RESUMO
Live cell imaging has been widely used to generate data for quantitative understanding of cellular dynamics. Various applications have been developed to perform automated imaging data analysis, which often requires tedious manual correction. It remains a challenge to develop an efficient curation method that can analyze massive imaging datasets with high accuracy. Here, we present eDetect, a fast error detection and correction tool that provides a powerful and convenient solution for the curation of live cell imaging analysis results. In eDetect, we propose a gating strategy to distinguish correct and incorrect image analysis results by visualizing image features based on principal component analysis. We demonstrate that this approach can substantially accelerate the data correction process and improve the accuracy of imaging data analysis. eDetect is well documented and designed to be user friendly for non-expert users. It is freely available at https://sites.google.com/view/edetect/ and https://github.com/Zi-Lab/eDetect.
RESUMO
Cellular senescence is an irreversible cell cycle arrest program in response to various exogenous and endogenous stimuli like telomere dysfunction and DNA damage. It has been widely accepted as an anti-tumor program and is also found closely related to embryo development, tissue repair, organismal aging and age-related degenerative diseases. In the past decades, numerous efforts have been made to uncover the gene regulatory mechanisms of cellular senescence. There is a strong demand to integrate these data from various resources into one open platform. To facilitate researchers on cellular senescence, we have developed Human Cellular Senescence Gene Database (HCSGD) by integrating multiple online published data sources into a comprehensive senescence gene annotation platform (http://bioinfo.au.tsinghua.edu.cn/member/xwwang/HCSGD). Potential Human Cellular Senescence Genes (HCSGS) were collected by combining information from published literatures, gene expression profiling data and Protein-Protein Interaction networks. Additionally, genes are annotated with gene ontology annotation and microRNA/drug/compound target information. HCSGD provides a valuable resource to visualize cellular senescence gene networks, browse annotated functional information, and retrieve senescence-associated genes with a user-friendly web interface.