The constantly developing high content and high throughput screening (HCS & HTS) microscopy and next generation sequencing technologies routinely produce experiment datasets in the terabyte (TB) or petabyte (PB) range resulting in millions of data files that can vary from simply numbers to signals and images. If the collaborators working on the same project are spread over large geographical distances, data sharing, interactive visualization and collaborative annotation techniques become important determinants of the success of a research project. On the other hand, there are hundreds of bioinformatic and cheminformatic databases, billions of documents in available literature, and many image based biological repositories, which need to be referred simultaneously to make sense out of the acquired data. To draw the conclusions from such increasingly complex and large scale data sources, the scientific community must be provided with simple to use methods to retrieve, analyze, visualize, annotate, and crosslink these data sources on a common platform in an efficient manner. However, on the other hand, these modern biological experiments and the subsequent analyses are completed with use of an array of different software suites and automated tools. A constant feedback from the experimenter is needed to change experimental paradigms for the follow-up experiments. Such a software platform to address the HCS data in these aspects does not exist yet, to the best of our knowledge. We have developed a simple to use software package called “AskMe” for users to publish their large scale biological experiment data on to the web by use of data mining and visualization concepts. With use of AskMe, scientists can share these HCS datasets easily with their collaborators or made publicly accessible to the whole scientific community. From the initial stages of experiments, AskMe can ease the experimental analysis process by mining data and providing useful visualizations. Moreover, integration and crosslinks to other databases also allow easy evaluation of data generated. By these principles, we bring the tools to the data and make the data access transparent to the users without any capacity tradeoff.

SUMMARY: Modern biological experiments create vast amounts of data which are geographically distributed. These datasets consist of petabytes of raw data and billions of documents. Yet to the best of our knowledge, a search engine technology that searches and cross-links all different data types in life sciences does not exist. We have developed a prototype distributed scientific search engine technology, 'Sciencenet', which facilitates rapid searching over this large data space. By 'bringing the search engine to the data', we do not require server farms. This platform also allows users to contribute to the search index and publish their large-scale data to support e-Science. Furthermore, a community-driven method guarantees that only scientific content is crawled and presented. Our peer-to-peer approach is sufficiently scalable for the science web without performance or capacity tradeoff. AVAILABILITY AND IMPLEMENTATION: The free to use search portal web page and the downloadable client are accessible at: http://sciencenet.kit.edu. The web portal for index administration is implemented in ASP.NET, the 'AskMe' experiment publisher is written in Python 2.7, and the backend 'YaCy' search engine is based on Java 1.6.

We investigate different approaches for efficient feature space reduction and compare different methods for cell classification. The application context is the development of automatic methods for analysing fluorescence microscopy images with the goal to identify those genes that are involved in the mitosis of human cells (cell division). We distinguish four cell classes comprising interphase cells, mitotic cells, apoptotic cells, and cells with clustered nuclei. Feature space reduction was performed using the Principal Component Analysis and Independent Component Analysis methods. Six classification methods were examined including unsupervised clustering algorithms such as K-means, Hard Competitive Learning, and Neural Gas as well as Hierarchical Clustering, Support Vector Machines, and Random Forests classifiers. Detailed results on the cell image classification accuracy and computational efficiency achieved using different feature sets and different classification methods are reported.

SUMMARY: We have developed a Web-based tool named 'Harvester' that bulk-collects bioinformatic data on human proteins from various databases and prediction servers. The information on every single protein is assembled on a single HTML page as a combination of database screen-shots and plain text. A full text meta search engine, similar to Google trade mark, allows screening of the whole genome proteome for current protein functions and predictions in a few seconds. With Harvester it is now possible to compare and check the quality of different database entries and prediction algorithms on a single page. A feedback forum allows users to comment on Harvester and to report database inconsistencies. AVAILABILITY: The service is freely available to the academic community at http://harvester.embl.de.