New, more effective software tools are needed for the analysis and organization of the continually growing biological databases. An extension of the Self-Organizing Map (SOM) is used in this work for the clustering of all the 77,977 protein sequences of the SWISS-PROT database, release 37. In this method, unlike in some previous ones, the data sequences are not converted into histogram vectors ...

Within this paper we are proposing and testing a new strategy for detection and measurement of similarity between sequences of proteins. Our approach has its roots in computational linguistics and the related techniques for quantifying and comparing content in strings of characters. The pairwise comparison of proteins relies on the content regularities expected to uniquely characterize each seq...

For comprehensive analysis of genes expressed in a model legume, Lotus japonicus, a total of 22,983 5' end expressed sequence tags (ESTs) were accumulated from normalized and size-selected cDNA libraries constructed from young (2 weeks old) plants. The EST sequences were clustered into 7137 non-redundant groups. Similarity search against public non-redundant protein database indicated that 3302...

Kernel-based approaches for sequence classification have been successfully applied to a variety of domains, including the text categorization, image classification, speech analysis, biological sequence analysis, time series and music classification, where they show some of the most accurate results. Typical kernel functions for sequences in these domains (e.g., bag-of-words, mismatch, or subseq...

Kernel-based approaches for sequence classification have been successfully applied to a variety of domains, including the text categorization, image classification, speech analysis, biological sequence analysis, time series and music classification, where they show some of the most accurate results. Typical kernel functions for sequences in these domains (e.g., bag-of-words, mismatch, or subseq...

Alignment-free sequence analysis approaches provide important alternatives over multiple sequence alignment (MSA) in biological sequence analysis because alignment-free approaches have low computation complexity and are not dependent on high level of sequence identity. However, most of the existing alignment-free methods do not employ true full information content of sequences and thus can not ...

Title of dissertation: SEARCHING, CLUSTERING AND EVALUATING BIOLOGICAL SEQUENCES Mohammadreza Ghodsi, Doctor of Philosophy, 2012 Dissertation directed by: Professor Mihai Pop Department of Computer Science The latest generation of biological sequencing technologies have made it possible to generate sequence data faster and cheaper than ever before. The growth of sequence data has been exponenti...

The search for similarity between two biological sequences lies at the core of many applications in bioinformatics. This paper aims to highlight a few of the principles that should be kept in mind when evaluating the statistical significance of alignments between sequences. The extreme value distribution is first introduced, which in most cases describes the distribution of alignment scores bet...

Genes evolve by point mutations, but also by shuffling, fusion, and fission of genetic fragments. Therefore, similarity between two sequences can be due to common ancestry producing homology, and/or partial sharing of component fragments. Disentangling these processes is especially challenging in large molecular data sets, because of computational time. In this article, we present CompositeSear...

Bioinformatics emerged as a challenging new area of research and brought forth numerous computational problems. Here computers are used to gather, store, analyze and merge biological data. In this paper, the problem of clustering interval-scaled data and sequence data is analyzed in a new approach using Hierarchical Sequence Clustering. In Sequence clustering, it is necessary to find the simila...