Improved metagenomic analysis with Kraken 2, bioRxiv, 2019-09-08
Although Kraken’s k-mer-based approach provides fast taxonomic classification of metagenomic sequence data, its large memory requirements can be limiting for some applications. Kraken 2 improves upon Kraken 1 by reducing memory usage by 85%, allowing greater amounts of reference genomic data to be used, while maintaining high accuracy and increasing speed five-fold. Kraken 2 also introduces a translated search mode, providing increased sensitivity in viral metagenomics analysis.
biorxiv bioinformatics 200-500-users 2019GeneWalk identifies relevant gene functions for a biological context using network representation learning, bioRxiv, 2019-09-05
AbstractThe primary bottleneck in high-throughput genomics experiments is identifying the most important genes and their relevant functions from a list of gene hits. Existing methods such as Gene Ontology (GO) enrichment analysis provide insight at the gene set level. For individual genes, GO annotations are static and biological context can only be added by manual literature searches. Here, we introduce GeneWalk (<jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpgithub.comchurchmanlabgenewalk>github.comchurchmanlabgenewalk<jatsext-link>), a method that identifies individual genes and their relevant functions under a particular experimental condition. After automatic assembly of an experiment-specific gene regulatory network, GeneWalk quantifies the similarity between vector representations of each gene and its GO annotations through representation learning, yielding annotation significance scores that reflect their functional relevance for the experimental context. We demonstrate the use of GeneWalk analysis of RNA-seq and nascent transcriptome (NET-seq) data from human cells and mouse brains, validating the methodology. By performing gene- and condition-specific functional analysis that converts a list of genes into data-driven hypotheses, GeneWalk accelerates the interpretation of high-throughput genetics experiments.
biorxiv bioinformatics 200-500-users 2019A novel weight lifting task for investigating effort and persistence in rats, bioRxiv, 2019-09-04
AbstractHere we present a novel effort-based task for laboratory rats the weight lifting task (WLT). Studies of effort expenditure in rodents have typically involved climbing barriers within T-mazes or operant lever pressing paradigms. These task designs have been successful for neuropharmacological and neurophysiological investigations, but both tasks involve simple action patterns prone to automatization. Furthermore, high climbing barriers present risk of injury to animals andor tethered recording equipment. In the WLT, a rat is placed in a large rectangular arena and tasked with pulling a rope 30 cm to trigger food delivery at a nearby spout; weights can be added to the rope in 45 g increments to increase the intensity of effort. As compared to lever pressing and barrier jumping, 30 cm of rope pulling is a multi-step action sequence requiring sustained effort. The actions are carried out on the single plane of the arena floor, making it safer for the animal and more suitable for tethered equipment and video tracking. A microcontroller and associated sensors enable precise timestamping of specific behaviors to synchronize with electrophysiological recordings. The rope and reward spout are spatially segregated to allow for spatial discrimination of the effort zone and the reward zone. We validated the task across five cohorts of rats (total n=35) and report consistent behavioral metrics. The WLT is well-suited for neuropharmacological andor in vivo neurophysiological investigations surrounding effortful behaviors, particularly when wanting to probe different aspects of effort expenditure (intensity vs. duration).
biorxiv animal-behavior-and-cognition 200-500-users 2019Genomics reveals the origins of ancient specimens, bioRxiv, 2019-09-04
Centuries of zoological studies amassed billions of specimens in collections worldwide. Genomics of these specimens promises to rejuvenate biodiversity research. The obstacles stem from DNA degradation with specimen age. Overcoming this challenge, we set out to resolve a series of long-standing controversies involving a group of butterflies. We deduced geographical origins of several ancient specimens of uncertain provenance that are at the heart of these debates. Here, genomics tackles one of the greatest problems in zoology countless old, poorly documented specimens that serve as irreplaceable embodiments of species concepts. The ability to figure out where they were collected will resolve many on-going disputes. More broadly, we show the utility of genomics applied to ancient museum specimens to delineate the boundaries of species and populations, and to hypothesize about genotypic determinants of phenotypic traits.
biorxiv zoology 200-500-users 2019A neural network model of flexible grasp movement generation, bioRxiv, 2019-08-25
AbstractOne of the main ways we interact with the world is using our hands. In macaques, the circuit formed by the anterior intraparietal area, the hand area of the ventral premotor cortex, and the primary motor cortex is necessary for transforming visual information into grasping movements. We hypothesized that a recurrent neural network mimicking the multi-area structure of the anatomical circuit and trained to transform visual features into the muscle fiber velocity required to grasp objects would recapitulate neural data in the macaque grasping circuit. While a number of network architectures produced the required kinematics, modular networks with visual input and activity that was encouraged to be biologically realistic best matched neural data and the inter-area differences present in the biological circuit. Network dynamics could be explained by simple rules that also allowed the correct prediction of kinematics and neural responses to novel objects, providing a potential mechanism for flexibly generating grasping movements.
biorxiv neuroscience 200-500-users 2019Deep Learning-Based Point-Scanning Super-Resolution Imaging, bioRxiv, 2019-08-22
Point scanning imaging systems (e.g. scanning electron or laser scanning confocal microscopes) are perhaps the most widely used tools for high resolution cellular and tissue imaging. Like all other imaging modalities, the resolution, speed, sample preservation, and signal-to-noise ratio (SNR) of point scanning systems are difficult to optimize simultaneously. In particular, point scanning systems are uniquely constrained by an inverse relationship between imaging speed and pixel resolution. Here we show these limitations can be mitigated via the use of deep learning-based super-sampling of undersampled images acquired on a point-scanning system, which we termed point-scanning super-resolution (PSSR) imaging. Oversampled, high SNR ground truth images acquired on scanning electron or Airyscan laser scanning confocal microscopes were crappified to generate semi-synthetic training data for PSSR models that were then used to restore real-world undersampled images. Remarkably, our EM PSSR model could restore undersampled images acquired with different optics, detectors, samples, or sample preparation methods in other labs. PSSR enabled previously unattainable 2 nm resolution images with our serial block face scanning electron microscope system. For fluorescence, we show that undersampled confocal images combined with a multiframe PSSR model trained on Airyscan timelapses facilitates Airyscan-equivalent spatial resolution and SNR with ~100x lower laser dose and 16x higher frame rates than corresponding high-resolution acquisitions. In conclusion, PSSR facilitates point-scanning image acquisition with otherwise unattainable resolution, speed, and sensitivity.
biorxiv bioinformatics 200-500-users 2019