Tandem mass spectrometry is a widely used method for protein and peptide sequences identification. Since the mass spectra contain up to 80% of noise and many other inaccuracies, there still exists a need for more accurate algorithms for... more
Tandem mass spectrometry is a widely used method for protein and peptide sequences identification. Since the mass spectra contain up to 80% of noise and many other inaccuracies, there still exists a need for more accurate algorithms for mass spectra interpretation.
The sizes of protein databases grow rapidly and the methods for indexing these databases in order to interpret mass spectra become very popular. The parametrised Hausdorff distance, suitable for non-metric search, is presented in this paper. It models the similarity among tandem mass spectra very well and it is able to match the spectrum to correct peptide sequence in many cases without any post-processing scoring system.
With the emerging applications dealing with complex multimedia retrieval, such as the multimedia exploration, appropriate indexing structures need to be designed. A formalism for compact metric region description can significantly... more
With the emerging applications dealing with complex multimedia retrieval, such as the multimedia exploration, appropriate indexing structures need to be designed. A formalism for compact metric region description can significantly simplify the design of algorithms for such indexes, thus more complex and efficient metric indexes can be developed. In this paper, we introduce the cut-regions that are suitable for compact metric region description and we discuss their basic operations. To demonstrate the power of cut-regions, we redefine the PM-Tree using the cut-region formalism and, moreover, we use the formalism to describe our new improvements of the PM-Tree construction techniques. We have experimentally evaluated that the improved construction techniques lead to query performance originally obtained just using expensive construction techniques. Also in comparison with other metric and spatial access methods, the revisited PM-Tree proved its benefits.
In biological applications, the tandem mass spectrometry is a widely used method for determining protein and peptide sequences from an "in vitro" sample. The sequences are not determined directly, but they must be interpreted from the... more
In biological applications, the tandem mass spectrometry is a widely used method for determining protein and peptide sequences from an "in vitro" sample. The sequences are not determined directly, but they must be interpreted from the mass spectra, which is the output of the mass spectrometer. This work is focused on a similarity-search approach to mass spectra interpretation, where the parametrized Hausdorff distance (dHP) is used as the similarity. In order to provide an efficient similarity search under dHP, the metric access methods and the TriGen algorithm (controlling the metricity of dHP) are employed. We show that similarity search using dHP exhibits better correctness of peptide mass spectra interpretation than the cosine similarity commonly mentioned in mass spectrometry literature.
Moreover, the search model using the dHP distance could be extended to support chemical modifications in the query mass spectra, which is typically a problem when the cosine similarity is used. Our approach can be utilized as a coarse filter by any other database approach for mass spectra interpretation.
This work investigates the performance of several spatial access methods with respect to the distribution of the indexed spatial objects. Although having gathered storage and insertion costs as well this work focuses on some issues... more
This work investigates the performance of several spatial access methods with respect to the distribution of the indexed spatial objects. Although having gathered storage and insertion costs as well this work focuses on some issues regarding query costs. The performance results have showed that the R+-tree was the best spatial index structure for the point queries and the enclosure range
Tandem mass spectrometry is a well-known technique for identification of protein sequences from an "in vitro" sample. To identify the sequences from spectra captured by a spectrometer, the similarity search in a database of hypothetical... more
Tandem mass spectrometry is a well-known technique for identification of protein sequences from an "in vitro" sample. To identify the sequences from spectra captured by a spectrometer, the similarity search in a database of hypothetical mass spectra is often used. For this purpose, a database of known protein sequences is utilized to generate the hypothetical spectra. Since the number of sequences in the databases grows rapidly over the time, several approaches have been proposed to index the databases of mass spectra. In this paper, we improve an approach based on the non-metric similarity search where the M-tree and the TriGen algorithm are employed for fast and approximative search. We show that preprocessing of mass spectra by clustering speeds up the identification of sequences more than 100x with respect to the sequential scan of the entire database. Moreover, when the protein candidates are refined by sequential scan in the postprocessing step, the whole approach exhibits precision similar to that of sequential scan over the entire database (over 90%).
