Figure 2. Workflow schema. Input: Viral protein sequences, typically obtained from publicly available databases (NCBI virus and GISAID, among others), aligned and submitted to DiMA in aligned FASTA (.afa) format. Process: DiMA provides a quantitative measure of sequence diversity by use of Shannon’s entropy, applied via a user-defined k-mer sliding window. Further, the entropy value is corrected for sample size bias by applying a statistical adjustment (Lipinski’s rule). Additionally, DiMA further interrogates the diversity by dissecting the entropy value at each k-mer position to various distinct k-mer sequences that are classified into diversity motifs (index, major, minor and unique; see Section 3 for the definition of the diversity motifs) based on their incidence. Output: The entropy values, diversity motifs, and each of the k-mer corresponding metadata is plotted to provide a panoramic overview of the protein sequence diversity.
2.1. Entropy algorithm
2.2. Performance testing of DiMA
DiMA has been extensively tested with 18 protein datasets from six viral species. External validation of our tool has been performed by three individuals, with a total of 36 protein datasets, originating from three viral species.
2.3. Performance testing of Sample size bias correction
As it was explained in Figure 3, entropy is corrected for sample size bias. Uncorrected (baseline) and corrected entropy values were calculated over a wide range of sample sizes (100 to 100,000)subsetted from SARS-CoV-2 Spike protein alignment. Then, protein-wide average entropy were plotted for each dataset.
Conclusion:
The baseline entropy value appears to be generally an under-estimate relative to the corrected entropy, which can be a reflection of better data distribution achieved through the resampling approach for the corrected entropy.