Dataset Information

Similarity/Dissimilarity analysis of protein sequences based on a new spectrum-like graphical representation.

ABSTRACT: Sequence comparison is one of the foundations in bioinformatics, which can be used to study evolutionary relations among the sequences. In this study, a 2D spectrum-like graphical representation of protein sequences is presented based on the hydrophobicity scale of amino acids. The frequencies of amplitudes of 4-subsequences are adopted to characterize a spectrum-like graph, and a 17D vector is used as the descriptor of protein sequence. The ?(2) value of compatibility test is performed. New similarity analysis approach is illustrated on the all protein sequences, which are encoded by the mitochondrion genome of 20 different species. Finally, comparison with the ClustalW method shows the utility of our method.

SUBMITTER: Yao Y

PROVIDER: S-EPMC4068907 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Similarity/Dissimilarity analysis of protein sequences based on a new spectrum-like graphical representation.

Yao Yuhua Y Yan Shoujiang S Xu Huimin H Han Jianning J Nan Xuying X He Ping-An PA Dai Qi Q

Evolutionary bioinformatics online 20140612

Sequence comparison is one of the foundations in bioinformatics, which can be used to study evolutionary relations among the sequences. In this study, a 2D spectrum-like graphical representation of protein sequences is presented based on the hydrophobicity scale of amino acids. The frequencies of amplitudes of 4-subsequences are adopted to characterize a spectrum-like graph, and a 17D vector is used as the descriptor of protein sequence. The χ(2) value of compatibility test is performed. New sim ...[more]

PMID: 25002811

Similar Datasets

Project description:BACKGROUND: The rapid burgeoning of available protein data makes the use of clustering within families of proteins increasingly important. The challenge is to identify subfamilies of evolutionarily related sequences. This identification reveals phylogenetic relationships, which provide prior knowledge to help researchers understand biological phenomena. A good evolutionary model is essential to achieve a clustering that reflects the biological reality, and an accurate estimate of protein sequence similarity is crucial to the building of such a model. Most existing algorithms estimate this similarity using techniques that are not necessarily biologically plausible, especially for hard-to-align sequences such as proteins with different domain structures, which cause many difficulties for the alignment-dependent algorithms. In this paper, we propose a novel similarity measure based on matching amino acid subsequences. This measure, named SMS for Substitution Matching Similarity, is especially designed for application to non-aligned protein sequences. It allows us to develop a new alignment-free algorithm, named CLUSS, for clustering protein families. To the best of our knowledge, this is the first alignment-free algorithm for clustering protein sequences. Unlike other clustering algorithms, CLUSS is effective on both alignable and non-alignable protein families. In the rest of the paper, we use the term "phylogenetic" in the sense of "relatedness of biological functions". RESULTS: To show the effectiveness of CLUSS, we performed an extensive clustering on COG database. To demonstrate its ability to deal with hard-to-align sequences, we tested it on the GH2 family. In addition, we carried out experimental comparisons of CLUSS with a variety of mainstream algorithms. These comparisons were made on hard-to-align and easy-to-align protein sequences. The results of these experiments show the superiority of CLUSS in yielding clusters of proteins with similar functional activity. CONCLUSION: We have developed an effective method and tool for clustering protein sequences to meet the needs of biologists in terms of phylogenetic analysis and prediction of biological functions. Compared to existing clustering methods, CLUSS more accurately highlights the functional characteristics of the clustered families. It provides biologists with a new and plausible instrument for the analysis of protein sequences, especially those that cause problems for the alignment-dependent algorithms.

Project description:This paper introduces "Kabirian-based optinalysis (KBO)," a pioneering framework that addresses the longstanding challenges in estimating symmetry/asymmetry, similarity/dissimilarity, and identity/unidentity within mathematical structures and biological sequences. The existing methods often lack a strong theoretical foundation, leading to inconsistencies and limitations. Kabirian-based optinalysis draws inspiration from isomorphism and automorphism, providing a theoretically grounded framework that unifies estimation methodologies. It introduces the concept of optiscale, autoreflective pairing, isoreflective pairing, and others ensuring invariance and robustness under various mathematical transformations and establishing functional bijectivity for isomorphic or automorphic structures. This not only overcomes previous limitations but also offers precise and interpretable estimations. Additionally, the framework introduces "geometrical pairwise analysis" to improve sensitivity to position-specific and character-specific variations in biological sequences. This novel approach enhances the accuracy of sequence similarity assessments, surpassing the constraints of conventional methods. The novelty of this work extends beyond mathematics and biology, impacting diverse fields such as computer science, data analysis, pattern recognition, and evolutionary biology. Kabirian-based optinalysis presents a holistic and theoretically grounded solution that has the potential to revolutionize the analysis of complex structures and sequences, opening new horizons for interdisciplinary research.•Inspired by automorphism and isomorphism, Kabirian-based optinalysis introduces a new paradigm-shifting and unified approach to estimations in mathematical structures and biological sequences with a solid conceptual and theoretical foundation.•The GPA method enhances pairwise sequence similarity estimation by being sensitive to position-specific and character-specific variations and providing a comprehensive characterization of these features.

Dataset Information

Similarity/Dissimilarity analysis of protein sequences based on a new spectrum-like graphical representation.

Publications

Similarity/Dissimilarity analysis of protein sequences based on a new spectrum-like graphical representation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets