Data from Bei et al. (2018). Here we give a brief summary of the data - see
the original paper for full details.
Samples were randomly selected from longitudinal cohorts collected from
Dielmo/Ndiop, Senegal, and focus on 2 distant time-points (2001-2002, and
2014). The first time-point corresponds to a period of high transmission, and
the second a period of extremely low transmission. Samples were genotyped
using a 24-SNP barcode, and complexity of infection was estimated using the
COIL algorithm. Samples were also matched against a large database of
previously published and unpublished barcodes. For samples from the low
transmission period (2014), one of the three repeated barcode clusters (n =
6) corresponded to a parasite type (haplotype 3), observed in Thiès in both
2007 and 2010. The other 2 clusters (IP1, n = 2; IP2, n = 2) had not
previously been observed. Data were extracted using Tabula v1.2.1.
data(Bei_2018)A list of multiple data objects:
EIR: estimates of the EIR at each location and year
barcodes: genetic data and associated sample characteristics
SNP_locations: genomic locations of SNPs (key to be used with barcodes)
EIR: A dataframe with 3 columns, giving the time, the sampling
location, and the estimated EIR (see original paper for details). The EIR
in 2014 in Ndiop was recorded as "0.0" in the paper, and so has been coded
as "<0.05" here to indicate the precision of this estimate.
barcodes: A dataframe with 34 columns. Gives sample characteristics
(columns 1:7), the estimated COI and whether this indicates a
monogenomic/polygenomic infection (columns 8:9), the individual SNPs
(columns 10:33) and the corresponding haplotype, if known (column 34).
SNP_locations: A dataframe that acts as a key relating the SNP codes
present in barcodes to the corresponding genomic location.
Bei AK, Niang M, Deme AB, Daniels RF, Sarr FD, Sokhna C, Talla C, Faye J, Diagne N, Doucoure S, Mboup S, Wirth DF, Tall A, Ndiaye D, Hartl DL, Volkman SK, Toure-Balde A (2018). “Dramatic Changes in Malaria Population Genetic Complexity in Dielmo and Ndiop, Senegal, Revealed Using Genomic Surveillance.” The Journal of Infectious Diseases, 217(4), 622--627. ISSN 0022-1899, 1537-6613, doi: 10.1093/infdis/jix580 , https://academic.oup.com/jid/article/217/4/622/4793403.