We present the genome sequences of a new clinical isolate of the important human pathogen, Aspergillus fumigatus, A1163, and two closely related but rarely pathogenic species, Neosartorya fischeri NRRL181 and Aspergillus clavatus NRRL1. Comparative genomic analysis of A1163 with the recently sequenced A. fumigatus isolate Af293 has identified core, variable and up to 2% unique genes in each genome. While the core genes are 99.8% identical at the nucleotide level, identity for variable genes can be as low 40%. The most divergent loci appear to contain heterokaryon incompatibility (het) genes associated with fungal programmed cell death such as developmental regulator rosA. Cross-species comparison has revealed that 8.5%, 13.5% and 12.6%, respectively, of A. fumigatus, N. fischeri and A. clavatus genes are species-specific. These genes are significantly smaller in size than core genes, contain fewer exons and exhibit a subtelomeric bias. Most of them cluster together in 13 chromosomal islands, which are enriched for pseudogenes, transposons and other repetitive elements. At least 20% of A. fumigatus-specific genes appear to be functional and involved in carbohydrate and chitin catabolism, transport, detoxification, secondary metabolism and other functions that may facilitate the adaptation to heterogeneous environments such as soil or a mammalian host. Contrary to what was suggested previously, their origin cannot be attributed to horizontal gene transfer (HGT), but instead is likely to involve duplication, diversification and differential gene loss (DDL). The role of duplication in the origin of lineage-specific genes is further underlined by the discovery of genomic islands that seem to function as designated “gene dumps” and, perhaps, simultaneously, as “gene factories”.

Massively parallel sequencing approaches are beginning to be used clinically to characterize individual patient tumors and to select therapies based on the identified mutations. A major question in these analyses is the extent to which these methods identify clinically actionable alterations and whether the examination of the tumor tissue alone is sufficient or whether matched normal DNA should also be analyzed to accurately identify tumor-specific (somatic) alterations. To address these issues, we comprehensively evaluated 815 tumor-normal paired samples from patients of 15 tumor types. We identified genomic alterations using next-generation sequencing of whole exomes or 111 targeted genes that were validated with sensitivities >95% and >99%, respectively, and specificities >99.99%. These analyses revealed an average of 140 and 4.3 somatic mutations per exome and targeted analysis, respectively. More than 75% of cases had somatic alterations in genes associated with known therapies or current clinical trials. Analyses of matched normal DNA identified germline alterations in cancer-predisposing genes in 3% of patients with apparently sporadic cancers. In contrast, a tumor-only sequencing approach could not definitively identify germline changes in cancer-predisposing genes and led to additional false-positive findings comprising 31% and 65% of alterations identified in targeted and exome analyses, respectively, including in potentially actionable genes. These data suggest that matched tumor-normal sequencing analyses are essential for precise identification and interpretation of somatic and germline alterations and have important implications for the diagnostic and therapeutic management of cancer patients.

Cassava is a major tropical food crop in the Euphorbiaceae family that has high carbohydrate production potential and adaptability to diverse environments. Here we present the draft genome sequences of a wild ancestor and a domesticated variety of cassava and comparative analyses with a partial inbred line. We identify 1,584 and 1,678 gene models specific to the wild and domesticated varieties, respectively, and discover high heterozygosity and millions of single-nucleotide variations. Our analyses reveal that genes involved in photosynthesis, starch accumulation and abiotic stresses have been positively selected, whereas those involved in cell wall biosynthesis and secondary metabolism, including cyanogenic glucoside formation, have been negatively selected in the cultivated varieties, reflecting the result of natural selection and domestication. Differences in microRNA genes and retrotransposon regulation could partly explain an increased carbon flux towards starch accumulation and reduced cyanogenic glucoside accumulation in domesticated cassava. These results may contribute to genetic improvement of cassava through better understanding of its biology.

BackgroundNext-generation sequencing technologies have decentralized sequence acquisition, increasing the demand for new bioinformatics tools that are easy to use, portable across multiple platforms, and scalable for high-throughput applications. Cloud computing platforms provide on-demand access to computing infrastructure over the Internet and can be used in combination with custom built virtual machines to distribute pre-packaged with pre-configured software.ResultsWe describe the Cloud Virtual Resource, CloVR, a new desktop application for push-button automated sequence analysis that can utilize cloud computing resources. CloVR is implemented as a single portable virtual machine (VM) that provides several automated analysis pipelines for microbial genomics, including 16S, whole genome and metagenome sequence analysis. The CloVR VM runs on a personal computer, utilizes local computer resources and requires minimal installation, addressing key challenges in deploying bioinformatics workflows. In addition CloVR supports use of remote cloud computing resources to improve performance for large-scale sequence processing. In a case study, we demonstrate the use of CloVR to automatically process next-generation sequencing data on multiple cloud computing platforms.ConclusionThe CloVR VM and associated architecture lowers the barrier of entry for utilizing complex analysis protocols on both local single- and multi-core computers and cloud systems for high throughput data processing.

