Publications (48) View all
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Article: SNP genotyping defines complex gene-flow boundaries among African malaria vector mosquitoes.
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ABSTRACT: Mosquitoes in the Anopheles gambiae complex show rapid ecological and behavioral diversification, traits that promote malaria transmission and complicate vector control efforts. A high-density, genome-wide mosquito SNP-genotyping array allowed mapping of genomic differentiation between populations and species that exhibit varying levels of reproductive isolation. Regions near centromeres or within polymorphic inversions exhibited the greatest genetic divergence, but divergence was also observed elsewhere in the genomes. Signals of natural selection within populations were overrepresented among genomic regions that are differentiated between populations, implying that differentiation is often driven by population-specific selective events. Complex genomic differentiation among speciating vector mosquito populations implies that tools for genome-wide monitoring of population structure will prove useful for the advancement of malaria eradication.Science 10/2010; 330(6003):514-7. · 31.20 Impact Factor -
Article: Widespread divergence between incipient Anopheles gambiae species revealed by whole genome sequences.
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ABSTRACT: The Afrotropical mosquito Anopheles gambiae sensu stricto, a major vector of malaria, is currently undergoing speciation into the M and S molecular forms. These forms have diverged in larval ecology and reproductive behavior through unknown genetic mechanisms, despite considerable levels of hybridization. Previous genome-wide scans using gene-based microarrays uncovered divergence between M and S that was largely confined to gene-poor pericentromeric regions, prompting a speciation-with-ongoing-gene-flow model that implicated only about 3% of the genome near centromeres in the speciation process. Here, based on the complete M and S genome sequences, we report widespread and heterogeneous genomic divergence inconsistent with appreciable levels of interform gene flow, suggesting a more advanced speciation process and greater challenges to identify genes critical to initiating that process.Science 10/2010; 330(6003):512-4. · 31.20 Impact Factor -
Article: Hidden variable analysis of transcription factor cooperativity from microarray time courses
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ABSTRACT: Gene expression is regulated by transcription factor activity, which can be extremely difficult to measure directly. Previous work has established a method to extract the `hidden` transcription factor activity profile from microarray data and use it to effectively identify genes that are targets of a single transcription factor. However, most genes are regulated by two or more transcription factors, and so may not be recognised by this method. Here, the authors present a model-based analysis technique which is able to extract two separate `hidden` transcription factor profiles using microarray data from wild-type and gene knock-down samples. The algorithm can predict targets of each of the transcription factors as well as the amount of cooperative regulation of genes which occurs because of the interaction between the two transcription factors. The authors evaluate this method using simulated data, and show that it is highly effective at classifying genes into categories based on their relative regulation by each of the transcription factors. The authors also show that our method can accurately measure the effectiveness of a gene knock-down when including of a reasonable amount of measurement error.IET Systems Biology 04/2010; · 1.35 Impact Factor -
Article: Hidden variable analysis of transcription factor cooperativity from microarray time courses.
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ABSTRACT: Gene expression is regulated by transcription factor activity, which can be extremely difficult to measure directly. Previous work has established a method to extract the 'hidden' transcription factor activity profile from microarray data and use it to effectively identify genes that are targets of a single transcription factor. However, most genes are regulated by two or more transcription factors, and so may not be recognised by this method. Here, the authors present a model-based analysis technique which is able to extract two separate 'hidden' transcription factor profiles using microarray data from wild-type and gene knock-down samples. The algorithm can predict targets of each of the transcription factors as well as the amount of cooperative regulation of genes which occurs because of the interaction between the two transcription factors. The authors evaluate this method using simulated data, and show that it is highly effective at classifying genes into categories based on their relative regulation by each of the transcription factors. The authors also show that our method can accurately measure the effectiveness of a gene knock-down when including of a reasonable amount of measurement error.IET Systems Biology 03/2010; 4(2):131-44. · 1.35 Impact Factor -
Article: An expression map for Anopheles gambiae.
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ABSTRACT: Quantitative transcriptome data for the malaria-transmitting mosquito Anopheles gambiae covers a broad range of biological and experimental conditions, including development, blood feeding and infection. Web-based summaries of differential expression for individual genes with respect to these conditions are a useful tool for the biologist, but they lack the context that a visualisation of all genes with respect to all conditions would give. For most organisms, including A. gambiae, such a systems-level view of gene expression is not yet available. We have clustered microarray-based gene-averaged expression values, available from VectorBase, for 10194 genes over 93 experimental conditions using a self-organizing map. Map regions corresponding to known biological events, such as egg production, are revealed. Many individual gene clusters (nodes) on the map are highly enriched in biological and molecular functions, such as protein synthesis, protein degradation and DNA replication. Gene families, such as odorant binding proteins, can be classified into distinct functional groups based on their expression and evolutionary history. Immunity-related genes are non-randomly distributed in several distinct regions on the map, and are generally distant from genes with house-keeping roles. Each immunity-rich region appears to represent a distinct biological context for pathogen recognition and clearance (e.g. the humoral and gut epithelial responses). Several immunity gene families, such as peptidoglycan recognition proteins (PGRPs) and defensins, appear to be specialised for these distinct roles, while three genes with physically interacting protein products (LRIM1/APL1C/TEP1) are found in close proximity. The map provides the first genome-scale, multi-experiment overview of gene expression in A. gambiae and should also be useful at the gene-level for investigating potential interactions. A web interface is available through the VectorBase website http://www.vectorbase.org/. It is regularly updated as new experimental data becomes available.BMC Genomics 12/2011; 12:620. · 4.07 Impact Factor
