Extended many-item similarity indices for sets of nucleotide and protein sequences

Computational and Structural Biotechnology Journal

Anita Rácz¹  Ramón Alain Miranda-Quintana²  Dávid Bajusz³  Károly Héberger⁴ 

[1] Corresponding authors.;Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, FL 32611, USA;Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117 Budapest, Hungary;Plasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, 1117 Budapest, Hungary;

关键词: Multiple comparisons;    DNA sequences;    Protein sequences;    Diversity analysis;    Similarity indices;    Consistency;   

DOI  :  

来源: DOAJ

【 摘 要 】

Quantification of similarities between protein sequences or DNA/RNA strands is a (sub-)task that is ubiquitously present in bioinformatics workflows, and is usually accomplished by pairwise comparisons of sequences, utilizing simple (e.g. percent identity) or more intricate concepts (e.g. substitution scoring matrices). Complex tasks (such as clustering) rely on a large number of pairwise comparisons under the hood, instead of a direct quantification of set similarities. Based on our recently introduced framework that enables multiple comparisons of binary molecular fingerprints (i.e., direct calculation of the similarity of fingerprint sets), here we introduce novel symmetric similarity indices for analogous calculations on sets of character sequences with more than two (t) possible items (e.g. DNA/RNA sequences with t = 4, or protein sequences with t = 20). The features of these new indices are studied in detail with analysis of variance (ANOVA), and demonstrated with three case studies of protein/DNA sequences with varying degrees of similarity (or evolutionary proximity). The Python code for the extended many-item similarity indices is publicly available at: https://github.com/ramirandaq/tn_Comparisons.

【 授权许可】

Unknown