Background: The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases. Results: We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to discover gene-disease relationships from the GeneRIF database. We utilized a comprehensive subset of UMLS, which is disease-focused and structured as a directed acyclic graph (the Disease Ontology), to filter and interpret results from MMTx. The results were validated against the Homayouni gene collection using recall and precision measurements. We compared our results with the widely used Online Mendelian Inheritance in Man (OMIM) annotations. Conclusion: The validation data set suggests a 91% recall rate and 97% precision rate of disease annotation using GeneRIF, in contrast with a 22% recall and 98% precision using OMIM. Our thesaurus-based approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for diseases dramatically increases the coverage of the disease annotation of human genome.

Comparative genomics of 45 epidemiologically varied variola virus isolates from the past 30 years of the smallpox era indicate low sequence diversity, suggesting that there is probably little difference in the isolates' functional gene content. Phylogenetic clustering inferred three clades coincident with their geographical origin and case-fatality rate; the latter implicated putative proteins that mediate viral virulence differences. Analysis of the viral linear DNA genome suggests that its evolution involved direct descent and DNA end-region recombination events. Knowing the sequences will help understand the viral proteome and improve diagnostic test precision, therapeutics, and systems for their assessment.

Mycoplasma pneumoniae is a common pathogen that causes upper and lower respiratory tract infections in people of all ages, responsible for up to 40 % of community-acquired pneumonias. It also causes a wide array of extrapulmonary infections and autoimmune phenomena. Phylogenetic studies of the organism have been generally restricted to specific genes or regions of the genome, because whole genome sequencing has been completed for only 4 strains. To better understand the physiology and pathogenicity of this important human pathogen, we performed comparative genomic analysis of 15 strains of M. pneumoniae that were isolated between the 1940s to 2009 from respiratory specimens and cerebrospinal fluid originating from the USA, China and England. Illumina MiSeq whole genome sequencing was performed on the 15 strains and all genome sequences were completed. Results from the comparative genomic analysis indicate that although about 1500 SNP and indel variants exist between type1 and type 2 strains, there is an overall high degree of sequence similarity among the strains (>99 % identical to each other). Within the two subtypes, conservation of most genes, including the CARDS toxin gene and arginine deiminase genes, was observed. The major variation occurs in the P1 and ORF6 genes associated with the adhesin complex. Multiple hsdS genes (encodes S subunit of type I restriction enzyme) with variable tandem repeat copy numbers were found in all 15 genomes. These data indicate that despite conclusions drawn from 16S rRNA sequences suggesting rapid evolution, the M. pneumoniae genome is extraordinarily stable over time and geographic distance across the globe with a striking lack of evidence of horizontal gene transfer.

Several mycoplasma species have been shown to form biofilms that confer resistance to antimicrobials and which may affect the host immune system, thus making treatment and eradication of the pathogens difficult. The present study shows that the biofilms formed by two strains of the human pathogen Mycoplasma pneumoniae differ quantitatively and qualitatively. Compared with strain UAB PO1, strain M129 grows well but forms biofilms that are less robust, with towers that are less smooth at the margins. A polysaccharide containing N-acetylglucosamine is secreted by M129 into the culture medium but found in tight association with the cells of UAB PO1. The polysaccharide may have a role in biofilm formation, contributing to differences in virulence, chronicity and treatment outcome between strains of M. pneumoniae. The UAB PO1 genome was found to be that of a type 2 strain of M. pneumoniae, whereas M129 is type 1. Examination of other M. pneumoniae isolates suggests that the robustness of the biofilm correlates with the strain type.

Summary: Poxvirus Orthologous Clusters (POCs) is a JAVA client–server application which accesses an updated database containing all complete poxvirus genomes; it automatically groups orthologous genes into families based on BLASTP scores for assessment by a human database curator. POCs has a user-friendly interface permitting complex SQL queries to retrieve interesting groups of DNA and protein sequences as well as gene families for subsequent interrogation by a variety of integrated tools: BLASTP, BLASTX, TBLASTN, Jalview (multiple alignment), Dotlet (Dotplot), Laj (local alignment), and NAP (nucleotide to amino acid alignment). Availability: Direct access to the POCs database via the GENOME ANALYSIS link at the Poxvirus Bioinformatics Resource: http://www.poxvirus.org. The Software is available for download via HTTP at: http://athena.bioc.uvic.ca/pbr/POCs/pocs.html. Contact: cupton@uvic.ca Supplementary Information: Installation instructions, the User's Manual, screenshots, and examples are available at the POCs home page http://athena.bioc.uvic.ca/pbr/POCs/pocs.html. The software is free for non-commercial applications. For information on poxviruses see http://www.poxvirus.org.

Objective To help cancer registrars efficiently and accurately identify reportable cancer cases. Material and Methods The Cancer Registry Control Panel (CRCP) was developed to detect mentions of reportable cancer cases using a pipeline built on the Unstructured Information Management Architecture – Asynchronous Scaleout (UIMA-AS) architecture containing the National Library of Medicine’s UIMA MetaMap annotator as well as a variety of rule-based UIMA annotators that primarily act to filter out concepts referring to nonreportable cancers. CRCP inspects pathology reports nightly to identify pathology records containing relevant cancer concepts and combines this with diagnosis codes from the Clinical Electronic Data Warehouse to identify candidate cancer patients using supervised machine learning. Cancer mentions are highlighted in all candidate clinical notes and then sorted in CRCP’s web interface for faster validation by cancer registrars. Results CRCP achieved an accuracy of 0.872 and detected reportable cancer cases with a precision of 0.843 and a recall of 0.848. CRCP increases throughput by 22.6% over a baseline (manual review) pathology report inspection system while achieving a higher precision and recall. Depending on registrar time constraints, CRCP can increase recall to 0.939 at the expense of precision by incorporating a data source information feature. Conclusion CRCP demonstrates accurate results when applying natural language processing features to the problem of detecting patients with cases of reportable cancer from clinical notes. We show that implementing only a portion of cancer reporting rules in the form of regular expressions is sufficient to increase the precision, recall, and speed of the detection of reportable cancer cases when combined with off-the-shelf information extraction software and machine learning.