Annotated database of fluorescence microscope images depicting subcellular location proteins with two interfaces: a text and image content search interface, and a graphical interface for exploring location patterns grouped into Subcellular Location Trees for the following data sets:
* 3D images of 90 clones of randomly CD-tagged 3T3.
* 2D images of 175 clones of randomly CD-tagged 3T3, for p-value thresholds used to select features of 0, 0.1, and 0.2.
* 3D images of wildtype and 11 mutants of GFP-tagged UCE in HeLa cells.
PSLID collects and structures 2-D through 5-D fluorescence microscope images, annotations, and derived features in a relational schema. It is designed so that interpretations as well as annotations can be queried. The annotations in PSLID, composed of 44 linked tables with publicly available descriptions, provide a thorough description of sample preparation and fluorescence microscope imaging.
Image interpretation is achieved using Subcellular Location Features that have been shown to be capable of recognizing all major subcellular structures and of resolving patterns that cannot be distinguished by eye.
The fundamental unit of PSLID is an image set, which is simply a logical grouping of images. Image sets can be defined at the time of image loading, or they can be defined by searching for images that meet specified criteria (e.g., all images of actin or all images that are similar to a query image). They can also be created by analysis functions such as cluster analysis (e.g., the images in each cluster found by cluster analysis can be put into distinct sets).
Analysis capabilities that are incorporated in PSLID include:
* Searching for images by context (annotations) or content
* Ranking images by typicality within a set: e.g., to choose an image for presentation or publication
* Ranking images by similarity to one or more query images: searching by image content or relevance feedback
* Comparing two sets of images (hypothesis testing): e.g., to determine whether a drug alters the distribution of a tagged protein
* Training a classifier to recognize subcellular patterns
* Using a trained classifier to assign images to pattern classes: e.g., assigning images to positive or negative
* Clustering images by their subcellular patterns: e.g., finding subcellular location families within a large set of images
Submission to PSLID of other image collections documenting the subcellular location of proteins to facilitate one-stop searching for information on subcellular patterns is encouraged.
Resource Type: Resource
Version: Latest Version
protein, structure, subcellular, organelle, image, fluorescence microscope, annotation, classify, rank, cluster, subcellular localization, 3d spatial image, 2d spatial image, micrograph, content-based retrieval, green fluorescent protein
Additional Resource Types
database, data set, data repository, image analysis service
PSLID - Protein Subcellular Location Image Database, Protein Subcellular Location Image Database
Last checked up;
Created 4 years ago by Anonymous