The Institute for Genome Sciences (IGS) has developed a prokaryotic annotation pipeline that is used for coding gene/RNA prediction and functional annotation of Bacteria and Archaea. The fully automated pipeline accepts one or many genomic sequences as input and produces output in a variety of standard formats. Functional annotation is primarily based on similarity searches and motif finding combined with a hierarchical rule based annotation system. The output annotations can also be loaded into a relational database and accessed through visualization tools.

For centuries, cholera has been one of the most feared diseases. The causative agent Vibrio cholerae is a waterborne Gram-negative enteric pathogen eliciting a severe watery diarrheal disease. In October 2010, the seventh pandemic reached Haiti, a country that had not experienced cholera for more than a century. By using whole-genome sequence typing and mapping strategies of 116 serotype O1 strains from global sources, including 44 Haitian genomes, we present a detailed reconstructed evolutionary history of the seventh pandemic with a focus on the Haitian outbreak. We catalogued subtle genomic alterations at the nucleotide level in the genome core and architectural rearrangements from whole-genome map comparisons. Isolates closely related to the Haitian isolates caused several recent outbreaks in southern Asia. This study provides evidence for a single-source introduction of cholera from Nepal into Haiti followed by rapid, extensive, and continued clonal expansion. The phylogeographic patterns in both southern Asia and Haiti argue for the rapid dissemination of V. cholerae across the landscape necessitating real-time surveillance efforts to complement the whole-genome epidemiological analysis. As eradication efforts move forward, phylogeographic knowledge will be important for identifying persistent sources and monitoring success at regional levels. The results of molecular and epidemiological analyses of this outbreak suggest that an indigenous Haitian source of V. cholerae is unlikely and that an indigenous source has not contributed to the genomic evolution of this clade.

Motivation: The growth of sequence data has been accompanied by an increasing need to analyze data on distributed computer clusters. The use of these systems for routine analysis requires scalable and robust software for data management of large datasets. Software is also needed to simplify data management and make large-scale bioinformatics analysis accessible and reproducible to a wide class of target users.Results: We have developed a workflow management system named Ergatis that enables users to build, execute and monitor pipelines for computational analysis of genomics data. Ergatis contains preconfigured components and template pipelines for a number of common bioinformatics tasks such as prokaryotic genome annotation and genome comparisons. Outputs from many of these components can be loaded into a Chado relational database. Ergatis was designed to be accessible to a broad class of users and provides a user friendly, web-based interface. Ergatis supports high-throughput batch processing on distributed compute clusters and has been used for data management in a number of genome annotation and comparative genomics projects.Availability: Ergatis is an open-source project and is freely available at http://ergatis.sourceforge.netContact: jorvis@users.sourceforge.net

Purpose: Exome sequencing (ES) is increasingly used for the diagnosis of rare genetic disease. However, some pathogenic sequence variants within the exome go undetected due to the technical difficulty of identifying them. Mobile element insertions (MEIs) are a known cause of genetic disease in humans but have been historically difficult to detect via ES and similar targeted sequencing methods. Methods: We developed and applied a novel MEI detection method prospectively to samples received for clinical ES beginning in November 2017. Positive MEI findings were confirmed by an orthogonal method and reported back to the ordering provider. In this study, we examined 89,874 samples from 38,871 cases. Results: Diagnostic MEIs were present in 0.03% (95% binomial test confidence interval: 0.02-0.06%) of all cases and account for 0.15% (95% binomial test confidence interval: 0.08-0.25%) of cases with a molecular diagnosis. One diagnostic MEI was a novel founder event. Most patients with pathogenic MEIs had prior genetic testing, three of whom had previous negative DNA sequencing analysis of the diagnostic gene. Conclusion: MEI detection from ES is a valuable diagnostic tool, reveals molecular findings that may be undetected by other sequencing assays, and increases diagnostic yield by 0.15%.