Ascaris suum draft genome

Abstract

Parasitic diseases have a devastating, long-term impact on human health, welfare and food production worldwide. More than two billion people are infected with geohelminths, including the roundworms Ascaris (common roundworm), Necator and Ancylostoma (hookworms), and Trichuris (whipworm), mainly in developing or impoverished nations of Asia, Africa and Latin America(1). In humans, the diseases caused by these parasites result in about 135,000 deaths annually, with a global burden comparable with that of malaria or tuberculosis in disability-adjusted life years(1). Ascaris alone infects around 1.2 billion people and, in children, causes nutritional deficiency, impaired physical and cognitive development and, in severe cases, death(2). Ascaris also causes major production losses in pigs owing to reduced growth, failure to thrive and mortality(2). The Ascaris-swine model makes it possible to study the parasite, its relationship with the host, and ascariasis at the molecular level. To enable such molecular studies, we report the 273 mega-base draft genome of Ascaris suum and compare it with other nematode genomes. This genome has low repeat content (4.4%) and encodes about 18,500 protein-coding genes. Notably, the A. suum secretome (about 750 molecules) is rich in peptidases linked to the penetration and degradation of host tissues, and an assemblage of molecules likely to modulate or evade host immune responses. This genome provides a comprehensive resource to the scientific community and underpins the development of new and urgently needed interventions (drugs, vaccines and diagnostic tests) against ascariasis and other nematodiases

Whole-genome sequence of Schistosoma haematobium

“…To facilitate gene annotation, the combined RNA-seq data generated (separately) from the male, female and egg cDNA libraries for S. haematobium were assembled de novo using SOAPdenovo 36 . Expressed sequence tag (EST) data for S. haematobium were used to train and validate gene models generated from the draft genome, as described previously 39 . Parameters for the gene-prediction model were established using a set of 1,355 S. haematobium genes encoding complete open reading frames (ORFs) that were predicted from an unmasked draft of the S. haematobium genome sequence.…”

“…Parameters for the gene-prediction model were established using a set of 1,355 S. haematobium genes encoding complete open reading frames (ORFs) that were predicted from an unmasked draft of the S. haematobium genome sequence. Genes were predicted 39 and based on homology to the S. mansoni and S. japonicum gene sets (both v.4; see URLs). De novo-assembled EST data (from this study and available from NCBI) and raw, paired-end RNA-seq data were mapped to the genome and transcripts were predicted 39 .…”

“…Genes were predicted 39 and based on homology to the S. mansoni and S. japonicum gene sets (both v.4; see URLs). De novo-assembled EST data (from this study and available from NCBI) and raw, paired-end RNA-seq data were mapped to the genome and transcripts were predicted 39 . The predicted genes were merged to establish nonredundant gene sets 39 .…”

“…De novo-assembled EST data (from this study and available from NCBI) and raw, paired-end RNA-seq data were mapped to the genome and transcripts were predicted 39 . The predicted genes were merged to establish nonredundant gene sets 39 . Subsequently, we classified the predicted S. haematobium genes on the basis of experimental evidence of homology to genes of other eukaryotic organisms.…”

“…The S. haematobium gene set was then annotated on the basis of homology to sequences within public sequence, gene ontology and biological pathway databases using an established pipeline 39 . In addition, classical excretorysecretory (ES) proteins of S. haematobium were predicted (on the basis of the presence of a signal peptide at the N terminus) using SignalP v.3.0 (ref.…”