Project description:A simple HEK293 lysate, with two files containing a putative mycoplasma contamination, and two negative control samples, taken from Geiger etc al. (Mol Cell Proteomics. 2012 Mar;11(3):M111.014050. doi: 10.1074/mcp.M111.014050. Epub 2012 Jan 25.) [PXD002395].
Project description:This SuperSeries is composed of the SubSeries listed below. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Refer to individual Series
Project description:Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes.
Project description:A simple HEK293 lysate, which can be used to benchmark the performance of both the LC system and the mass spectrometer. The four files uploaded here were aqcuired on different timepoints and show distinct LC column differences.
