1.1. Sequence Diversity Dynamics Analyser for Viruses (DiMA)

Viral infectious diseases are a major public health threat. Sequence diversity is one of the major challenges in the design of diagnostic, prophylactic and therapeutic interventions against viruses. The diversity can be an outcome of a combination of underlying evolutionary processes (mutation, recombination, and assortment). A continuing goal is a greater understanding of viral proteome sequence diversity, the dynamics of substitutions, and effective strategies to overcome the diversity for drug or vaccine design.

Herein, we present Diversity Motif Analyser (DiMA), a tool designed to facilitate the quantification and dissection of viral sequence diversity dynamics. DiMA provides a quantitative measure of sequence diversity by use of Shannon’s entropy (PMID: 18698358), applied via a user-defined k-mer sliding window to a protein alignment. Additionally, DiMA further interrogates the diversity by dissecting the entropy at each aligned k-mer position to various diversity motifs (PMIDs: 32518710, 23593157), based on the incidence of distinct k-mer sequences at the position. At a given position, index is the predominant sequence and all other distinct k-mers are referred to as total variants to the index, sub-classified into major variant (the most common variant), minor variants (comprising of k-mers with incidence lower than major and higher than unique), and unique variants (k-mers seen only once in the alignment). Moreover, the description line of the sequences in the input alignment can be enriched for inclusion of meta-data as part of the analysis, such as spatio-temporal information, among others. DiMA outputs a JSON file that provides multiple facets of sequence diversity: sequence name, k-mer position, entropy, distinct k-mers at the position, and their incidence, motif classification and metadata (if available). DiMA enables comparative sequence diversity dynamics analyses, within and between proteins of a virus species, and proteomes of different species.

DiMA is an outcome of many years of viral studies on several different species since 2007. These studies have been published in peer-reviewed journals (PMIDs: 17434154, 18030326, 18698358, 19401763, 21471731, 22573867, 23593157, 26680743, 29322922, 29363421, 31874646, 34068495 and 32518710 and international conferences. Besides being available as a webserver, it can also be downloaded as a standalone client tool (https://github.com/PU-SDS/DiMA), particularly for big data analyses.

DiMA webserver has been under development since March 2020. It has been extensively tested with 18 datasets (9 structural and 9 non-structural proteins) from six viral species. External validation of our tool has been performed by three individuals, with a total of 36 datasets (18 structural and 18 non-structural proteins), originating from three viral species.

1.2. Accesibility

The webserver is publicly available at: https://dima.bezmialem.edu.tr/

1.3. Browser compatibility

1.4. Frontend/Backend Frameworks

Python FastAPI utilized for DiMA webserver Backend. ReactJS utilized for DiMA webserver Frontend.

1.5. Novel features

Table 1. Novel features of DiMA compared to similar web servers

Features	DiMA	PVS	LANL Entropy	BV-BRC Variation Analysis
Analysis of nucleic acid - amino acid sequences	✅ - ✅	❌ - ✅	✅ - ✅	✅ - ✅
Shannon Entropy on user-defined sliding window	✅	❌	✅	❌
Entropy correction for size bias	✅	❌	❌	❌
K-mer/variant frequency calculation	✅	❌	❌	✅
K-mer diversity motifs classification	✅	❌	❌	❌
Metadata inclusion	✅	❌	❌	❌
Idenfication of historically conserved sequences	✅	✅	❌	❌
Output and visualizations	Dashboard with interactive plots	Static page with a line plot and interactive 3D-structure	Static page and a line plot	Static images: line plot, sequence logo
Web service input size limit	100 MB ⭐	~0.2 MB	No limit	No limit

⭐ Analysis of larger files possible with CLI version.

1.6. Defining diversity motifs

For a given sequence alignment, all sequences at each of the aligned k-mer positions are quantified for distinct sequences and ranked-classified into diversity motifs based on their incidences, as described in Hu et al. (2013) (Supplementary Figure 1, see extract below) (PMID: 23593157).

Figure 1. Definitions of diversity motifs. The ‘‘Index’’ nonamer is the most prevalent sequence, present in 8 of the 20 isolates. The ‘‘Major’’ variant is the most common variant of the index (5/20). ‘‘Minor’’ variants are multiple different repeated sequences, each with incidences less than the major variant. ‘‘Unique’’ variants are those represented by a single aligned sequence. Distinct variant sequences at a given nonamer position are the different sequence at the position; in this example one of major, two of minor, and three of unique.