The African Society of Human Genetics successfully launches global data science workshops

Nembaware, Victoria; Bennett, Declan; Chimusa, Emile R.; Chikowore, Tinashe; Daodu, Richard; Bitoungui, Valentina Ngo; Williams, Scott M.; Fatumo, Segun; Healy, Sandra; Seoighe, Cathal; Wonkam, Ambroise; Landouré, Guida; Ndiaye, Rokhaya; Dandara, Collet; Mutesa, Leon; Ramsay, Michele; El-Kamah, Ghada; Sirugo, Giorgio; Makani, Julie; Sadki, Khalid; Alimohamed, Mohamed Zahir; Nkya, Siana; Gaye, Amadou; Ramesar, Raj; Choudhury, Ananyo; Happi, Christian; Munung, Nchangwi Syntia; Kherji, Nadia; Hotchkiss, Jade; Ras, Verena; Ghansay, Anita; Musanabaganwa, Clarisse; Esoh, Kevin Kum; Adadey, Samuel Mawuli; Coughlan, Simone Christina

doi:10.1016/j.tig.2023.06.004

Cited by 1 publication

(1 citation statement)

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“…However, as more data emerges, the model will need to be updated using the workflow we have provided. This tool is a significant contribution to bioinformatics capacity on the African continent, a capacity that several African researchers have been calling for in recent times (Nembaware et al, 2023;Olono et al, 2024;Sharaf et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Reveals Key Glycoprotein Mutations and Rapidly Assigns Lassa Virus Lineages

Daodu,

Uwe-Ulrich,

Kühnert

2024

Preprint

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Lassa fever, caused by the Lassa virus (LASV), has led to numerous fatalities in West Africa and cases exported intercontinentally since its discovery in 1969. Currently, there are no approved vaccines, with recent research focusing on immunotherapy. Lassa virus is grouped into different lineages that circulate in specific geographical areas, elicit varying immune responses, and display distinct pathophysiological effects. Therefore, investigating the genetic differences between these lineages is crucial. Here, we analyzed the LASV glycoprotein, the only surface protein, using statistics, machine learning, and phylogenetics to identify key differences between Nigerian lineages and those endemic to other West African countries. We found that amino acid positions near the stable signal peptide cleavage site and sites impacting immune recognition, such as those between positions 59 and 76, were highly variable among the lineages. Additionally, we discovered that Lineage II and Lineage III sequences are one codon shorter than Lineage IV sequences, due to a codon insertion in positions 178-180, corresponding to amino acid position 60. This may explain the structural and phenotypical differences between the lineages. To quickly identify which lineages cause emerging outbreaks or exported infections, we also developed a highly accurate lineage classification tool based on machine learning.

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Section: Discussionmentioning

confidence: 99%