BackgroundMetagenomics is limited in its ability to link distinct microbial populations to genetic potential due to a current lack of representative isolate genome sequences. Reference-independent approaches, which exploit for example inherent genomic signatures for the clustering of metagenomic fragments (binning), offer the prospect to resolve and reconstruct population-level genomic complements without the need for prior knowledge.ResultsWe present VizBin, a Java™-based application which offers efficient and intuitive reference-independent visualization of metagenomic datasets from single samples for subsequent human-in-the-loop inspection and binning. The method is based on nonlinear dimension reduction of genomic signatures and exploits the superior pattern recognition capabilities of the human eye-brain system for cluster identification and delineation. We demonstrate the general applicability of VizBin for the analysis of metagenomic sequence data by presenting results from two cellulolytic microbial communities and one human-borne microbial consortium. The superior performance of our application compared to other analogous metagenomic visualization and binning methods is also presented.ConclusionsVizBin can be applied de novo for the visualization and subsequent binning of metagenomic datasets from single samples, and it can be used for the post hoc inspection and refinement of automatically generated bins. Due to its computational efficiency, it can be run on common desktop machines and enables the analysis of complex metagenomic datasets in a matter of minutes. The software implementation is available at https://claczny.github.io/VizBin under the BSD License (four-clause) and runs under Microsoft Windows™, Apple Mac OS X™ (10.7 to 10.10), and Linux.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-014-0066-1) contains supplementary material, which is available to authorized users.

Online judges are systems designed for the reliable evaluation of algorithm source code submitted by users, which is next compiled and tested in a homogeneous environment. Online judges are becoming popular in various applications. Thus, we would like to review the state of the art for these systems. We classify them according to their principal objectives into systems supporting organization of competitive programming contests, enhancing education and recruitment processes, facilitating the solving of data mining challenges, online compilers and development platforms integrated as components of other custom systems. Moreover, we introduce a formal definition of an online judge system and summarize the common evaluation methodology supported by such systems. Finally, we briefly discuss an Optil.io platform as an example of an online judge system, which has been proposed for the solving of complex optimization problems. We also analyze the competition results conducted using this platform. The competition proved that online judge systems, strengthened by crowdsourcing concepts, can be successfully applied to accurately and efficiently solve complex industrial- and science-driven challenges.Comment: Authors pre-print of the article accepted for publication in ACM Computing Surveys (accepted on 19-Sep-2017

UPDATED-January 2, 2016. The main objective of the presented research is to design a platform for continuous evaluation of optimization algorithms using crowdsourcing technique. The resulting platform, called Optil.io, runs in a cloud using platform as a service model and allows researchers from all over the world to collaboratively solve computational problems. This is the approach that has been already proved to be very successful for data mining problems by web services such as Kaggle. During our project we adapted this concept for solving computational problems that require implementation of software. To achieve this we designed the on-line judge system that receives algorithmic solutions in a form of source code from the crowd of programmers, compiles it, executes in a homogeneous run-time environment and objectively evaluates using the set of test cases. It was verified during internal experiments at the Poznan University of Technology and it is now ready to be presented to wider audience.