Ranking and clustering of Drosophila olfactory receptors using mathematical morphology

Abstract

This article introduces an alignment-free clustering method in order to cluster all the 66 DORs sequentially diverse protein sequences. Two different methods are discussed: one is utilizing twenty standard amino acids (without grouping) and another one is using chemical grouping of amino acids (with grouping). Two grayscale images (representing two protein sequences by order pair frequency matrices) are compared to find the similarity index using morphology technique. We could achieve the correlation coefficients of 0.9734 and 0.9403 for without and with grouping methods respectively with the ClustalW result in the ND5 dataset, which are much better than some of the existing alignment-free methods. Based on the similarity index, the 66 DORs are clustered into three classes - Highest, Moderate and Lowest - which are seen to be best fitted for 66 DORs protein sequences. OR83b is the distinguished olfactory receptor expressed in divergent insect population which is substantiated through our investigation.

Cancer PRSweb: An Online Repository with Polygenic Risk Scores for Major Cancer Traits and Their Evaluation in Two Independent Biobanks

“…Nevertheless, PRSs have proven to be a valuable tool for risk stratification, especially if combined with non-genetic risk factors. [87][88][89] PRSs will likely become more powerful with growing sample sizes, better tools, and more diverse resources.…”

Cancer PRSweb – an Online Repository with Polygenic Risk Scores (PRS) for Major Cancer Traits and Their Phenome-wide Exploration in Two Independent Biobanks

“…Several challenges remain in PRS research in terms of access, power, and transferability. Nevertheless, PRS have proven to be a valuable tool for risk stratification, especially if combined with non-genetic risk factors [65][66][67]. PRS will likely become more powerful with growing sample sizes, better tools, and more diverse resources.…”

Feature-extraction and analysis based on spatial distribution of amino acids for SARS-CoV-2 Protein sequences

“…The statistical analysis of these spatial features is also analyzed with the help of STATISTICA 10.0 software in the upcoming sections. The following section briefly describes these methods with reference to similar works [ [47] , [48] , [49] ].…”