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We present a novel fast string matching technique for special DNA pattern forms and compare performance of recent CPU architectures on the matching problem. In particular, we consider consensus P53 DNA-binding consensus sequence, which has an important contribution for cancer treatment. Based on biological findings, consensus P53 pattern may emerge in various sequence forms and its length is not deterministic. Therefore, classic string matching algorithms are not able to solve the problem. For efficient solution, we consider bitwise string matching algorithms with classes and present a novel search technique which is based on 64-bit packed variables. In order to prevent obstacles based on variable length of the pattern, we search right and left side indexes of P53 and reduce search space. For experimental analysis, we make use of mus musculus DNA sequences with approximately 2.3 billion nucleotides. We compare algorithm performance on three processors with distinct CPU architecture. Test results show that our search technique introduces at least 20% efficiency during P53 pattern search in each architecture platform. Due to its structure, the algorithm also introduces an efficient solution to similar string matching with class problems. Bu çalışmada özel DNA örüntüleri için yeni ve hızlı bir sekans eşleştirme tekniği sunulmakta ve yakın geçmişte üretilen CPU mimarileri üzerinde deneysel karşılaştırmalar yapılmaktadır. Bu çalışmada, bilhassa kanser tedavisinde, önemli bir yere sahip olan P53 DNA-bağlayan konsensüs sekansı göz önüne alınmıştır. Biyolojik kazanımlara göre P53 örüntüsü farklı sekans formlarında karşımıza çıkabilmekte ve sekans uzunluğu değişebilmektedir. Bu nedenle P53 sekansının klasik sekans eşleştirme algoritmaları ile çözümü mümkün olamamaktadır. Bu çalışmada verimli çözüm yöntemi sunmak amacıyla, sınıf özellikli bit-tabanlı sekans eşleştirme algoritması göz önüne alınmıştır. Hedef doğrultusunda, 64-bit paketlenmiş değişken kullanarak yeni bir arama ve eşleştirme algoritması önerilmiştir. Örüntü sekansının değişken uzunluk gösterebilmesi nedeniyle karşılaşılması muhtemel engelleri aşmak için ise veri tabanında P53 sekansının özel kısımlarına dair aramalar yapılmıştır. Deneysel analiz için yaklaşık 2.3 milyar nükleotidden oluşan mus musculus DNA sekansı seçilmiştir. Karşılaştırılan algoritmalar üç farklı bilgisayar mimarisinde test edilmiştir. Deneysel sonuçlar, geliştirdiğimiz algoritmanın P53 sekans arama konusunda tüm mimari platformlarında en iyi verimliliği sağladığını göstermektedir. Yapısı gereği bu algoritma, benzer sekans eşleştirme problemlerinin çözümünde de verimli olanaklar sunmaktadır.

Background Multiple sequence alignments are a fundamental tool for the comparative analysis of proteins and nucleic acids. However, large data sets are no longer manageable for visualization and investigation using the traditional stacked sequence alignment representation. Results We introduce ProfileGrids that represent a multiple sequence alignment as a matrix color-coded according to the residue frequency occurring at each column position. JProfileGrid is a Java application for computing and analyzing ProfileGrids. A dynamic interaction with the alignment information is achieved by changing the ProfileGrid color scheme, by extracting sequence subsets at selected residues of interest, and by relating alignment information to residue physical properties. Conserved family motifs can be identified by the overlay of similarity plot calculations on a ProfileGrid. Figures suitable for publication can be generated from the saved spreadsheet output of the colored matrices as well as by the export of conservation information for use in the PyMOL molecular visualization program. We demonstrate the utility of ProfileGrids on 300 bacterial homologs of the RecA family – a universally conserved protein involved in DNA recombination and repair. Careful attention was paid to curating the collected RecA sequences since ProfileGrids allow the easy identification of rare residues in an alignment. We relate the RecA alignment sequence conservation to the following three topics: the recently identified DNA binding residues, the unexplored MAW motif, and a unique Bacillus subtilis RecA homolog sequence feature. Conclusion ProfileGrids allow large protein families to be visualized more effectively than the traditional stacked sequence alignment form. This new graphical representation facilitates the determination of the sequence conservation at residue positions of interest, enables the examination of structural patterns by using residue physical properties, and permits the display of rare sequence features within the context of an entire alignment. JProfileGrid is free for non-commercial use and is available from http://www.profilegrid.org. Furthermore, we present a curated RecA protein collection that is more diverse than previous data sets; and, therefore, this RecA ProfileGrid is a rich source of information for nanoanatomy analysis.

The splice-site sequences of U2-type introns are highly degenerate, so many different sequences can function as U2-type splice sites. Using our new profiles based on hydrophobicity properties we pointed out specific properties for regions surrounding splice sites. We built a set T of flanking regions of genes with 1-3 introns from 21st and 22nd chromosomes extracted from GenBank to define positions having conserved properties, namely hydrophobicity, that are potentially essential for recognition by spliceosome. GT–AG introns exist in U2 and U12-types. Therefore, intron type cannot be simply determined by the dinucleotide termini. We attempted to distinguish U2 and U12-types introns with help of hydrophobicity profiles on sets of spice sites for U2 or U12-type introns extracted from SpliceRack database. The positions given by our method, which may be important for recognition by spliceosome, were compared to the nucleotide consensus provided by a classical method, Pictogram. We showed that there is a similarity of hydrophobicity profiles inside intron types. On one hand, GT–AG and GC–AG introns belonging to U2-type have resembling hydrophobicity profiles as well as AT–AC and GT–AG introns belonging to U12-type. On the other hand, hydrophobicity profiles of U2 and U12-types GT–AG introns are completely different. We suggest that hydrophobicity profiles facilitate definition of intron type, distinguishing U2 and U12 intron types and can be used to build programs to search splice site and to evaluate their strength. Therefore, our study proves that hydrophobicity profiles are informative method providing insights into mechanisms of splice sites recognition.