in the design of a properly powered spatial omics. We introduce a method for tunable in silico tissue generation, and use it with spatial profiling datasets to construct an exploratory computational framework for single cell spatial power analysis. Finally, we demonstrate that our framework can be applied across diverse spatial data modalities and tissues of interest.
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