Entropy Based Clustering of Viral Sequences

Clustering viral sequences allows us to characterize the composition and structure of intrahost and interhost viral populations, which play a crucial role in disease progression and epidemic spread. In this paper we propose and validate a new entropy based method for clustering aligned viral sequences considered as categorical data. The method finds a homogeneous clustering by minimizing information entropy rather than distance between sequences in the same cluster. We have applied our entropy based clustering method to SARS-CoV-2 viral sequencing data. We report the information content extracted from the sequences by entropy based clustering. Our method converges to similar minimum-entropy clusterings across different runs and limited permutations of data. We also show that a parallelized version of our tool is scalable to very large SARS-CoV-2 datasets.

Whole Sequencing and Detailed Analysis of SARS-CoV-2 Genomes in Southeast Spain: Identification of Recurrent Mutations in the 20E (EU1) Variant with Some Clinical Implications.

During the COVID-19 pandemic caused by SARS-CoV-2, new waves have been associated with new variants and have the potential to escape vaccinations. Therefore, it is useful to conduct retrospective genomic surveillance research. Herein, we present a detailed analysis of 88 SARS-CoV-2 genomes belonging to samples taken from COVID-19 patients from October 2020 to April 2021 at the "Reina Sofía" Hospital (Murcia, Spain) focused to variant appeared later. The results at the mentioned stage show the turning point since the 20E (EU1) variant was still prevalent (71.6%), but Alpha was bursting to 14.8%. Concern mutations have been found in 5 genomes classified as 20E (EU1), which were not characteristic of this still little evolved variant. Most of those mutations are found in the spike protein, namely Δ69-70, E484K, Q675H and P681H. However, a relevant deletion in ORF1a at positions 3675-3677 was also identified. These mutations have been reported in many later SARS-CoV-2 lineages, including Omicron. Taken together, our data suggest that preferential emergence mutations could already be present in the early converging evolution. Aside from this, the molecular information has been contrasted with clinical data. Statistical analyses suggest that the correlation between age and severity criteria is significantly higher in the viral samples with more accumulated changes.

Entropy Based Clustering of Viral Sequences

Clustering viral sequences allows us to characterize the composition and structure of intrahost and interhost viral populations, which play a crucial role in disease progression and epidemic spread. In this paper we propose and validate a new entropy based method for clustering aligned viral sequences considered as categorical data. The method finds a homogeneous clustering by minimizing information entropy rather than distance between sequences in the same cluster. We have applied our entropy based clustering method to SARS-CoV-2 viral sequencing data. We report the information content extracted from the sequences by entropy based clustering. Our method converges to similar minimum-entropy clusterings across different runs and limited permutations of data. We also show that a parallelized version of our tool is scalable to very large SARS-CoV-2 datasets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Use of DFT Distance Metrics for Classification of SARS-CoV-2 Genomes.

In this work, we investigate using Fourier coefficients (FCs) for capturing useful information about viral sequences in a computationally efficient and compact manner. Specifically, we extract geographic submission location from SARS-CoV-2 sequence headers submitted to the GISAID Initiative, calculate corresponding FCs, and use the FCs to classify these sequences according to geographic location. We show that the FCs serve as useful numerical summaries for sequences that allow manipulation, identification, and differentiation via classical mathematical and statistical methods that are not readily applicable for character strings. Further, we argue that subsets of the FCs may be usable for the same purposes, which results in a reduction in storage requirements. We conclude by offering extensions of the research and potential future directions for subsequent analyses, such as the use of other series transforms for discreetly indexed signals such as genomes.

Topological Analysis for Sequence Variability: Case Study on more than 2K SARS-CoV-2 sequences of COVID-19 infected 54 countries in comparison with SARS-CoV-1 and MERS-CoV.

The pandemic due to novel coronavirus, SARS-CoV-2 is a serious global concern now. More than thousand new COVID-19 infections are getting reported daily for this virus across the globe. Thus, the medical research communities are trying to find the remedy to restrict the spreading of this virus, while the vaccine development work is still under research in parallel. In such critical situation, not only the medical research community, but also the scientists in different fields like microbiology, pharmacy, bioinformatics and data science are also sharing effort to accelerate the process of vaccine development, virus prediction, forecasting the transmissible probability and reproduction cases of virus for social awareness. With the similar context, in this article, we have studied sequence variability of the virus primarily focusing on three aspects: (a) sequence variability among SARS-CoV-1, MERS-CoV and SARS-CoV-2 in human host, which are in the same coronavirus family, (b) sequence variability of SARS-CoV-2 in human host for 54 different countries and (c) sequence variability between coronavirus family and country specific SARS-CoV-2 sequences in human host. For this purpose, as a case study, we have performed topological analysis of 2391 global genomic sequences of SARS-CoV-2 in association with SARS-CoV-1 and MERS-CoV using an integrated semi-alignment based computational technique. The results of the semi-alignment based technique are experimentally and statistically found similar to alignment based technique and computationally faster. Moreover, the outcome of this analysis can help to identify the nations with homogeneous SARS-CoV-2 sequences, so that same vaccine can be applied to their heterogeneous human population.