malaria_genes — Malaria var DBLa HVR networks
- Description
Networks of recombinant var genes of the human malaria parasite Plasmodium falciparum, constructed by D. B. Larremore, A. Clauset and C. O. Buckee. The var genes encode the PfEMP1 antigens and diversify mainly by recombination. The data comprise the DBLα domains of 307 var genes from seven parasite genomes, sequenced by Rask et al. (PLOS Comput. Biol. 6, e1000933, 2010). The DBLα domain was divided into nine highly variable regions (HVRs) separated by conserved sequence blocks, and a separate network was built for each HVR: nodes are genes, and two genes are connected if their sequences at that HVR share an amino acid substring whose length is statistically significant, i.e. too long to be expected by chance, indicating a recombination event.
The networks
HVR_1toHVR_9correspond to the nine HVRs; they share the same set of 307 nodes (the node index identifies the same gene in all networks) but have different edges. The networkcombinedis a multilayer network containing the edges of all nine HVRs.The networks have the following properties:
name: the index of the gene (1 to 307), in the order of the source files.CysPoLV: the Cys-PoLV group of the gene (1 to 6), a classification of DBLα sequences based on the number of cysteines and on sequence motifs near HVR6.UPS: the upstream promoter sequence group of the gene, with 1 = A, 2 = B, 3 = C, and -1 if no UPS was identified.HVR(combinedonly): the HVR (1 to 9) in which the edge occurs.
- Tags
- Biological Genetic Unweighted Projection Metadata Multilayer
- Citation
-
- D. B. Larremore, A. Clauset, and C. O. Buckee, "A network approach to analyzing highly recombinant malaria parasite genes." PLOS Computational Biology 9(10), e1003268 (2013). 10.1371/journal.pcbi.1003268 Sci-Hub
- Upstream URL OK
- https://github.com/dblarremore/data_malaria_PLOSCompBiology_2013
- Networks
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Tip: click on a column header to sort the list, or hover over it for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties
Name Nodes Edges $\left<k\right>$ $\sigma_k$ $\lambda_h$ $\tau$ $r$ $c$ $\oslash$ $S$ Kind Mode NPs EPs gt GraphML GML csv HVR_1 307 2,812 18.32 11.05 35.20 76.31 0.60 0.59 8 1.00 Undirected Unipartite name CysPoLV UPS 8 KiB 14 KiB 13 KiB 12 KiB HVR_2 307 1,446 9.42 7.43 22.87 73.31 0.73 0.78 9 0.36 Undirected Unipartite name CysPoLV UPS 7 KiB 11 KiB 11 KiB 9 KiB HVR_3 307 659 4.29 4.29 15.79 10.48 0.98 0.97 5 0.06 Undirected Unipartite name CysPoLV UPS 7 KiB 10 KiB 9 KiB 8 KiB HVR_4 307 1,214 7.91 6.69 17.51 91.92 0.69 0.73 10 0.60 Undirected Unipartite name CysPoLV UPS 7 KiB 11 KiB 10 KiB 9 KiB HVR_5 307 2,684 17.49 11.20 25.86 46.45 0.33 0.36 11 0.97 Undirected Unipartite name CysPoLV UPS 8 KiB 14 KiB 14 KiB 12 KiB HVR_6 307 3,263 21.26 15.78 39.10 62.46 0.64 0.54 10 0.95 Undirected Unipartite name CysPoLV UPS 8 KiB 15 KiB 14 KiB 13 KiB HVR_7 307 11,688 76.14 36.86 98.57 11.89 0.35 0.64 5 1.00 Undirected Unipartite name CysPoLV UPS 8 KiB 32 KiB 32 KiB 32 KiB HVR_8 307 3,961 25.80 23.37 53.69 50.43 0.66 0.67 11 0.89 Undirected Unipartite name CysPoLV UPS 8 KiB 16 KiB 16 KiB 15 KiB HVR_9 307 7,579 49.37 32.06 79.04 5.38 0.48 0.61 5 0.97 Undirected Unipartite name CysPoLV UPS 9 KiB 23 KiB 23 KiB 23 KiB combined 307 35,306 230.01 92.95 159.41 2.34 0.35 0.71 3 1.00 Undirected Unipartite name CysPoLV UPS HRV 25 KiB 99 KiB 97 KiB 90 KiB - Ridiculograms*