Creating a new Ontology: a Modular Approach

Creating a new Ontology: a Modular ApproachComment: in Adrian Paschke, Albert Burger, Andrea Splendiani, M. Scott Marshall, Paolo Romano: Proceedings of the 3rd International Workshop on Semantic Web Applications and Tools for the Life Sciences, Berlin,Germany, December 8-10, 201

Construction of correlation networks with explicit time-slices using time-lagged, variable interval standard and partial correlation coefficients

The construction of gene regulatory models from microarray time-series data has received much attention. Here we propose a method that extends standard correlation networks to incorporate explicit time-slices. The method is applied to a time-series dataset of a study on gene expression in the developmental phase of zebrafish. Results show that the method is able to distinguish real relations between genes from the data. These relations are explicitly placed in time, allowing for a better understanding of gene regulation. The method and data normalisation procedure have been implemented using the R statistical language and are available from http://zebrafish.liacs.nl/supplements.html

Specificity Enhancement in microRNA Target Prediction through Knowledge Discovery

Computer Systems, Imagery and Medi

Frequent Episode Mining to support Pattern Analysis in Developmental Biology

Computer Systems, Imagery and Medi

Segmentation of NKX2.5 Signal in Human Pluripotent Stem Cell-Derived Cardiomyocytes

Human pluripotent stem cell-derived Cardiomyocytes (hPSC-CMs) become increasingly popular in recent years for disease modeling and drug screening. NKX2.5 gene is a key transcription factor that regulates cardiomyocyte differentiation. A human embryonic stem cell (hESC) reporter line with NKX2.5 in GFP signal allows us to monitor the specificity and efficiency of human cardiac differentiation. We intend to develop an automatic analysis pipeline for the NKX2.5 signal. However, the NKX2.5 signal captured from fluorescence microscopy is highly heterogeneous. It is not possible to be properly segmented using traditional thresholding methods. Therefore, in this paper, one machine learning method: enhanced Fuzzy C-Means clustering (EnFCM) and two deep learning models: U-Net and DeepLabV3+, are evaluated on the segmentation performance. Parameters have been tuned for each method so as to reach to the optimal segmentation performance. The results show that EnFCM reaches the performance of 0.85. U-Net and DeepLabV3+ have a superior performance. Their performances are 0.86 and 0.89 respectively.</p

Lentivirus-mediated transgene delivery to the hippocampus reveals sub-field specific differences in expression

<p>Abstract</p> <p>Background</p> <p>In the adult hippocampus, the granule cell layer of the dentate gyrus is a heterogeneous structure formed by neurons of different ages, morphologies and electrophysiological properties. Retroviral vectors have been extensively used to transduce cells of the granule cell layer and study their inherent properties in an intact brain environment. In addition, lentivirus-based vectors have been used to deliver transgenes to replicative and non-replicative cells as well, such as post mitotic neurons of the CNS. However, only few studies have been dedicated to address the applicability of these widespread used vectors to hippocampal cells in vivo. Therefore, the aim of this study was to extensively characterize the cell types that are effectively transduced in vivo by VSVg-pseudotyped lentivirus-based vectors in the hippocampus dentate gyrus.</p> <p>Results</p> <p>In the present study we used Vesicular Stomatitis Virus G glycoprotein-pseudotyped lentivirual vectors to express EGFP from three different promoters in the mouse hippocampus. In contrast to lentiviral transduction of pyramidal cells in CA1, we identified sub-region specific differences in transgene expression in the granule cell layer of the dentate gyrus. Furthermore, we characterized the cell types transduced by these lentiviral vectors, showing that they target primarily neuronal progenitor cells and immature neurons present in the sub-granular zone and more immature layers of the granule cell layer.</p> <p>Conclusion</p> <p>Our observations suggest the existence of intrinsic differences in the permissiveness to lentiviral transduction among various hippocampal cell types. In particular, we show for the first time that mature neurons of the granule cell layer do not express lentivirus-delivered transgenes, despite successful expression in other hippocampal cell types. Therefore, amongst hippocampal granule cells, only adult-generated neurons are target for lentivirus-mediated transgene delivery. These properties make lentiviral vectors excellent systems for overexpression or knockdown of genes in neuronal progenitor cells, immature neurons and adult-generated neurons of the mouse hippocampus in vivo.</p

Fungal metabarcoding data integration framework for the MycoDiversity DataBase (MDDB)

Fungi have crucial roles in ecosystems, and are important associates for many organisms. They are adapted to a wide variety of habitats, however their global distribution and diversity remains poorly documented. The exponential growth of DNA barcode information retrieved from the environment is assisting considerably the traditional ways for unraveling fungal diversity and detection. The raw DNA data in association to environmental descriptors of metabarcoding studies are made available in public sequence read archives. While this is potentially a valuable source of information for the investigation of Fungi across diverse environmental conditions, the annotation used to describe environment is heterogenous. Moreover, a uniform processing pipeline still needs to be applied to the available raw DNA data. Hence, a comprehensive framework to analyses these data in a large context is still lacking. We introduce the MycoDiversity DataBase, a database which includes public fungal metabarcoding data of environmental samples for the study of biodiversity patterns of Fungi. The framework we propose will contribute to our understanding of fungal biodiversity and aims to become a valuable source for large-scale analyses of patterns in space and time, in addition to assisting evolutionary and ecological research on Fungi

The novel gene asb11:a regulator of the size of the neural progenitor compartment

From a differential display designed to isolate genes that are down-regulated upon differentiation of the central nervous system in Danio rerio embryos, we isolated d-asb11 (ankyrin repeat and suppressor of cytokine signaling box–containing protein 11). Knockdown of the d-Asb11 protein altered the expression of neural precursor genes sox2 and sox3 and resulted in an initial relative increase in proneural cell numbers. This was reflected by neurogenin1 expansion followed by premature neuronal differentiation, as demonstrated by HuC labeling and resulting in reduced size of the definitive neuronal compartment. Forced misexpression of d-asb11 was capable of ectopically inducing sox2 while it diminished or entirely abolished neurogenesis. Overexpression of d-Asb11 in both a pluripotent and a neural-committed progenitor cell line resulted in the stimulus-induced inhibition of terminal neuronal differentiation and enhanced proliferation. We conclude that d-Asb11 is a novel regulator of the neuronal progenitor compartment size by maintaining the neural precursors in the proliferating undifferentiated state possibly through the control of SoxB1 transcription factors