[show abstract][hide abstract] ABSTRACT: The human malaria parasite Plasmodium falciparum survives pressures from the host immune system and antimalarial drugs by modifying its genome. Genetic recombination and nucleotide substitution are the two major mechanisms that the parasite employs to generate genome diversity. A better understanding of these mechanisms may provide important information for studying parasite evolution, immune evasion and drug resistance.
Here, we used a high-density tiling array to estimate the genetic recombination rate among 32 progeny of a P. falciparum genetic cross (7G8 × GB4). We detected 638 recombination events and constructed a high-resolution genetic map. Comparing genetic and physical maps, we obtained an overall recombination rate of 9.6 kb per centimorgan and identified 54 candidate recombination hotspots. Similar to centromeres in other organisms, the sequences of P. falciparum centromeres are found in chromosome regions largely devoid of recombination activity. Motifs enriched in hotspots were also identified, including a 12-bp G/C-rich motif with 3-bp periodicity that may interact with a protein containing 11 predicted zinc finger arrays.
These results show that the P. falciparum genome has a high recombination rate, although it also follows the overall rule of meiosis in eukaryotes with an average of approximately one crossover per chromosome per meiosis. GC-rich repetitive motifs identified in the hotspot sequences may play a role in the high recombination rate observed. The lack of recombination activity in centromeric regions is consistent with the observations of reduced recombination near the centromeres of other organisms.
[show abstract][hide abstract] ABSTRACT: Microarray and Next-Gen Sequencing technologies have generated a huge amount of genome scale expression data. Many software tools permit extraction of expression profiles/data for a given gene or a set of genes. It has been established that genes with similar expression profiles are likely to be associated functionally. Unfortunately, there has been no simple online application for mining expression databases for genes with similar profiles. We have developed a method and an associated web tool - Microarray Meta-Miner (http://exon.niaid.nih.gov/MMM/) - to identify genes with expression profiles similar to that of the query gene. Using the microarray meta-data from the ATLAS gene expression database and the NIH Biowulf cluster computing facility, we computed eight different vector similarity metrics (Pearson/Spearman/Kendall correlation coefficients, mutual information, chi-square, Euclidean distance, purity, and cosine similarity) for every gene's expression profile against every other gene's profile. Top scoring hits from the individual metrics were integrated and scored for overlap, generating a matrix of similar expressions. The MMM web tool returns the list of similarly expressed genes for the users query, along with links to annotations, individual expression profiles, and all expression profiles. MMM also retrieves and displays the known interaction data between the similarly expressed genes from the STRING interaction database. The experiment distribution information for the set of similarly expressed genes is also displayed. The current version of MMM supports only human data. Future plans include adding support for other organisms.