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Bioinformatics & Cheminformatics

Simtandem (Protein sequence identification)

SimTandem is a tool for protein or peptide sequences identification from tandem mass spectra. The identification is based on the similarity search in databases of already known or predicted protein sequences. Since the size of sequence databases grows rapidly, metric access methods are employed for database indexes. SimTandem implements a previously proposed method, where the M-tree and the TriGen algorithm were used for fast and approximative (i.e., non-metric) search. The recently introduced parameterized Hausdorff distance, which is suitable as a coarse filter for metric indexes, is utilized. SimTandem supports the search of mass spectra with posttranslational modifications, which are quite common problem when mass spectra are interpreted. SimTandem has been implemented as both the on-line web tool and the stand-alone application. SimTandem is freely available at or

Jiri Novak, Tomas Skopal


Multimedia exploration framework (Creation of efficient multimedia exploration applications)

Multimedia exploration framework is an extensible solution for creation of multimedia exploration applications.

It uses a modular architecture and already provides several implementantions for every component. Besides managing the software architecture and data flow, the framework also takes care of data source management, data retrieval, feature extraction, distance computation, metric indexing, query execution, data visiualization and GUI creation.

Contact the developers directly if you are interested in building an application using our framework.

SIR (Smart image retrieval)

When determining visual similarity of two images, it is evaluated on feature representations which consist of some content-based image properties. The conventional feature representations aggregate and store these properties in global feature histograms (e.g.,
MPEG-7 visual descriptors).

Recent feature representations, however, adaptively aggregate local image features in more flexible feature signatures, which can be
compared by adaptive similarity measures. The SIR engine developed at SIRET research group combines traditional MPEG-7 visual descriptors with feature signatures, leading to improved similarity search in image collections.

Currently, the SIR engine operates in a demo mode as a standalone image search engine. In order to manage large image collections in real time, the engine employs original database indexing technology. The SIR engine also includes meta-search functionality that allows to augment/rerank/explore results provided by other image search engines, such as Google Images and others. The actual version of the online re-ranking and exploration tool employes the particle physics model, that both distributes images on the screen and automatically creates visually similar clusters (as a side effect). To refer this tool, you can refer our publications - Image Exploration using Online Feature Extraction and Reranking (ICMR, 2012) and SIR: The Smart Image Retrieval Engine (SISAP, 2012).

Jakub Lokoc, Tomas Skopal