Analysis of The Nucleotide Sequence Diversity of the Lassa Virus and Augmenting its Phylogenetic Tree
Bibliographic record
Abstract
Lassa Virus (LASV) is the etiological catalyst for Lassa fever, an acute hemorrhagic disease with a mortality rate of 15%. Many aspects of the Lassa virus are not understood, like the causation of deafness in ⅓ of surviving patients or why symptoms are benign for 80% of those infected with the virus. Ambiguities like these suggest that there might exist some genomic heterogeneity among infecting viruses and demonstrate a need to quantify and analyze polymorphisms within LASV. Patterns that emerge from phylogenetic trees can be used to assess the structure of a population while also providing insights to the genetic makeup. The purpose of this investigation was to develop a more streamlined means of calculating nucleotide diversity within a subpopulation of Lassa virus strains and to augment a phylogenetic tree of the Lassa Virus glycoprotein precursor (GPC) segment. A total of 25 partial and complete data sequences of LASV strains were obtained from the Genbank Archives. During phase one of this investigation, the sequence data was inputted into MEGA analytical software and the sequence diversity was derived on a nucleotide level. Data from the individual strand sequences was used to augment a phylogenetic tree using Treeview X software. In phase two of this investigation, an algorithm was created using RStudio, with BSGenome and BioStrings extensions. The sequence diversity derived from the statistical analyses on MEGA was compared to that of the algorithm created. A p-value of 0.08 was found, which deviates from the accepted range of non-medical p-value of 0.00 to 0.05. It is suggested that future research focuses on creating a refurbished version of the algorithm to calculate a nucleotide diversity within a percent error of 5%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".