SynQuant An Automatic Tool to Quantify Synapses from Microscopy Images, bioRxiv, 2019-02-02

AbstractMotivationSynapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness for different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses.ResultsWe present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real data sets with ground truth annotation or manual labeling, SynQuant was demonstrated to outperform peer specialized synapse detection tools as well as generic spot detection methods, including 4 unsupervised and 11 variants of 3 supervised methods.AvailabilityJava source code, Fiji plug-in, and test data available at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsgithub.comyu-lab-vtSynQuant>httpsgithub.comyu-lab-vtSynQuant<jatsext-link>.Contactyug@vt.edu

biorxiv neuroscience 0-100-users 2019

Distinct characteristics of genes associated with phenome-wide variation in maize (Zea mays), bioRxiv, 2019-01-30

ABSTRACTNaturally occurring functional genetic variation is often employed to identify genetic loci that regulate specific traits. Existing approaches to link functional genetic variation to quantitative phenotypic outcomes typically evaluate one or several traits at a time. Advances in high throughput phenotyping now enable datasets which include information on dozens or hundreds of traits scored across multiple environments. Here, we develop an approach to use data from many phenotypic traits simultaneously to identify causal genetic loci. Using data for 260 traits scored across a maize diversity panel, we demonstrate that a distinct set of genes are identified relative to conventional genome wide association. The genes identified using this many-trait approach are more likely to be independently validated than the genes identified by conventional analysis of the same dataset. Genes identified by the new many-trait approach share a number of molecular, population genetic, and evolutionary features with a gold standard set of genes characterized through forward genetics. These features, as well as substantially stronger functional enrichment and purification, separate them from both genes identified by conventional genome wide association and from the overall population of annotated gene models. These results are consistent with a large subset of annotated gene models in maize playing little or no role in determining organismal phenotypes.

biorxiv bioinformatics 0-100-users 2019

 

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