De Novo Transcriptome Sequencing in Anopheles funestus Using Illumina RNA-Seq Technology

Abstract

Background Anopheles funestus is one of the primary vectors of human malaria, which causes a million deaths each year in sub-Saharan Africa. Few scientific resources are available to facilitate studies of this mosquito species and relatively little is known about its basic biology and evolution, making development and implementation of novel disease control efforts more difficult. The An. funestus genome has not been sequenced, so in order to facilitate genome-scale experimental biology, we have sequenced the adult female transcriptome of An. funestus from a newly founded colony in Burkina Faso, West Africa, using the Illumina GAIIx next generation sequencing platform.Methodology/Principal FindingsWe assembled short Illumina reads de novo using a novel approach involving iterative de novo assemblies and “target-based” contig clustering. We then selected a conservative set of 15,527 contigs through comparisons to four Dipteran transcriptomes as well as multiple functional and conserved protein domain databases. Comparison to the Anopheles gambiae immune system identified 339 contigs as putative immune genes, thus identifying a large portion of the immune system that can form the basis for subsequent studies of this important malaria vector. We identified 5,434 1∶1 orthologues between An. funestus and An. gambiae and found that among these 1∶1 orthologues, the protein sequence of those with putative immune function were significantly more diverged than the transcriptome as a whole. Short read alignments to the contig set revealed almost 367,000 genetic polymorphisms segregating in the An. funestus colony and demonstrated the utility of the assembled transcriptome for use in RNA-seq based measurements of gene expression.Conclusions/SignificanceWe developed a pipeline that makes de novo transcriptome sequencing possible in virtually any organism at a very reasonable cost ($6,300 in sequencing costs in our case). We anticipate that our approach could be used to develop genomic resources in a diversity of systems for which full genome sequence is currently unavailable. Our An. funestus contig set and analytical results provide a valuable resource for future studies in this non-model, but epidemiologically critical, vector insect.

Discovery of clubroot-resistant genes in Brassica napus by transcriptome sequencing

“…Tang et al (2011) conducted transcriptome sequencing in Siraitia grosvenorii and identified many genes related to the biosynthesis of S. grosvenorii glycoside. Crawford et al (2010) performed sequencing and analysis of the transcriptome on Anopheles funestus, and found some unigenes related to immune genes APL1 and LRIM1. Lin et al (2012) used transcriptome sequencing technology to obtain the complete cDNA sequence of the camellia ubiquitin-conjugating enzyme gene, which was named UBE2S and encoded a 270-amino acid protein.…”

Next-generation transcriptome assembly

“…In a recent study, catfish transcripts were aligned to the stickleback proteome to achieve significantly longer transcripts (the N50 size increased by 27%) 21 . The mosquito transcriptome was scaffolded using the same technique 24 .…”

“…In this review, we summarize these exciting breakthroughs that have resulted in a wealth of assembled transcriptomes from short reads [16][17][18][19][20][21][22][23][24][25][26][27] , while providing practical guidelines for implementing a transcriptome assembly experiment. We discuss the experimental and informatics considerations that need to be made before assembly, such as RNA-Seq library construction, data pre-processing and how to assess the assembly quality.…”

Survey of Global Genetic Diversity Within theDrosophilaImmune System

“…Immune genes are often evolutionary outliers in such studies, displaying fast rates of evolution and high population differentiation across multiple taxa including humans (Fumagalli et al 2011;Daub et al 2013;Quintana-Murci and Clark 2013), Daphnia (McTaggart et al 2012), mosquitoes (Waterhouse et al 2007;Crawford et al 2010), and bees (Chavez-Galarza et al 2013;Erler et al 2014). Across Drosophila species, immune genes are known to evolve faster than the genome average (Sackton et al 2007), and on shorter timescales, both genome-wide studies and studies of individual genes have highlighted examples of immune genes displaying unusually high population differentiation across Drosophila melanogaster populations (Juneja and Lazzaro 2010;Fabian et al 2012;Hubner et al 2013).…”