Diagnosis of adverse events after hysterectomy with postoperative self-care web applications: A pilot study.

Increased pressures from multiple sources are leading to earlier patient discharge following surgery. Our objective was to test the feasibility of self-care web applications to inform women if, when, and where to seek help for symptoms after hysterectomy. We asked 31 women recovering at home after hysterectomy at two centers to sign into a website on a schedule. For each session, the website informed them about normal postoperative symptoms and prompted them to complete an interactive symptom questionnaire that provided detailed information on flagged responses. We interviewed eight women who experienced an adverse event. Six of these women had used the web application regularly, each indicating they used the information to guide them in seeking care for their complications. These data support that self-care applications may empower patients to manage their own care and present to appropriate health care providers and venues when they experience abnormal symptoms.

Rapid identification of high-confidence taxonomic assignments for metagenomic data.

Determining the taxonomic lineage of DNA sequences is an important step in metagenomic analysis. Short DNA fragments from next-generation sequencing projects and microbes that lack close relatives in reference sequenced genome databases pose significant problems to taxonomic attribution methods. Our new classification algorithm, RITA (Rapid Identification of Taxonomic Assignments), uses the agreement between composition and homology to accurately classify sequences as short as 50 nt in length by assigning them to different classification groups with varying degrees of confidence. RITA is much faster than the hybrid PhymmBL approach when comparable homology search algorithms are used, and achieves slightly better accuracy than PhymmBL on an artificial metagenome. RITA can also incorporate prior knowledge about taxonomic distributions to increase the accuracy of assignments in data sets with varying degrees of taxonomic novelty, and classified sequences with higher precision than the current best rank-flexible classifier. The accuracy on short reads can be increased by exploiting paired-end information, if available, which we demonstrate on a recently published bovine rumen data set. Finally, we develop a variant of RITA that incorporates accelerated homology search techniques, and generate predictions on a set of human gut metagenomes that were previously assigned to different 'enterotypes'. RITA is freely available in Web server and standalone versions.

Classifying short genomic fragments from novel lineages using composition and homology.

BACKGROUND: The assignment of taxonomic attributions to DNA fragments recovered directly from the environment is a vital step in metagenomic data analysis. Assignments can be made using rank-specific classifiers, which assign reads to taxonomic labels from a predetermined level such as named species or strain, or rank-flexible classifiers, which choose an appropriate taxonomic rank for each sequence in a data set. The choice of rank typically depends on the optimal model for a given sequence and on the breadth of taxonomic groups seen in a set of close-to-optimal models. Homology-based (e.g., LCA) and composition-based (e.g., PhyloPythia, TACOA) rank-flexible classifiers have been proposed, but there is at present no hybrid approach that utilizes both homology and composition. RESULTS: We first develop a hybrid, rank-specific classifier based on BLAST and Naïve Bayes (NB) that has comparable accuracy and a faster running time than the current best approach, PhymmBL. By substituting LCA for BLAST or allowing the inclusion of suboptimal NB models, we obtain a rank-flexible classifier. This hybrid classifier outperforms established rank-flexible approaches on simulated metagenomic fragments of length 200 bp to 1000 bp and is able to assign taxonomic attributions to a subset of sequences with few misclassifications. We then demonstrate the performance of different classifiers on an enhanced biological phosphorous removal metagenome, illustrating the advantages of rank-flexible classifiers when representative genomes are absent from the set of reference genomes. Application to a glacier ice metagenome demonstrates that similar taxonomic profiles are obtained across a set of classifiers which are increasingly conservative in their classification. CONCLUSIONS: Our NB-based classification scheme is faster than the current best composition-based algorithm, Phymm, while providing equally accurate predictions. The rank-flexible variant of NB, which we term ε-NB, is complementary to LCA and can be combined with it to yield conservative prediction sets of very high confidence. The simple parameterization of LCA and ε-NB allows for tuning of the balance between more predictions and increased precision, allowing the user to account for the sensitivity of downstream analyses to misclassified or unclassified sequences.

Efficient learning of microbial genotype-phenotype association rules.

MOTIVATION: Finding biologically causative genotype-phenotype associations from whole-genome data is difficult due to the large gene feature space to mine, the potential for interactions among genes and phylogenetic correlations between genomes. Associations within phylogenetically distinct organisms with unusual molecular mechanisms underlying their phenotype may be particularly difficult to assess. RESULTS: We have developed a new genotype-phenotype association approach that uses Classification based on Predictive Association Rules (CPAR), and compare it with NETCAR, a recently published association algorithm. Our implementation of CPAR gave on average slightly higher classification accuracy, with approximately 100 time faster running times. Given the influence of phylogenetic correlations in the extraction of genotype-phenotype association rules, we furthermore propose a novel measure for downweighting the dependence among samples by modeling shared ancestry using conditional mutual information, and demonstrate its complementary nature to traditional mining approaches. AVAILABILITY: Software implemented for this study is available under the Creative Commons Attribution 3.0 license from the author at http://kiwi.cs.dal.ca/Software/PICA