EVidenceModeler (EVM) is presented as an automated eukaryotic gene structure annotation tool that reports eukaryotic gene structures as a weighted consensus of all available evidence. EVM, when combined with the Program to Assemble Spliced Alignments (PASA), yields a comprehensive, configurable annotation system that predicts protein-coding genes and alternatively spliced isoforms. Our experiments on both rice and human genome sequences demonstrate that EVM produces automated gene structure annotation approaching the quality of manual curation.

The parasite Plasmodium falciparum is responsible for hundreds of millions of cases of malaria, and kills more than one million African children annually. Here we report an analysis of the genome sequence of P. falciparum clone 3D7. The 23-megabase nuclear genome consists of 14 chromosomes, encodes about 5,300 genes, and is the most (A + T)-rich genome sequenced to date. Genes involved in antigenic variation are concentrated in the subtelomeric regions of the chromosomes. Compared to the genomes of free-living eukaryotic microbes, the genome of this intracellular parasite encodes fewer enzymes and transporters, but a large proportion of genes are devoted to immune evasion and host–parasite interactions. Many nuclear-encoded proteins are targeted to the apicoplast, an organelle involved in fatty-acid and isoprenoid metabolism. The genome sequence provides the foundation for future studies of this organism, and is being exploited in the search for new drugs and vaccines to fight malaria.

Cryptococcus neoformans is a basidiomycetous yeast ubiquitous in the environment, a model for fungal pathogenesis, and an opportunistic human pathogen of global importance. We have sequenced its ∼20-megabase genome, which contains ∼6500 intron-rich gene structures and encodes a transcriptome abundant in alternatively spliced and antisense messages. The genome is rich in transposons, many of which cluster at candidate centromeric regions. The presence of these transposons may drive karyotype instability and phenotypic variation. C. neoformans encodes unique genes that may contribute to its unusual virulence properties, and comparison of two phenotypically distinct strains reveals variation in gene content in addition to sequence polymorphisms between the genomes.

Species of malaria parasite that infect rodents have long been used as models for malaria disease research. Here we report the whole-genome shotgun sequence of one species, Plasmodium yoelii yoelii, and comparative studies with the genome of the human malaria parasite Plasmodium falciparum clone 3D7. A synteny map of 2,212 P. y. yoelii contiguous DNA sequences (contigs) aligned to 14 P. falciparum chromosomes reveals marked conservation of gene synteny within the body of each chromosome. Of about 5,300 P. falciparum genes, more than 3,300 P. y. yoelii orthologues of predominantly metabolic function were identified. Over 800 copies of a variant antigen gene located in subtelomeric regions were found. This is the first genome sequence of a model eukaryotic parasite, and it provides insight into the use of such systems in the modelling of Plasmodium biology and disease.

We report the genome sequence of Theileria parva , an apicomplexan pathogen causing economic losses to smallholder farmers in Africa. The parasite chromosomes exhibit limited conservation of gene synteny with Plasmodium falciparum , and its plastid-like genome represents the first example where all apicoplast genes are encoded on one DNA strand. We tentatively identify proteins that facilitate parasite segregation during host cell cytokinesis and contribute to persistent infection of transformed host cells. Several biosynthetic pathways are incomplete or absent, suggesting substantial metabolic dependence on the host cell. One protein family that may generate parasite antigenic diversity is not telomere-associated.

Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing classification accuracy with computational efficiency presents a fundamental challenge. Results: A method is presented to shift computational costs to an off-line computation by creating a taxonomy/genome index that supports scalable metagenomic classification. Scalable performance is demonstrated on real and simulated data to show accurate classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take <20 h on a single node 40 core large memory machine and provide new insights on the metagenomic contents of the sample. Availability: Software was implemented in C++ and is freely available at http://sourceforge.net/projects/lmat Contact:allen99@llnl.gov Supplementary information: Supplementary data are available at Bioinformatics online.