John D. Osborne

Annotating the human genome with Disease Ontology.

BackgroundThe human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases.ResultsWe 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.ConclusionThe 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.

Type 1 and type 2 strains of Mycoplasma pneumoniae form different biofilms

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.

Mining Biomedical Data Using MetaMap Transfer (MMTx) and the Unified Medical Language System (UMLS)

Detailed instruction is described for mapping unstructured, free text data into common biomedical concepts (drugs, diseases, anatomy, and so on) found in the Unified Medical Language System using MetaMap Transfer (MMTx). MMTx can be used in applications including mining and inferring relationship between concepts in MEDLINE publications by transforming free text into computable concepts. MMTx is in general not designed to be an end-user program; therefore, a simple analysis is described using MMTx for users without any programming knowledge. In addition, two Java template files are provided for automated processing of the output from MMTx and users can adopt this with minimum Java program experience.

Interpreting microarray results with gene ontology and MeSH.

Methods are described to take a list of genes generated from a microarray experiment and interpret these results using various tools and ontologies. A workflow is described that details how to convert gene identifiers with SOURCE and MatchMiner and then use these converted gene lists to search the gene ontology (GO) and the medical subject headings (MeSH) ontology. Examples of searching GO with DAVID, EASE, and GOMiner are provided along with an interpretation of results. The mining of MeSH using high-density array pattern interpreter with a set of gene identifiers is also described.

Efficient identification of nationally mandated reportable cancer cases using natural language processing and machine learning

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 Medicines 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 CRCPs 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.

Reduction of hydrogen peroxide accumulation and toxicity by a catalase from Mycoplasma iowae.

Mycoplasma iowae is a well-established avian pathogen that can infect and damage many sites throughout the body. One potential mediator of cellular damage by mycoplasmas is the production of H2O2 via a glycerol catabolic pathway whose genes are widespread amongst many mycoplasma species. Previous sequencing of M. iowae serovar I strain 695 revealed the presence of not only genes for H2O2 production through glycerol catabolism but also the first documented mycoplasma gene for catalase, which degrades H2O2. To test the activity of M. iowae catalase in degrading H2O2, we studied catalase activity and H2O2 accumulation by both M. iowae serovar K strain DK-CPA, whose genome we sequenced, and strains of the H2O2-producing species Mycoplasma gallisepticum engineered to produce M. iowae catalase by transformation with the M. iowae putative catalase gene, katE. H2O2-mediated virulence by M. iowae serovar K and catalase-producing M. gallisepticum transformants were also analyzed using a Caenorhabditis elegans toxicity assay, which has never previously been used in conjunction with mycoplasmas. We found that M. iowae katE encodes an active catalase that, when expressed in M. gallisepticum, reduces both the amount of H2O2 produced and the amount of damage to C. elegans in the presence of glycerol. Therefore, the correlation between the presence of glycerol catabolism genes and the use of H2O2 as a virulence factor by mycoplasmas might not be absolute.

Draft Genome Sequence of Hymenobacter sp. Strain IS2118, Isolated from a Freshwater Lake in Schirmacher Oasis, Antarctica, Reveals Diverse Genes for Adaptation to Cold Ecosystems

ABSTRACT Hymenobacter sp. IS2118, isolated from a freshwater lake in Schirmacher Oasis, Antarctica, produces extracellular polymeric substance (EPS) and manifests tolerance to cold, UV radiation (UVR), and oxidative stress. We report the 5.26-Mb draft genome of strain IS2118, which will help us to understand its adaptation and survival mechanisms in Antarctic extreme ecosystems.

CUAB: Supervised Learning of Disorders and their Attributes using Relations

We implemented an end-to-end system for disorder identification and slot filling. For identifying spans for both disorders and their attributes, we used a linear chain conditional random field (CRF) approach coupled with cTAKES for pre-processing. For combining disjoint disorder spans, finding relations between attributes and disorders, and attribute normalization, we used l2-regularized l2-loss linear support vector machine (SVM) classification. Disorder CUIs were identified using a back-off approach to YTEX lookup (CUAB1) or NLM UTS API (CUAB2) if the target text was not found in the training data. Our best system utilized UMLS semantic type features for disorder/attribute span identification and the NLM UTS API for normalization. It was ranked 12th in Task 1 (disorder identification) and 6th in Task 2b (disorder identification and slot filling) with a weighted F Measure of 0.711.

CUILESS2016: a clinical corpus applying compositional normalization of text mentions

BackgroundTraditionally text mention normalization corpora have normalized concepts to single ontology identifiers (“pre-coordinated concepts”). Less frequently, normalization corpora have used concepts with multiple identifiers (“post-coordinated concepts”) but the additional identifiers have been restricted to a defined set of relationships to the core concept. This approach limits the ability of the normalization process to express semantic meaning. We generated a freely available corpus using post-coordinated concepts without a defined set of relationships that we term “compositional concepts” to evaluate their use in clinical text.MethodsWe annotated 5397 disorder mentions from the ShARe corpus to SNOMED CT that were previously normalized as “CUI-less” in the “SemEval-2015 Task 14” shared task because they lacked a pre-coordinated mapping. Unlike the previous normalization method, we do not restrict concept mappings to a particular set of the Unified Medical Language System (UMLS) semantic types and allow normalization to occur to multiple UMLS Concept Unique Identifiers (CUIs). We computed annotator agreement and assessed semantic coverage with this method.ResultsWe generated the largest clinical text normalization corpus to date with mappings to multiple identifiers and made it freely available. All but 8 of the 5397 disorder mentions were normalized using this methodology. Annotator agreement ranged from 52.4% using the strictest metric (exact matching) to 78.2% using a hierarchical agreement that measures the overlap of shared ancestral nodes.ConclusionOur results provide evidence that compositional concepts can increase semantic coverage in clinical text. To our knowledge we provide the first freely available corpus of compositional concept annotation in clinical text.